{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2520b517",
   "metadata": {},
   "source": [
    "Prerequisites\n",
    "Make sure you have the necessary Python libraries installed:\n",
    "pip install numpy pandas scikit-learn scipy matplotlib statsmodels\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2389f5b0",
   "metadata": {},
   "source": [
    "Numerical "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b63c1cbb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:2385: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:2443: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q * linpred)))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parametric Bootstrap Results:\n",
      "Intercept: mean = -11.6793, 95% CI = [-18.5765, -4.8140]\n",
      "Slope:     mean = 5.6853, 95% CI = [1.8714, 9.5282]\n",
      "\n",
      "Delta Method Results:\n",
      "Intercept: -11.6839 ± 6.8804 (95% CI: [-18.5643, -4.8035])\n",
      "Slope:     5.6870 ± 3.8302 (95% CI: [1.8568, 9.5171])\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import io\n",
    "from scipy.ndimage import gaussian_filter1d\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import statsmodels.api as sm\n",
    "\n",
    "\n",
    "\n",
    "#  Load Data \n",
    "data_path = \"../data/\"\n",
    "suv = io.loadmat(data_path + \"suv_percentilesSLOthenUWM.mat\")['lung_SUVperc_COMBINED'][0:58, :, :]\n",
    "flags = io.loadmat(data_path + \"flags_combined.mat\")['flags'][0:58, 3]\n",
    "\n",
    "# Preprocessing \n",
    "p = 94\n",
    "X = np.nanmax(suv[:, :, p], axis=1).reshape(-1, 1)\n",
    "valid = ~np.isnan(X).flatten()\n",
    "X = X[valid]\n",
    "flags = flags[valid]\n",
    "\n",
    "\n",
    "x_vals = np.linspace(X.min(), X.max(), 500)\n",
    "\n",
    "#Parametric Bootstrap Method \n",
    "logr = LogisticRegression(penalty=None, solver='lbfgs')\n",
    "logr.fit(X, flags)\n",
    "\n",
    "X_design = np.hstack([np.ones((X.shape[0], 1)), X])\n",
    "pred_probs = logr.predict_proba(X)[:, 1]\n",
    "V = np.diagflat(pred_probs * (1 - pred_probs))\n",
    "cov_matrix = np.linalg.inv(X_design.T @ V @ X_design)\n",
    "params = np.insert(logr.coef_[0], 0, logr.intercept_[0])\n",
    "\n",
    "n_bootstraps = 200000\n",
    "bootstrap_params = np.random.multivariate_normal(params, cov_matrix, size=n_bootstraps)\n",
    "predicted_probs_boot = 1 / (1 + np.exp(-(bootstrap_params[:, 0:1] + bootstrap_params[:, 1:2] * x_vals)))\n",
    "\n",
    "# Smooth and filter bootstrap curves\n",
    "sigma_curve = 0.5\n",
    "smoothed = np.array([gaussian_filter1d(c, sigma=sigma_curve) for c in predicted_probs_boot])\n",
    "\n",
    "def is_reasonable(c): return (0.05 < c.mean() < 0.95) and (c.max() - c.min() > 0.2)\n",
    "filtered = np.array([c for c in smoothed if is_reasonable(c)])\n",
    "mean_probs_boot = filtered.mean(axis=0)\n",
    "\n",
    "lower_boot = gaussian_filter1d(np.percentile(filtered, 5, axis=0), sigma=0.2)\n",
    "upper_boot = gaussian_filter1d(np.percentile(filtered, 95, axis=0), sigma=0.2)\n",
    "\n",
    "# Delta Method\n",
    "X_with_intercept = sm.add_constant(X)\n",
    "model = sm.Logit(flags, X_with_intercept)\n",
    "result = model.fit(disp=0)\n",
    "\n",
    "x_vals_stats = np.linspace(X.min(), X.max(), 500)\n",
    "x_vals_intercept = sm.add_constant(x_vals_stats)\n",
    "predicted_probs_delta = result.predict(x_vals_intercept)\n",
    "\n",
    "# Confidence Interval (delta method)\n",
    "cov_params = result.cov_params()\n",
    "linear_pred = x_vals_intercept @ result.params\n",
    "\n",
    "def logistic(z): return 1 / (1 + np.exp(-z))\n",
    "\n",
    "g = x_vals_intercept * (logistic(linear_pred) * (1 - logistic(linear_pred)))[:, None]\n",
    "se = np.sqrt(np.sum(g @ cov_params * g, axis=1))\n",
    "z = 1.96\n",
    "lower_delta = np.clip(predicted_probs_delta - z * se, 0, 1)\n",
    "upper_delta = np.clip(predicted_probs_delta + z * se, 0, 1)\n",
    "\n",
    "#  Plotting Together \n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.title(\"Comparison of Parametric Bootstrap vs Delta Method\")\n",
    "plt.xlabel(r\"$x = \\max_{visit} SUV(visit, p)$\")\n",
    "plt.ylabel(r\"$P(\\mathrm{AE} \\mid X = x)$\")\n",
    "\n",
    "# Data points\n",
    "plt.scatter(X[flags == 0], flags[flags == 0], label=\"NC\", color='blue', alpha=0.5)\n",
    "plt.scatter(X[flags == 1], flags[flags == 1], label=\"AE\", color='red', alpha=0.5)\n",
    "\n",
    "# Delta method curve and CI\n",
    "plt.plot(x_vals_stats, predicted_probs_delta, color='green', label=\"MLE (Delta Method)\")\n",
    "plt.fill_between(x_vals_stats, lower_delta, upper_delta, color='green', alpha=0.2, label=\"95% CI (Delta)\")\n",
    "\n",
    "# Bootstrap curve and CI\n",
    "plt.plot(x_vals, mean_probs_boot, color='orange', label=\"MLE (Parametric Bootstrap)\", linewidth=2)\n",
    "plt.fill_between(x_vals, lower_boot, upper_boot, color='orange', alpha=0.2, label=\"95% CI (Bootstrap)\")\n",
    "\n",
    "plt.grid(True)\n",
    "plt.legend(loc='center right')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "# --- Parametric Bootstrap estimates (mean and 95% CI for intercept and slope) ---\n",
    "boot_intercepts = bootstrap_params[:, 0]\n",
    "boot_slopes = bootstrap_params[:, 1]\n",
    "\n",
    "print(\"Parametric Bootstrap Results:\")\n",
    "print(f\"Intercept: mean = {np.mean(boot_intercepts):.4f}, 95% CI = [{np.percentile(boot_intercepts, 2.5):.4f}, {np.percentile(boot_intercepts, 97.5):.4f}]\")\n",
    "print(f\"Slope:     mean = {np.mean(boot_slopes):.4f}, 95% CI = [{np.percentile(boot_slopes, 2.5):.4f}, {np.percentile(boot_slopes, 97.5):.4f}]\")\n",
    "\n",
    "# --- Delta Method estimates (coef and 95% CI from statsmodels) ---\n",
    "params_delta = result.params\n",
    "se_delta = np.sqrt(np.diag(cov_params))\n",
    "z = 1.96\n",
    "\n",
    "print(\"\\nDelta Method Results:\")\n",
    "print(f\"Intercept: {params_delta[0]:.4f} ± {z*se_delta[0]:.4f} (95% CI: [{params_delta[0] - z*se_delta[0]:.4f}, {params_delta[0] + z*se_delta[0]:.4f}])\")\n",
    "print(f\"Slope:     {params_delta[1]:.4f} ± {z*se_delta[1]:.4f} (95% CI: [{params_delta[1] - z*se_delta[1]:.4f}, {params_delta[1] + z*se_delta[1]:.4f}])\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab8b7a67",
   "metadata": {},
   "source": [
    "Aalytical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "283a7c28",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Fitted Parameters:\n",
      "Intercept (w0): -11.6832 [-18.5622, -4.8042]\n",
      "Coefficient (w1): 5.6871 [1.8573, 9.5169]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Selection Criteria:\n",
      "Log-Likelihood: -7.3942\n",
      "AIC: 18.7884\n",
      "BIC: 22.9093\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 0.9655\n",
      "\n",
      "Confusion Matrix:\n",
      "[[52  1]\n",
      " [ 1  4]]\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.9811    0.9811    0.9811        53\n",
      "           1     0.8000    0.8000    0.8000         5\n",
      "\n",
      "    accuracy                         0.9655        58\n",
      "   macro avg     0.8906    0.8906    0.8906        58\n",
      "weighted avg     0.9655    0.9655    0.9655        58\n",
      "\n",
      "ROC AUC Score: 0.9811\n"
     ]
    },
    {
     "data": {
      "image/png": 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goMCuXd1QqpSFzrMqijI2FkREREREGThz5gHGjj2I8+cfAQA++6wqPvqotDS/dOlickXLlwrvjXSJiIiIiLLg3r2X6N59J5o2XS81FQDw++83ZEyV//GIBRERERERgNjYZMydexJLlpxDcrJGqlerVhJ+fl7w9CwvY7r8j40FERERERVpGo0W69eH4JtvjuLp03ipXrKkGb79thUGDqwLQ0Oe6PMhbCyIiIiIqEg7ePAOvvrqD2lapVJizJiG+PrrZihWzETGZAULWy8iIiIiKtLat6+A5s3LAHhzgfb168Mxf74Hmwo98YgFERERERUZMTEJ2LEjDEOHuks1hUKB5cvb4+XLJDRrVkbGdAUbGwsiIiIiKvRSUjRYufICZs06jhcvklCunI3Oxdg1atjLmK5w4KlQRERERFRoCSHw++83UL36SowdexAvXiQBAKZNOypzssKHRyyIiIiIqFAKDY3CuHGBOHIkXKfet28tzJ7dWqZUhRcbCyIiIiIqVCIj4/C//x3F+vUhEOJtvVmz0liyxAv16jnKF64QY2NBRERERIXG/fsvUb26P16/TpFq5crZYOFCD3TpUhkKhULGdIUbr7EgIiIiokKjTBlrNGtWGgBgZWWMhQs9EBbmg08/rcKmIpfxiAURERERFViXLz9BjRp2Ok3D4sWecHW1xowZLVGypLmM6YoWHrEgIiIiogLn/v2X+OKLXahVaxX27v1HZ16VKiWxYkUHNhV5jI0FERERERUYcXHJ+Prrw6hUaTm2bbsKAJgwIQjJyakyJyOeCkVERERE+Z5Go8WGDaH45psjePIkXqqXKGGGceMaQ6nk9+VyY2NBRERERPnakSPh8PU9iL//fiLVVColRo9uiK+/bgZraxMZ01EaNhZERERElC+lpGjw+ec78PvvN3TqXbtWwfz5bVG+fHGZklFG2FgQERERUb6kUilhbKyUpuvVc4CfnxeaNy8jYyp6FzYWRERERJQvqNUaKJUGMDB4e+vYefPa4uLFSEyf3gJffllTZx7lL7zKhYiIiIhkJYTAvn03UL26PzZvvqwzr1w5G9y8OQJ9+tRiU5HPsbEgIiIiItlcvvwEHh4/o1Onbbh5MwZTphxGfHyKzjK841PBwFOhiIiIiCjPPXnyGv/731GsWxcCrVZIdVdXG8TEJMLcXCVjOsoKNhZERERElGeSklKxdOk5zJlzEnFxb49MuLpaY8ECD3TtWgUKBU95KojYWBARERFRntix4xomTAjC/fuvpJqlpQrffNMco0Y1hIkJd00LMv70iIiIiChPBATclpoKAwMFBg+ui1mzWsHOzlzmZJQTeCUMEREREeWJ2bNbw9zcCB4e5RAaOgSrVn3MpqIQ4RELIiIiIspRr1+nYP78U3B1tcGAAXWkuqOjJS5fHgZXV2teR1EIsbEgIiIiohyh0WixadPfmDr1CCIjX6NkSTN07VoFxYqZSMuUK2cjY0LKTTwVioiIiIiy7dixe3B3X4MBA35HZORrAMDLl0k4eTJC5mSUV3jEgoiIiIiy7NatGEyceAh79/6jU+/SpTLmz2+LihVtZUpGeY2NBRERERHp7cWLRHz77QksX34earVWqtepUwp+fl5o2bKsfOFIFmwsiIiIiEhvc+acxJIl56TpUqUsMGdOa/TpUwtKJc+2L4r4UyciIiIivU2e/BGsrU1gYmKI//2vOW7dGon+/euwqSjCeMSCiIiIiN7r6tWnuHkzBp9+WkWq2dqaYcuWT1G9uh1cXIrJmI7yCzYWRERERJShp0/jMW3aUaxZcwkWFio0a1YaJUu+faBd+/YVZUxH+Q2PVRERERGRjqSkVMyffwoVKvyA1asvQqsViI1N1rmmgui/eMSCiIiIiAAAQgjs2BGGSZMO4d69l1Ld0lKFr79uhjFjGskXjvI9NhZEREREhPPnH8HX9yBOn34g1QwMFBg0qA5mzWoFe3sLGdNRQcDGgoiIiKiI2779Knr02KVTa9u2HPz8PFGjhr1MqaigYWNBREREVMS1b18RdnbmePo0HpUq2WLxYk94e1eEQqGQOxoVIGwsiIiIiIoQrVbgypUnqFWrlFSzsjLG4sWeePEiEUOHusPISCljQiqo2FgQERERFRHHj9/D2LEH8c8/0bh1ayScnKykeV9+WVPGZFQY8HazRERERIXc7dvP8emn29Gy5U8ICYlCYmIqvv76iNyxqJDhEQsiIiKiQurlyyR8990J/PDDX1CrtVK9Vi179OtXS8ZkVBixsSAiIiIqZFJTtVi9OhjTpx9DTEyiVC9VygKzZ7dG3761oFTyxBXKWWwsiIiIiAqRmzdj8Mkn23D9erRUMzExxLhxjTFpUlNYWhrLmI4KMzYWRERERIWIi4sVXr9OkaZ79qyBuXPboHTpYjKmoqKAx8CIiIiICrCkpFSdaVNTI8yb1xaNGzvj7NmB2Lz5UzYVlCfYWBAREREVQMnJqViw4DRKl16CO3ee68z74ovqOH16ABo1cpYpHRVFbCyIiIiIChAhBHbuDEOVKiswadIhPHuWgIkTD+kso1Ao+NRsynO8xoKIiIiogAgOfgxf34M4eTJCqikUQPHiJkhN1cLQkN8Zk3xkH30rV66Eq6srTExMUK9ePZw8efK9y2/evBm1atWCmZkZHBwc0L9/f8TExORRWiIiIqK89/BhLPr23Yv69dfoNBVt2rgiJGQI1qzpxKaCZCfrCNy+fTvGjBmDqVOnIiQkBM2aNUP79u0RERGR4fKnTp1Cnz59MHDgQFy7dg07duzAhQsXMGjQoDxOTkRERJT7kpJSMWPGMbi5LcOmTX9LdTc3W/z+ew8EBfVGrVqlZExI9JasjYWfnx8GDhyIQYMGoUqVKli6dClcXFzg7++f4fLnzp1D2bJlMWrUKLi6uuKjjz7CkCFDEBwcnMfJiYiIiHKfgYECW7ZcQWLimzs/2diYYOlSL1y5MgwdO1bidRSUr8jWWKSkpODixYvw9PTUqXt6euLMmTMZrtOkSRM8fPgQAQEBEELgyZMn2LlzJzp06JAXkYmIiIjylEqlxMKFHjA0NMCoUQ1w+/YojB7dCCqVUu5oROnIdvF2dHQ0NBoN7O3tder29vaIiorKcJ0mTZpg8+bN6N69O5KSkpCamopOnTph2bJl79xOcnIykpOTpenY2FgAgFqthlqtzoF3or+07WZ2+4pbO6E8NxNIeZ35jSREQgFACCBVpvdJH6bvWKDCieOA0nAsFG13777A1KlHMXXqR6hUyQbAm7HQvn05hIUNRdmy1lKNiob88Jmgz7ZlvyvUfw/hCSHeeVgvLCwMo0aNwrRp0+Dl5YXIyEhMmDABQ4cOxbp16zJcZ+7cuZg5c2a6emBgIMzMzLL/BrIhKCgoU8u1vj8RluqHWdrG6xQFjgQEZGldyjuZHQtUuHEcUBqOhaIlPl6DHTue4I8/niE1VeDu3UeYPr08AN2xEBYmV0KSm5yfCQkJCZleViGEELmY5Z1SUlJgZmaGHTt2oEuXLlJ99OjRCA0NxfHjx9Ot07t3byQlJWHHjh1S7dSpU2jWrBkeP34MBweHdOtkdMTCxcUF0dHRsLKyyuF3lTlqtRpBQUHw8PCAkZHRB5c3XOcKRfwjCIUBYJb+Pb6TygKaRjMgKnbNRlrKTfqOBSqcOA4oDcdC0ZKaqsX69aGYOfMEnj17u/NmZ2eGM2f64urVsxwLRVx++EyIjY1FiRIl8OrVqw/uO8t2xEKlUqFevXoICgrSaSyCgoLQuXPnDNdJSEiAoaFuZKXyzTmG7+qPjI2NYWxsnK5uZGQk+y9qpjP8/wEchbkDMES/IxeyH5KiTMkP45Hkx3FAaTgWCr+DB29j3LhAXLv2TKoZGyvh69sYU6Z8BBMTA1y9yrFAb8g5DvTZrqz7nb6+vujduzfc3d3RuHFj/Pjjj4iIiMDQoUMBAFOmTMGjR4+wadMmAEDHjh0xePBg+Pv7S6dCjRkzBg0aNICjo6Ocb4WIiIjog8LCnmH8+EDs339bp969ezXMm9eW11FQgSZrY9G9e3fExMRg1qxZiIyMRPXq1REQEIAyZcoAACIjI3WeadGvXz/ExcVh+fLlGDduHKytrdG6dWvMnz9frrdARERElClCCPTpswcXL0ZKtYYNnbBkiRcaN3aRMRlRzpD9TBkfHx/4+PhkOG/jxo3paiNHjsTIkSNzORURERFRzlIoFJg/vy3atv0ZLi5WmDevLXr0qA4DAz6LggoH2RsLIiIiosJGCIE9e/5B2bLWqFv37Y1X2rQph61bu6JTp0owM+O1E1S4yPrkbSIiIqLC5tKlSLRq9RO6dv0Vo0btT3eDmR49qrOpoEKJjQURERFRDnj8OA79+u2Fu/uPOH78PgDg9OkHOHIkXOZkRHmDp0IRERERZUNCghqLFp3B/PmnkZDw9m5OFSoUx6JFHmjd2lXGdER5h40FERERURZotQJbtlzBlCmH8fBhrFS3tjbB9Okt4ONTHyqVUsaERHmLjQURERFRFvj4/InVqy9K00qlAj4+9TF9egvY2prJmIxIHrzGgoiIiCgL+vWrLf29Q4eKuHrVBz/80J5NBRVZPGJBRERE9AGxscmIinoNNzdbqdaokTO++aYZmjcvAw+P8jKmI8of2FgQERERvUNqqhbr1l3C//53FM7OVrhwYTCUyrcnfHz7bWsZ0xHlLzwVioiIiCgDQUF3UKfOagwd+ieePUtASEgUNm36W+5YRPkWj1gQERER/cv1688wfnwQAgJu6dS7dauGli3LyhOKqABgY0FEREQEICYmATNmHIO/fzA0mrdPy65f3xFLlnihadPSMqYjyv/YWBAREVGRt2fPdQwY8DtevkySas7OVpg7tw169qwBAwOFjOmICgY2FkRERFTkubra4NWrN02FmZkRJk9uinHjmsDMzEjmZEQFBxsLIiIiKnISE9UwNX3bNNSuXQqDBtWFWq3F7Nmt4ehoKWM6ooKJjQUREREVGZGRcfjmmyP4669HCAkZAiMjpTRv1aqPecoTUTbwdrNERERU6CUmqjF79glUrLgM69eH4tq1Z1i9+qLOMmwqiLKHRyyIiIio0BJCYOvWq5g8+RAePIiV6sWKGcPIiN+vEuUkNhZERERUKJ058wC+vgfx11+PpJpSqcDQoe6YPr0FSpY0lzEdUeHDxoKIiIgKlYcPYzF+fCC2b7+mU2/fvgIWLfJE1aolZUpGVLixsSAiIqJCJSFBjV27rkvTVauWhJ+fJ7y8KsiYiqjw48mFREREVKi4udlixIj6KFHCDP7+HfD330PZVBDlATYWREREVGAdPnwXnTptRVJSqk59xoyWuH17JIYOdYehIXd3iPICf9OIiIiowLlxIxodO25F27Y/Y9++m/j++3M684sVM0GxYiYypSMqmthYEBERUYHx/HkiRo/ej+rV/fHHHzelemDgXQghZExGRLx4m4iIiPK9lBQNVq68gFmzjuPFiySp7uRkiblz26BXr5pQKPiAOyI5sbEgIiKifEsIgX37bmL8+EDcuvVcqpuZGWHixCYYP74JzM1VMiYkojRsLIiIiCjfevIkHt2779S5OLtv31qYPbs1nJysZExGRP/FayyIiIgo3ypVygLjxjUGADRrVhrBwYOxceMnbCqI8iEesSAiIqJ8ITFRjRUrLmDIkHqwtDSW6pMnf4R69RzwySeVeR0FUT7GxoKIiIhkJYTAtm1XMXnyYUREvMLLl0n47rvW0nwLCxW6dKkiY0IiygyeCkVERESyOXv2AZo0WY+ePXcjIuIVAOD77//Cq1dJH1iTiPIbNhZERESU5+7ff4kvvtiFJk3W49y5h1Ldy6s8zp0byIfbERVAPBWKiIiI8kxcXDLmzTsFP79zOnd6qlq1JBYv9kS7dhVkTEdE2cHGgoiIiPKERqNFvXo/6jyPokQJM8yc2RJffVUPhoY8kYKoIONvMBEREeUJpdIA/fvXBgAYGRlg/PjGuHVrJHx86rOpICoEeMSCiIiIcsWtWzEoUcIMNjamUm3s2MaIiHiF8eOboHz54jKmI6Kcxq8HiIiIKEe9eJGIsWMPoGrVlZg167jOPBMTQ/j7f8ymgqgQYmNBREREOUKt1mDZsr9QocIyLF36F1JTtVi+/AJu3oyROxoR5QGeCkVERETZIoTAn3/ewvjxgbhx420TYWpqiAkTmsDJyVLGdESUV9hYEBERUZZdufIEvr6BOHTork69d++amDOnDZydrWRKRkR5jY0FERERZcnkyYewcOEZaLVCqjVt6oIlS7xQv76TjMmISA5sLIiIiChLnJwspaaibFlrLFjQFp99VhUKhULmZEQkBzYWRERE9EFCCCQlpcLU1EiqDR3qjl9+uYJPP62M0aMbwcSEuxVERVmWPgFSU1Nx7Ngx3LlzBz179oSlpSUeP34MKysrWFhY5HRGIiIiktH5848wduxBVKtWEj/+2FGqGxkpce7cQB6hICIAWWgs7t+/j3bt2iEiIgLJycnw8PCApaUlFixYgKSkJKxatSo3chIREVEee/DgFSZPPowtW64AAM6de4gRIxqgZk17aRk2FUSURu/nWIwePRru7u548eIFTE3fPkmzS5cuOHz4cI6GIyIiorz3+nUK/ve/I3BzWy41FQDg5maL+PgUGZMRUX6m9xGLU6dO4fTp01CpVDr1MmXK4NGjRzkWjIiIiPKWRqPFpk1/Y+rUI4iMfC3Vixc3xcyZLTFkSD0YGSnlC0hE+ZrejYVWq4VGo0lXf/jwISwt+QAcIiKigujUqQiMHLkfoaFRUs3IyAAjRzbAN980h42N6XvWJiLKwqlQHh4eWLp0qTStUCjw+vVrTJ8+Hd7e3jmZjYiIiPLIzZsxOk1Fly6Vce2aDxYv9mJTQUSZovcRiyVLlqBVq1aoWrUqkpKS0LNnT9y6dQslSpTA1q1bcyMjERER5bK+fWth2bLzUCgAPz8vtGxZVu5IRFTA6N1YODo6IjQ0FNu2bcPFixeh1WoxcOBA9OrVS+dibiIiIsp/1GoNVq++iH/+icby5W/PNFAqDRAQ0BN2duZQKvU+oYGISP/G4sSJE2jSpAn69++P/v37S/XU1FScOHECzZs3z9GARERElH1CCOzffxvjxgXin3+iAQBfflkTjRo5S8s4OPBaSSLKOr2/kmjVqhWeP3+erv7q1Su0atUqR0IRERFRzrl69Sm8vH5Bhw5bpKYCAA4fvitjKiIqbPQ+YiGEyPBhODExMTA3N8+RUERERJR9T5/GY9q0o1iz5hK0WiHVmzRxgZ+fJxo2dH7P2kRE+sl0Y/Hpp58CeHMXqH79+sHY2Fiap9FocPnyZTRp0iTnExIREZFekpJS8cMPf2H27JOIjU2W6mXKFMOCBR74/POqfGI2EeW4TDcWxYoVA/DmiIWlpaXOhdoqlQqNGjXC4MGDcz4hERER6eXAgduYNOmQNG1pqcLXXzfDmDGNYGKi98kKRESZkulPlw0bNgAAypYti/Hjx/O0JyIionyqc+dKaNrUBWfPPsTAgXXw7betYG9vIXcsIirk9P7aYvr06bmRg4iIiLLg4cNY7NwZhjFjGkk1hUKBVas+hlYrULOmvYzpiKgoydLx0J07d+LXX39FREQEUlJSdOZdunQpR4IRERHRu71+nYIFC05j0aIzSExMRc2a9mjd2lWaX726nYzpiKgo0vt2sz/88AP69+8POzs7hISEoEGDBrC1tcXdu3fRvn373MhIRERE/0+rFdi4MRRubsvw7bcnkJiYCgD47rsTMicjoqJO78Zi5cqV+PHHH7F8+XKoVCpMnDgRQUFBGDVqFF69epUbGYmIiAjAiRP3Ub/+GvTv/xsiI18DAAwNDTBmTEPs2tVN5nREVNTp3VhERERIt5U1NTVFXFwcAKB3797YunVrzqYjIiIi3LnzHF27/ooWLTbi0qVIqd6pUyVcu+aDJUvawcbG9D2vQESU+/RuLEqVKoWYmBgAQJkyZXDu3DkAQHh4OIQQ71s1QytXroSrqytMTExQr149nDx58r3LJycnY+rUqShTpgyMjY1Rvnx5rF+/Xu/tEhERFQQ3bkSjatWV2L37ulSrVcsehw/3wW+/9YCbm62M6YiI3tL74u3WrVtj3759qFu3LgYOHIixY8di586dCA4Olh6il1nbt2/HmDFjsHLlSjRt2hSrV69G+/btERYWhtKlS2e4Trdu3fDkyROsW7cOFSpUwNOnT5Gamqrv2yAiIioQ3Nxs0aJFGQQF3YW9vTlmz26Nfv1qQ6nU+7tBIqJcpXdj8eOPP0Kr1QIAhg4diuLFi+PUqVPo2LEjhg4dqtdr+fn5YeDAgRg0aBAAYOnSpTh48CD8/f0xd+7cdMsfOHAAx48fx927d1G8eHEAb56rQUREVFj880882rd/ewaAQqGAn58Xtm69gsmTP4KlpbGM6YiI3k3vrzsMDAxgaPi2H+nWrRt++OEHjBo1Cs+ePcv066SkpODixYvw9PTUqXt6euLMmTMZrvP777/D3d0dCxYsgJOTE9zc3DB+/HgkJibq+zaIiIjylWvXnqJjx22YPPkW/vjjls686tXtMHt2GzYVRJSvZek5Fv8VFRWF2bNnY+3atZneyY+OjoZGo4G9ve6De+zt7REVFZXhOnfv3sWpU6dgYmKCPXv2IDo6Gj4+Pnj+/Pk7r7NITk5GcnKyNB0bGwsAUKvVUKvVmcqa09K2m9ntGwpAAUAIIFWmzJQ79B0LVDhxHBRtz57FY9ask1i7NgQazZsjFZMmHYaXV3moVEqZ05Fc+LlAQP4YB/psO9ONxcuXLzF8+HAEBgbCyMgIkydPxogRIzBjxgwsWrQI1apVy9JF1AqFQmdaCJGulkar1UKhUGDz5s0oVqwYgDenU3322WdYsWIFTE3T3xFj7ty5mDlzZrp6YGAgzMzM9M6bk4KCgjK1nGdSEkwBJCUlITAgIHdDkSwyOxaocOM4KFrUai3++CMaO3ZEISFBK9VLlDBCp05WCAo68M7/D6no4OcCAfKOg4SEhEwvm+nG4uuvv8aJEyfQt29fHDhwAGPHjsWBAweQlJSE/fv3o0WLFnqFLFGiBJRKZbqjE0+fPk13FCONg4MDnJycpKYCAKpUqQIhBB4+fIiKFSumW2fKlCnw9fWVpmNjY+Hi4gJPT09YWVnplTmnqNVqBAUFwcPDA0ZGRh9c3nCdCRAPmJiYwNvbOw8SUl7RdyxQ4cRxULQIIbBnzw18/fUR3L37UqpbWKgwfnxDVKnyCh9/7MWxUMTxc4GA/DEO0s72yYxMNxZ//vknNmzYgLZt28LHxwcVKlSAm5sbli5dmpWMUKlUqFevHoKCgtClSxepHhQUhM6dO2e4TtOmTbFjxw68fv0aFhYWAICbN2/CwMAAzs7OGa5jbGwMY+P056QaGRnJ/oua6Qz//4WVQgHZM1PuyA/jkeTHcVD4JSaq0a7dFpw4cV+qKRTAgAF18N13rWFra4yAgACOBZJwLBAg7zjQZ7uZvnj78ePHqFq1KgCgXLlyMDExke7mlFW+vr5Yu3Yt1q9fj+vXr2Ps2LGIiIiQ7i41ZcoU9OnTR1q+Z8+esLW1Rf/+/REWFoYTJ05gwoQJGDBgQIanQREREeUnpqZGsLV9+/9V69auCAkZgrVrO6FUKQsZkxERZV+mj1hotVqdjkWpVMLc3DxbG+/evTtiYmIwa9YsREZGonr16ggICECZMmUAAJGRkYiIiJCWt7CwQFBQEEaOHAl3d3fY2tqiW7du+O6777KVg4iIKDckJKhhYmIIA4O310osWOCBW7eeY/bs1ujY0Y3XURBRoZHpxkIIgX79+kmnFSUlJWHo0KHpmovdu3frFcDHxwc+Pj4Zztu4cWO6WuXKlXkhExER5WtarcAvv1zG118fxsKFHvjiixrSvAoViuPy5aFsKIio0Ml0Y9G3b1+d6S+//DLHwxARERV0J0/eh69vIIKDHwMAJk06hE8+qQxT07dH/dlUEFFhlOnGYsOGDbmZg4iIqEC7e/cFJk06hJ07w3TqtWuXwqtXyTqNBRFRYZQjD8gjIiIqql69SsLs2Sfx/fd/ISVFI9Vr1rTH4sWeaNu2nIzpiIjyDhsLIiKiLPrxx4v45psjePbs7QOk7OzM8d13rTBgQB0olZm++SIRUYHHxoKIiCiLTp2KkJoKY2MlfH0bY/Lkj2Bllf75SUREhR2/SiEiIsqiOXPawMzMCN27V8M//4zAnDlt2FQQUZHFIxZEREQfEB2dgOnTj6JePUcMGFBHqjs7W+H27ZFwcLCUMR0RUf6QpSMWP//8M5o2bQpHR0fcv38fALB06VL89ttvORqOiIhITsnJqVi8+AwqVPgBK1cGY8qUw4iNTdZZhk0FEdEbejcW/v7+8PX1hbe3N16+fAmN5s0dMKytrbF06dKczkdERJTnhBDYvfs6qlVbifHjg/Dq1ZtmIj4+BRcvPpY5HRFR/qR3Y7Fs2TKsWbMGU6dOhVKplOru7u64cuVKjoYjIiLKa5cuRaJVq5/QteuvuHPnBQBAoQAGDKiNW7dGolUrV5kTEhHlT3pfYxEeHo46deqkqxsbGyM+Pj5HQhEREeW1x4/j8PXXh7Fp098Q4m29Zcuy8PPzRJ06DvKFIyIqAPRuLFxdXREaGooyZcro1Pfv34+qVavmWDAiIqK8NG/eKfz009/SdIUKxbFokQc6daoEhUIhYzIiooJB78ZiwoQJGD58OJKSkiCEwPnz57F161bMnTsXa9euzY2MREREue5//2uOn376GwYGCkyb1hzDhzeASqX88IpERAQgC41F//79kZqaiokTJyIhIQE9e/aEk5MTvv/+e/To0SM3MhIREeWoM2ce4OHDWHTrVk2qlSxpjt27u6F27VKwtTWTMR0RUcGUpedYDB48GIMHD0Z0dDS0Wi3s7OxyOhcREVGOu3fvJSZNOoRff70Ga2sTtGnjqtNEtGlTTsZ0REQFm953hZo5cybu3LkDAChRogSbCiIiyvdiY5MxZcohVK68HL/+eg0A8PJlEvz9g2VORkRUeOjdWOzatQtubm5o1KgRli9fjmfPnuVGLiIiomzTaLT48ceLqFhxGebNO43k5DfPXipZ0gyrVnXA5MkfyZyQiKjw0LuxuHz5Mi5fvozWrVvDz88PTk5O8Pb2xpYtW5CQkJAbGYmIiPR26NBd1KmzGkOG/IGnT9/cDl2lUmLixCa4dWskhgxxh6Gh3v8NEhHRO2TpE7VatWqYM2cO7t69i6NHj8LV1RVjxoxBqVKlcjofERGR3tavD4GHx8+4cuWpVPv886r455/hmD/fA8WKmciYjoiocMr2VzXm5uYwNTWFSqWCWq3OiUxERETZ0rVrFZQo8eaibHd3R5w82R+//vo5XF1tZE5GRFR4ZemuUOHh4diyZQs2b96Mmzdvonnz5pgxYwY+//zznM5HRET0XikpGoSGRqFBAyepVqyYCb7/vh00Gi169aoJAwM+4I6IKLfp3Vg0btwY58+fR40aNdC/f3/pORZERER5SQiB33+/gQkTgvD4cRxu3hwJR0dLaX7PnjVkTEdEVPTo3Vi0atUKa9euRbVq1T68MBERUS4IDY2Cr+9BHD16T6p9880RrF/fWb5QRERFnN6NxZw5c3IjBxER0QdFRsbhm2+OYMOGUAjxtt68eRmMGNFAvmBERJS5xsLX1xfffvstzM3N4evr+95l/fz8ciQYERFRmsRENfz8zmLu3FOIj397o5By5WywcKEHunSpDIWC11EQEckpU41FSEiIdMenkJCQXA1ERET0b5cvP8HHH2/BgwexUq1YMWP873/NMWJEAxgbZ+k+JERElMMy9Wl89OjRDP9ORESU28qXt4FG8+a8J6VSgaFD3TF9eguULGkuczIiIvo3vZ9jMWDAAMTFxaWrx8fHY8CAATkSioiIiq7Xr1N0ps3NVZg7tw3at6+Ay5eHYflybzYVRET5kN6NxU8//YTExMR09cTERGzatClHQhERUdETG5uMr78+DGdnP4SHv9CZ17t3TQQE9ELVqiVlSkdERB+S6RNTY2NjIYSAEAJxcXEwMTGR5mk0GgQEBMDOzi5XQhIRUeGl0Wixfn0IvvnmKJ4+jQcATJp0CL/++vahq7wwm4go/8t0Y2FtbQ2FQgGFQgE3N7d08xUKBWbOnJmj4YiIqHA7fPgufH0DcfnyE6mmUilRtqw1tFrBJ2YTERUgmW4sjh49CiEEWrdujV27dqF48eLSPJVKhTJlysDR0TFXQhIRUeFy40Y0JkwIwr59N3XqXbtWwfz5bVG+fPF3rElERPlVphuLFi1aAADCw8NRunRpHpYmIiK9vX6dgqlTD2PlymCkpmqler16DliyxAvNmpWRMR0REWVHphqLy5cvo3r16jAwMMCrV69w5cqVdy5bs2bNHAtHRESFi5GRAf7885bUVDg6WmLu3Db48suaPO2JiKiAy1RjUbt2bURFRcHOzg61a9eGQqGAECLdcgqFAhqNJsdDEhFR4WBsbIgFCzzQu/ceTJzYBOPHN4G5uUruWERElAMy1ViEh4ejZMmS0t+JiIg+5O+/ozBx4iEsXeqFKlXe3ia2S5fKuHt3FOztLWRMR0REOS1TjUWZMmUy/DsREdF/RUW9xjffHMH69SEQAhg/Pgh//tlTmq9QKNhUEBEVQll6QN6ff/4pTU+cOBHW1tZo0qQJ7t+/n6PhiIio4EhMVGPOnJOoWHEZ1q1701QAwD//RCM6OkHecERElOv0bizmzJkDU1NTAMDZs2exfPlyLFiwACVKlMDYsWNzPCAREeVvQghs23YVVaqswNSpR/D6dQoAwMrKGAsWtEVYmA9KlDCTOSUREeW2TN9uNs2DBw9QoUIFAMDevXvx2Wef4auvvkLTpk3RsmXLnM5HRET52F9/PcTYsQdx9uxDqWZgoMBXX9XFzJmtYGdnLmM6IiLKS3ofsbCwsEBMTAwAIDAwEG3btgUAmJiYIDExMWfTERFRvqXVCgwatE+nqfDyKo/Ll4fC3/9jNhVEREWM3kcsPDw8MGjQINSpUwc3b95Ehw4dAADXrl1D2bJlczofERHlUwYGCixa5IF27TajSpUSWLzYE+3bV5Q7FhERyUTvIxYrVqxA48aN8ezZM+zatQu2trYAgIsXL+KLL77I8YBERCQ/jUaL9etDEBoapVP38qqAvXu74++/h7KpICIq4vQ+YmFtbY3ly5enq8+cOTNHAhERUf5y9Gg4fH0DERoahRYtyuDo0b5QKN4+Jbtz58oypiMiovxC78YCAF6+fIl169bh+vXrUCgUqFKlCgYOHIhixYrldD4iIpLJrVsxmDAhCL/9dkOqHT9+H+fOPUTjxi4yJiMiovxI71OhgoODUb58eSxZsgTPnz9HdHQ0lixZgvLly+PSpUu5kZGIiPLQixeJGDv2AKpWXanTVNSpUwrHjvVlU0FERBnS+4jF2LFj0alTJ6xZswaGhm9WT01NxaBBgzBmzBicOHEix0MSEVHuU6s1WLUqGDNmHMfz52/v8ufgYIE5c9qgT59aMDBQvOcViIioKNO7sQgODtZpKgDA0NAQEydOhLu7e46GIyKivNO3715s3XpVmjY1NcSECU0wYUJTWFioZExGREQFgd6nQllZWSEiIiJd/cGDB7C0tMyRUERElPd8fOpLf//yy5q4cWMEZs5sxaaCiIgyRe8jFt27d8fAgQOxaNEiNGnSBAqFAqdOncKECRN4u1kiogLiyZPXeP48EVWqlJRqH31UGjNntkS7dhXQoIGTfOGIiKhA0ruxWLRoERQKBfr06YPU1FQAgJGREYYNG4Z58+bleEAiIso5SUmpWLr0HObMOYlKlUrgr78G6Vw3MW1aCxnTERFRQaZ3Y6FSqfD9999j7ty5uHPnDoQQqFChAszMzHIjHxER5QAhBHbsCMOkSYdw795LAEBw8GNs3nwZvXvXkjccEREVCpm+xiIhIQHDhw+Hk5MT7OzsMGjQIDg4OKBmzZpsKoiI8rHz5x/ho482oHv3nVJTYWCgwJAh9eDpWV7ecEREVGhk+ojF9OnTsXHjRvTq1QsmJibYunUrhg0bhh07duRmPiIiyqIHD15hypTD2Lz5ik7dw6McFi/2RI0a9jIlIyKiwijTjcXu3buxbt069OjRAwDw5ZdfomnTptBoNFAqlbkWkIiI9PfTT6EYOvRPJCWlSrVKlWyxeLEnvL0rQqHg8yiIiChnZbqxePDgAZo1ayZNN2jQAIaGhnj8+DFcXPgUViKi/KRGDXskJ79pKooXN8XMmS0xZEg9GBnxiyAiIsodmW4sNBoNVCrde5kbGhpKd4YiIiL5xMYmw8rKWJquW9cBX31VD2ZmRvjf/5rDxsZUxnRERFQUZLqxEEKgX79+MDZ++x9XUlIShg4dCnNzc6m2e/funE1IRETvdPv2c0yYEIRbt2IQGjoUhoZv78nh79+BpzwREVGeyXRj0bdv33S1L7/8MkfDEBFR5rx8mYRvvz2OZcvOQ63WAgDWrLmIYcPePj2bTQUREeWlTDcWGzZsyM0cRESUCWq1BqtXX8SMGccQE5Mo1UuVsuDpTkREJCu9H5BHRER5TwiB/ftvY9y4QPzzT7RUNzExxPjxjTFp0kewsFC95xWIiIhyFxsLIqJ87tatGAwfHoCgoLs69V69amDOnDYoXbqYTMmIiIjeYmNBRJTPaTQCR46ES9NNmrjAz88TDRs6y5iKiIhIl8GHF8ldK1euhKurK0xMTFCvXj2cPHkyU+udPn0ahoaGqF27du4GJCKSWeXKJTBsmDvKlCmGbdu64tSp/mwqiIgo35G1sdi+fTvGjBmDqVOnIiQkBM2aNUP79u0RERHx3vVevXqFPn36oE2bNnmUlIgo9wkhsHNnGDw8fpYebpdm9uw2+OefEejevTrv9kRERPlSlhqLn3/+GU2bNoWjoyPu378PAFi6dCl+++03vV7Hz88PAwcOxKBBg1ClShUsXboULi4u8Pf3f+96Q4YMQc+ePdG4ceOsxCciyndu305A69Y/4/PPd+DQobtYtuy8znwrK2OYmPDsVSIiyr/0/l/K398f06ZNw5gxYzB79mxoNBoAgLW1NZYuXYrOnTtn6nVSUlJw8eJFTJ48Wafu6emJM2fOvHO9DRs24M6dO/jll1/w3XfffXA7ycnJSE5OlqZjY2MBAGq1Gmq1OlNZc1radjO7fUMBKAAIAaTKlJlyh75jgQqfhw9jMXXqEWzdelOnfvp0BEaPrv+Otaiw4mcCpeFYICB/jAN9tq13Y7Fs2TKsWbMGn3zyCebNmyfV3d3dMX78+Ey/TnR0NDQaDezt7XXq9vb2iIqKynCdW7duYfLkyTh58iQMDTMXfe7cuZg5c2a6emBgIMzMzDKdNzcEBQVlajnPpCSY4s2TzgMDAnI3FMkis2OBCo+kJA327HmKPXueIiVFSHVHR2P06+eI+vVNEMDf9yKLnwmUhmOBAHnHQUJCQqaX1buxCA8PR506ddLVjY2NER8fr+/LpTtXWAiR4fnDGo0GPXv2xMyZM+Hm5pbp158yZQp8fX2l6djYWLi4uMDT0xNWVlZ6580JarUaQUFB8PDwgJGR0QeXN1xnAsQDJiYm8Pb2zoOElFf0HQtU8Gm1Ar/8cgXTph3D48evpbqFhRLTprWAj099qFRKGROSnPiZQGk4FgjIH+Mg7WyfzNC7sXB1dUVoaCjKlCmjU9+/fz+qVq2a6dcpUaIElEpluqMTT58+TXcUAwDi4uIQHByMkJAQjBgxAgCg1WohhIChoSECAwPRunXrdOsZGxvD2Ng4Xd3IyEj2X9RMZ/j/PkuhgOyZKXfkh/FIeSMi4hWGD9+P5OQ3p5EaGhpg2LB6aNAgEd27N+I4IAD8TKC3OBYIkHcc6LNdvRuLCRMmYPjw4UhKSoIQAufPn8fWrVsxd+5crF27NtOvo1KpUK9ePQQFBaFLly5SPSgoKMPrNKysrHDlyhWd2sqVK3HkyBHs3LkTrq6u+r4VIqI8V7p0MYwZ0wjz559Gp06VsGBBW5QrV4ynPRERUYGnd2PRv39/pKamYuLEiUhISEDPnj3h5OSE77//Hj169NDrtXx9fdG7d2+4u7ujcePG+PHHHxEREYGhQ4cCeHMa06NHj7Bp0yYYGBigevXqOuvb2dnBxMQkXZ2IKD949SoJS5acw4QJTWBurpLqX3/dDJ6e5dG69ZsvRHhxJhERFQZZunfh4MGDMXjwYERHR0Or1cLOzi5LG+/evTtiYmIwa9YsREZGonr16ggICJBOs4qMjPzgMy2IiPKb1FQt1qy5iGnTjiE6+s1FbzNmtJTmW1kZS00FERFRYZGtB+SVKFEiy01FGh8fH9y7dw/Jycm4ePEimjdvLs3buHEjjh079s51Z8yYgdDQ0Gxtn4goJx04cBu1aq2Cj0+A1FQsW3YeCQk8KkFERIVbli7eft9TX+/evZutQEREBVFY2DOMGxeIAwdu69R79KiOefPawMyMF18SEVHhpndjMWbMGJ1ptVqNkJAQHDhwABMmTMipXEREBcKzZ/GYMeMYVq++CI3m7fMoGjZ0wpIlXmjc2EXGdERERHlH78Zi9OjRGdZXrFiB4ODgbAciIiooUlI0qFNnNR49ipNqLi5WmD+/LXr0qP7eo7tERESFTbausfi39u3bY9euXTn1ckRE+Z5KpcTQoe4AAAsLFWbPbo0bN0bgiy9qsKkgIqIiJ0t3hcrIzp07Ubx48Zx6OSKifOfixccoX744rK1NpNq4cY3x8mUSxo9vglKlLGRMR0REJC+9G4s6derofBMnhEBUVBSePXuGlStX5mg4IqL84NGjWHz99RFs2vQ3xo9vjIULPaV5pqZGWLTI8z1rExERFQ16NxaffPKJzrSBgQFKliyJli1bonLlyjmVi4hIdvHxKVi06AwWLDgj3S72++//wrBh9VGunI3M6YiIiPIXvRqL1NRUlC1bFl5eXihVqlRuZSIikpVWK/DLL5fx9deHdS7MtrY2wfTpLeDsbCVjOiIiovxJr8bC0NAQw4YNw/Xr13MrDxGRrE6disDYsQcRHPxYqhkaGsDHxx3TprWAra2ZjOmIiIjyL71PhWrYsCFCQkJQpkyZ3MhDRCSbr77ahzVrLunUOnZ0w8KFHqhUqYRMqYiIiAoGvRsLHx8fjBs3Dg8fPkS9evVgbm6uM79mzZo5Fo6IKC9VrVpS+nuNGnbw8/NC27blZExERERUcGS6sRgwYACWLl2K7t27AwBGjRolzVMoFBBCQKFQQKPR5HxKIqIclpqqRVJSKiwsVFLNx6c+du++jt69a2LAgDpQKnPsUT9ERESFXqYbi59++gnz5s1DeHh4buYhIsp1gYF34Ot7EM2alYa//8dSXaVS4vjxfny4HRERURZkurEQQgAAr60gogLr+vVnGDcuEPv33/7/6WgMH94A1avbScuwqSAiIsoava6x4H+4RFQQRUcnYMaMY1i1KhgajZDq7u6O0Gi0MiYjIiIqPPRqLNzc3D7YXDx//jxbgYiIckpKigbLl5/HrFnH8epVslR3drbCvHlt8MUXNWBgwC9MiIiIcoJejcXMmTNRrFix3MpCRJRj9u+/hVGjDuD27bdfdpibG2Hy5I/g69sYZmZGMqYjIiIqfPRqLHr06AE7O7sPL0hEJLOHD2OlpkKhAPr1q43vvmsNR0dLmZMREREVTpluLHh9BREVJAMG1MGyZedRvLgp/Py8ULeug9yRiIiICjW97wpFRJSfJCSo4ed3Fo8fx2Hlyg5SXak0wJEjfWFra8ovRoiIiPJAphsLrZZ3TiGi/EOrFdi69QomTz6Mhw9jAQD9+9dG/fpO0jIlSpjJFY+IiKjI4WNliajAOXPmARo3Xocvv9wjNRVKpQJ//fVI5mRERERFl14XbxMRyenevZeYNOkQfv31mk7d27siFi3yQJUqJWVKRkRERGwsiCjfi41Nxty5J7FkyTkkJ2ukerVqJeHn5wVPz/IypiMiIiKAjQURFQD799/CvHmnpemSJc3w7betMHBgXRga8oxOIiKi/ID/IxNRvtetWzU0auQMlUqJiROb4NatkRgyxJ1NBRERUT7CIxZElK/88080fvvtH0ya9JFUUygUWLu2I0xNjVCunI2M6YiIiOhd2FgQUb4QE5OAmTOPw98/GKmpWjRq5IwWLcpK86tVs5MvHBEREX0QzyMgIlmlpGiwZMlZVKiwDMuWnUdq6ptn5ixadFbmZERERKQPHrEgIlkIIfD77zcwYUIQbt16LtXNzIwwaVJTjB/fRMZ0REREpC82FkSU50JDo+DrexBHj96TagoF0LdvbXz3XSs4OVnJF46IiIiyhI0FEeWpv/+OQt26qyHE21rz5mWwZIkX6tZ1kC8YERERZQuvsSCiPFWzpj1atXIFAJQrZ4Ndu7rh2LG+bCqIiIgKOB6xIKJcI4TAoUN30bZtOSgUCgBvbh3r5+eJoKC7GDmyAYyN+TFERERUGPCIBRHlirNnH6Bx43Xw9PwFAQG3dObVqlUK48c3YVNBRERUiLCxIKIcdf/+S/TosRNNmqzHX389AgCMGxco3UaWiIiICid+XUhEOSIuLhlz556Cn99ZJCdrpHrVqiXh5+cJQ0N+j0FERFSYsbEgomzRaLTYsCEU33xzBE+exEv1EiXMMGtWSwweXI9NBRERURHAxoKIsiwuLhkffbQBly8/kWoqlRKjRzfE1183g7W1iYzpiIiIKC+xsSCiLLO0NIarq7XUWHTtWgXz57dF+fLFZU5GREREeY2NBRFl2suXSShWzFi6dSwALFzogadP4zFvXls0b15GxnREREQkJ574TEQfpFZr8MMPf6Fcue+xY0eYzryKFW1x5sxANhVERERFHBsLInonIQT27buB6tX9MXr0Abx4kYRJkw4hKSlV7mhERESUz/BUKCLK0OXLT+DrexCHD4fr1Js1K42EBDVMTPjxQURERG9xz4CIdERFvcb//ncE69eHQqsVUv2jj0rDz88T9es7yZiOiIiI8is2FkQE4M1pT/Pnn8bs2Sfx+nWKVHd1tcaCBR7o2rWKzkXbRERERP/GxoKIAAAKhQKXLz+RmgpLSxW++aY5Ro1qyNOeiIiI6IN48TYRSebNawsLCxWGDq2H27dHYeLEpmwqiIiIKFO4x0BUBEVEvMKUKYfRtq0r+vevI9VLly6Ge/dGw9bWTMZ0REREVBCxsSAqQl6/TsH8+aewaNFZJCWl4siRcHz2WVVYWhpLy7CpICIioqxgY0FUBGg0Wvz009+YOvUIoqJeS3W1WoNr156hUSNnGdMRERFRYcDGgqiQO3o0HL6+gQgNjZJqRkYGGDmyAb75pjlsbExlTEdERESFBRsLokLq1q0YTJgQhN9+u6FT79KlMhYs8ECFCsVlSkZERESFERsLokJq8eKzOk1FnTql4OfnhZYty8oXioiIiAot3m6WqJCaObMlLCxUcHCwwIYNnREc/BWbCiIiIso1PGJBVMAJIRAQcAuxscn44osaUt3e3gIBAT1Rp44DLCxUMiYkIiKiooCNBVEBduXKE/j6BuLQobsoXtwU7dpV0LkYu1mzMjKmIyIioqKEp0IRFUBPnrzGkCH7ULv2ahw6dBcA8Px5In766W+ZkxEREVFRxSMWRAVIUlIqli49hzlzTiIuLkWqly1rjfnz2+Lzz6vKmI6IiIiKMjYWRAWAEAI7doRh0qRDuHfvpVS3tFRh6tRmGD26EUxM+OtMRERE8uGeCFEB8MMPf2HMmIPStIGBAoMH18XMmS1hb28hXzAiIiKi/8drLIgKgD59asHW9s1F2R4e5RAaOgSrVn3MpoKIiIjyDR6xIMpnXr9OwaVLkWje/O0dnWxsTLF8uTcsLVXw9q4IhUIhY0IiIiKi9GQ/YrFy5Uq4urrCxMQE9erVw8mTJ9+57O7du+Hh4YGSJUvCysoKjRs3xsGDB9+5PFFBotUKbNgQAje3ZejQYQuePHmtM79Hj+ro0MGNTQURERHlS7I2Ftu3b8eYMWMwdepUhISEoFmzZmjfvj0iIiIyXP7EiRPw8PBAQEAALl68iFatWqFjx44ICQnJ4+REOevEiftwd/8RAwb8jsjI13j9OgXTph2VOxYRERFRpsl6KpSfnx8GDhyIQYMGAQCWLl2KgwcPwt/fH3Pnzk23/NKlS3Wm58yZg99++w379u1DnTp18iIyUY66ffs55s0Lx7lzoTr1zp0rYfz4JvKEIiIiIsoC2RqLlJQUXLx4EZMnT9ape3p64syZM5l6Da1Wi7i4OBQvXvydyyQnJyM5OVmajo2NBQCo1Wqo1eosJM8+7T/b0fr+FCjXaSGQidNaEiKhACAEkCpTZspZL18mYe7c01i+/ALUaq1Ur1XLHgsXtkHLlmUBQLYxSnkr7efMnzdxLFAajgUC8sc40GfbsjUW0dHR0Gg0sLe316nb29sjKioqU6+xePFixMfHo1u3bu9cZu7cuZg5c2a6emBgIMzMzPQLnUNa358CS/VDQM8x8jpFgSMBAbkTivLMzZvx+Pbbu4iL00g1GxtD9OrlgFatiiMhIQwBAWEyJiS5BAUFyR2B8gmOBUrDsUCAvOMgISEh08vKfleo/16IKoTI1MWpW7duxYwZM/Dbb7/Bzs7unctNmTIFvr6+0nRsbCxcXFzg6ekJKyurrAfPBuU6LaAGhMIAMHPI3EoqC5g2mgHvit65G45yXfPmKfDz80dcXDxMTAzRsaMtli3rgeLFzeWORjJRq9UICgqCh4cHjIyM5I5DMuJYoDQcCwTkj3GQdrZPZsjWWJQoUQJKpTLd0YmnT5+mO4rxX9u3b8fAgQOxY8cOtG3b9r3LGhsbw9jYOF3dyMhIth+QdPqTmQMUQx9mej3Zu0DKkufPE1G8uKk0bWNjhDlz2iAo6C6+/bYFrl49jeLFzfkfB8n6uUT5C8cCpeFYIEDecaDPdmW7K5RKpUK9evXSHdoJCgpCkybvvmh169at6NevH7Zs2YIOHTrkdkyiLHv2LB7Dhv2B0qWX4P79lzrz+vevgy1buqJ06WLyhCMiIiLKYbLebtbX1xdr167F+vXrcf36dYwdOxYREREYOnQogDenMfXp00dafuvWrejTpw8WL16MRo0aISoqClFRUXj16pVcb4EoneTkVCxceBoVKizDqlUXER+vxpQph+WORURERJSrZD27pnv37oiJicGsWbMQGRmJ6tWrIyAgAGXKvHnicGRkpM4zLVavXo3U1FQMHz4cw4cPl+p9+/bFxo0b8zo+kQ4hBHbtuo6JE4MQHv5SqltYqFCzpn2mrx8iIiIiKohkP23fx8cHPj4+Gc77b7Nw7Nix3A9ElAXBwY8xduxBnDr1thFWKICBA+vg229bo1QpCxnTEREREeU+2RsLooLs+fNEjBlzAD//fFmn3rq1K/z8PFGrVimZkhERERHlLTYWRNlgZmaEkyffHqVwc7PFokUe+PhjN572REREREWKrBdvExV0JiaGmD+/LWxsTLB0qReuXBmGjh0rsakgIiKiIoeNBVEmnTx5H40br8ONG9E69c8/r4q7d0dj9OhGUKmUMqUjIiIikhcbC6IPuHv3BT777Fc0b74R5849xMSJh3TmKxQKWFubyJSOiIiIKH/gNRZE7/DqVRK+++4EfvjhPFJSNFL93r2XiI1NhpVV+ie6ExERERVVbCyI/iM1VYs1ay5i2rRjiI5OkOr29ub47rvW6N+/NpRKHuwjIiIi+jc2FkT/cuDAbYwbF4iwsGdSzdhYiXHjGmPy5I9gacmjFEREREQZYWNB9P80Gi18fQ/i+vW3F2f36FEd8+a1QZky1vIFIyIiIioAeD4H0f9TKg2waJEnAKBhQyecOTMAW7d2ZVNBRERElAk8YkFFUnJyKpYtOw8Pj3I6T8du374CgoJ6o00bVz6LgoiIiEgPbCyoSBFCYPfu65g48RDu3n2BVq3K4vDhPlIToVAo0LZtOZlTEhERERU8PBWKioyLFx+jZcuf8NlnO3D37gsAwLFj9/D3309kTkZERERU8LGxoELv0aNY9Ou3F/Xrr8GJE/eleqtWZXHp0hDUrl3qPWsTERERUWbwVCgqtOLjU7Bo0RksWHAGCQlqqV6xYnEsWuSJjh3deB0FERERUQ5hY0GF1hdf7MK+fTelaWtrE0yf3gI+PvWhUillTEZERERU+PBUKCq0xo1rDABQKhUYObIBbt8eiTFjGrGpICIiIsoFPGJBhUJ4+AskJKhRrZqdVGvRoizmzWuDzp0ro3LlEjKmIyIiIir8eMSCCrTY2GRMnnwIlSuvwMCBv0OrFTrzJ036iE0FERERUR5gY0EFUmqqFqtXB6NChR8wf/5ppKRo8Ndfj/Drr9fkjkZERERUJPFUKCpwgoLuwNc3EFevPpVqKpUSY8c2grd3RRmTERERERVdbCyowLh+/RnGjw9CQMAtnXq3btUwb14buLrayJSMiIiIiNhYUIGwYsV5jB59ABrN22so6td3xJIlXmjatLSMyYiIiIgIYGNBBUSDBk5SU+HsbIW5c9ugZ88aMDDgA+6IiIiI8gM2FpTvCCEQE5OIEiXMpFr9+k4YNswdDg4WGDeuCczMjGRMSERERET/xcaC8pWQkEj4+gbi+fNEXLr0FZTKtzcuW7myg4zJiIiIiOh9eLtZyhciI+MwYMBvqFfvRxw7dg+XLz/B+vUhcsciIiIiokziEQuSVUKCGosXn8H8+acRH6+W6uXL28DJyUrGZERERESkDzYWJAutVmDr1iuYPPkwHj6MlerFihlj2rQWGDGiAVQqpYwJiYiIiEgfbCwoz12+/ASDB+/D+fOPpJpSqcDQoe6YMaOlzkXbRERERFQwsLGgPKdUKnDx4mNp2tu7IhYu9EDVqiVlTEVERERE2cHGgnKdEAIKxdvnTVSrZoevvqqHEyfuw8/PC56e5WVMR0REREQ5gY0F5RqNRov160OwadNlHD7cR+eaiQULPGBiYghDQ96YjIiIiKgw4F4d5YpDh+6iTp3V+OqrP3DqVARWrDivM9/CQsWmgoiIiKgQ4RELylH//BONCROC8McfN3XqYWHPZEpERERERHmBjQXliJiYBMyceRz+/sFITdVKdXd3RyxZ4oWPPiotYzoiIiIiym1sLChbUlI0WLHiPGbNOoGXL5OkupOTJebObYNevWrCwEDxnlcgIiIiosKAjQVly8OHsZg8+TBSUjQAADMzI0ya1BTjxjWGublK5nRERERElFd49SxlS7lyNhg1qgEAoG/fWrh5cwSmTWvBpoKIiIioiGFjQZkWGRmHceMOIiFBrVP/5pvmCA4ejI0bP4GTk5VM6YiIiIhITjwVij4oMVENP7+zmDv3FOLj1bC2NsH//tdCml+smAnq1XOUMSERERERyY1HLOidhBDYuvUKKldegW++OYr4+DdHKlatuihdU0FEREREBLCxoHc4d+4hmjRZj549dyMi4hUAwMBAgWHD3BEaOkTnKdpERERERDwVinRERLzC5MmHsHXrVZ16u3YVsGiRB6pVs5MpGRERERHlZ2wsSJKYqEbduqsRE5Mo1apWLYnFiz3Rrl0FGZMRERERUX7HU6FIYmpqhJEj39w61tbWFCtWeOPvv4eyqSAiIiKiD+IRiyLs6NFw1KvnCCsrY6k2fnwTaLUCY8c2hrW1iYzpiIiIiKgg4RGLIujmzRh07rwNrVtvwty5J3XmmZurMHNmKzYVRERERKQXNhZFyPPniRgz5gCqVVuJ33+/AQBYsuScdNcnIiIiIqKs4qlQRYBarYG/fzBmzDiGFy+SpLqjoyXmzGkNZ2c+LZuIiIiIsoeNRSEmhMAff9zE+PFBuHkzRqqbmhpiwoQmmDixKczNVTImJCIiIqLCgo1FIdat207s3BmmU+vduybmzGnDoxRERERElKN4jUUh1rChk/T3jz4qjfPnB2HTpi5sKoiIiIgox/GIRSGRlJSKlBSNzq1jR45sgIMH72DIkHro2rUKFAqFjAmJiIj0J4RAamoqNBqN3FHylFqthqGhIZKSkorce6e38mIcKJVKGBoa5sh+IhuLAk4IgV9/vYZJkw7h44/dsHy5tzTP2NgQQUG9ZUxHRESUdSkpKYiMjERCQoLcUfKcEAKlSpXCgwcP+MVgEZZX48DMzAwODg5QqbJ37S0biwLs/PlHGDv2IM6ceQAAWLUqGMOH10eVKiVlTkZERJQ9Wq0W4eHhUCqVcHR0hEqlKlI72FqtFq9fv4aFhQUMDHjmelGV2+NACIGUlBQ8e/YM4eHhqFixYra2w8aiAIqIeIUpUw5jy5YrOvXWrV2hVPLDh4iICr6UlBRotVq4uLjAzMxM7jh5TqvVIiUlBSYmJmwsirC8GAempqYwMjLC/fv3pW1lFRuLAuT16xTMn38KixadRVJSqlSvXLkEFi/2RPv2FYrUtzlERFT4caeaKPfl1O8ZG4sCYvv2qxgz5iCiol5LNVtbU8yc2RJffVUPRkZK+cIRERERUZHHxqKAePEiSWoqjIwMMHJkA3zzTXPY2JjKnIyIiIiIiM+xyLeEEDrTgwbVRfXqdujSpTKuXfPB4sVebCqIiIioUImJiYGdnR3u3bsnd5RC48qVK3B2dkZ8fHyub4uNRT7z4kUifH0PYujQP3TqhoYGOH16AHbv7o6KFW1lSkdEREQf0q9fPygUCigUChgaGqJ06dIYNmwYXrx4kW7ZM2fOwNvbGzY2NjAxMUGNGjWwePHiDJ9ZcPToUXh7e8PW1hZmZmaoWrUqxo0bh0ePHuXF28oTc+fORceOHVG2bNl08zw9PaFUKnHu3Ll081q2bIkxY8akq+/duzfd9acpKSlYsGABatWqBTMzM5QoUQJNmzbFhg0boFarc+qtpBMREYGOHTvC3NwcJUqUwKhRo5CSkvLede7cuYMvv/wS9vb2sLKyQrdu3fDkyROdZW7evInOnTujRIkSsLKyQtOmTXH06FFpfo0aNdCgQQMsWbIkV97Xv7GxyCfUag2WLfsLFSosw5Il57BmzSVcuhSps8y/H35HRERE+Ve7du0QGRmJe/fuYe3atdi3bx98fHx0ltmzZw9atGgBZ2dnHD16FP/88w9Gjx6N2bNn44svvtA5e2H16tVo27YtSpUqhV27diEsLAyrVq3Cq1evsHjx4jx7Xx/aEc6OxMRErFu3DoMGDUo3LyIiAmfPnsWIESOwbt26LG8jJSUFXl5emDdvHr766iucOXMG58+fx/Dhw7Fs2TJcu3YtO2/hnTQaDTp06ID4+HicOnUK27Ztw65duzBu3Lh3rhMfH4927dpBoVDg0KFDOH36NFJSUtCxY0dotVppuQ4dOiA1NRVHjhzBxYsXUbt2bXz88ceIioqSlunfvz/8/f1z/2GLQmYrVqwQZcuWFcbGxqJu3brixIkT713+2LFjom7dusLY2Fi4uroKf39/vbb36tUrAUC8evUqO7GzRevvJMQiCK2/k9BqteKPP26IypWXC2CG9MfU9DuxcWOIbBkpb6SkpIi9e/eKlJQUuaOQjDgOKA3HwluJiYkiLCxMJCYmyh1Fb3379hWdO3fWqfn6+orixYtL069fvxa2trbi008/Tbf+77//LgCIdevWCY1GIx48eCBUKpUYM2ZMhtt78eLFO7O8ePFCDB48WNjZ2QljY2NRrVo1sW/fPiGEENOnTxe1atXSWX7JkiWiTJky6d7LnDlzhIODgyhTpoyYPHmyaNiwYbpt1ahRQ0ybNk2aXr9+vahcubIwNjYWlSpVEitWrHhnTiGE2LVrlyhRokSG82bMmCF69Oghrl+/LiwtLcXr16915rdo0UKMHj063Xp79uwR/97dnT9/vjAwMBCXLl1Kt2xKSkq6180pAQEBwsDAQDx69Eiqbd26VRgbG79zn/TgwYPCwMBA3L9/X2g0GiGEEM+fPxcARFBQkBBCiGfPngkAOvvPsbGxAoA4dOiQVEtOThbGxsbi8OHDGW7rfb9v+uw7y3rx9vbt2zFmzBisXLkSTZs2xerVq9G+fXuEhYWhdOnS6ZYPDw+Ht7c3Bg8ejF9++QWnT5+Gj48PSpYsia5du8rwDrLn6iMbjPP6BUFBd3XqX35ZE3PmtIaLSzGZkhEREeVTv7gD8VEfXi6nmZcCvgzO0qp3797FgQMHYGRkJNUCAwMRExOD8ePHp1u+Y8eOcHNzw65du9CvXz/s2LEDKSkpmDhxYoavb21tnWFdq9Wiffv2iIuLwy+//ILy5csjLCwMSqV+d5I8fPgwrKysEBQUJB1FmTdvHu7cuYPy5csDAK5du4YrV65g586dAIA1a9Zg+vTpWL58OerUqYOQkBAMHjwY5ubm6Nu3b4bbOXHiBNzd3dPVhRDYsGEDVqxYgcqVK8PNzQ2//vor+vfvr9f7AIDNmzejbdu2qFOnTrp5RkZGOj+jf4uIiEDVqlXf+9pffvklVq1aleG8s2fPonr16nB0dJRqXl5eSE5OxsWLF9GqVat06yQnJ0OhUMDY+O0ZK2nPszh16hTatm0LW1tbVKlSBZs2bULdunVhbGyM1atXw97eHvXq1ZPWU6lUqFWrFk6ePInWrVu/931kh6yNhZ+fHwYOHCgd8lq6dCkOHjwIf39/zJ07N93yq1atQunSpbF06VIAQJUqVRAcHIxFixYVqMbiaawJpu/5GGv+qguteNtUNGnigiVLvNCggZOM6YiIiPKx+Cjgdf6/puCPP/6AhYUFNBoNkpKSALzZ70lz8+ZNAG/2ZTJSqVIlaZlbt27BysoKDg4OemU4dOgQzp8/j+vXr8PNzQ0AUK5cOb3fi7m5OdauXQuVSiXVatasiS1btuB///sfgDc77PXr15e28+2332Lx4sX49NNPAQCurq4ICwvD6tWr39lY3Lt3T2fH+9/vIyEhAV5eXgDe7MCvW7cuS43FrVu30LJlS73Xc3R0RGho6HuXsbKyeue8qKgo2Nvb69RsbGygUql0Tln6t0aNGsHc3BwzZszAwoULoVAoMGnSJGi1WkRGvjldXqFQICgoCJ07d4alpSUMDAxgb2+PAwcOpGs4nZyccv2ieNkai5SUFFy8eBGTJ0/WqXt6euLMmTMZrnP27Fl4enrq1Ly8vLBu3Tqo1eoMu8zk5GQkJydL07GxsQAAtVqdqxfovM/B605Yfe5tR162bDHMnt0Kn31WBQqFQrZclPfSftb8mRdtHAeUhmPhLbVaDSEEtFqtzvnkCrNS8gQyKwXxrxzvI4RAy5YtsXLlSiQkJGDdunW4efMmhg8fLr2XtG/+NRqNzvv792soFArp30ChUGS43PuEhITA2dkZFSpUeOc2AOjM+29NCIHq1avD0NBQZ7mePXtiw4YNmDp1KoQQ2Lp1K0aPHg2tVotnz57hwYMHGDhwIAYPHiytk5qaimLFir3zfSQkJMDR0THd/LVr16Jbt24wMDCAVqtF9+7dMWHCBFy/fh2VKlXSyf7fddOm//vvru+/pYGBQaaasne97ru2K4TIMDcA2NraYtu2bfDx8cHq1athYGCAHj16oG7dutK/hRACw4YNQ8mSJXH8+HGYmppi3bp1+Pjjj/HXX3/pNKMmJiaIj4/PcFtpr6VWq9Md0dLn80i2xiI6OhoajSZd92Zvb//Ozi2jbs/e3h6pqamIjo7OsJOfO3cuZs6cma4eGBgIMzOzbLyDrOta4xqWOVfCjWcl0aV7GXz8cUmoVPewf/89WfKQ/IKCguSOQPkAxwGl4VgADA0NUapUKbx+/Vr3guGOh+QL9f9fTn6IWq2GsbEx7OzsALz59r5jx46YOnUqpk6dCgBwdnYGAAQHB6Nhw4bpXiNtpzkuLg6lS5fGq1evcPPmTZQqlfnGKq0ZiX1HbrVajdTUVJ35cXFxOuukvZf/vsbHH3+MKVOm4OTJk0hMTMSDBw/g7e2N2NhYvHr1CsCbM1H+e2qTUql8Z55ixYrh6dOnOvNfvHiB3377DWq1Wuc0I41Gg1WrVkn7eKampoiOjk732lFRUbC0tJTq5cuXx9WrV9+Z4V0ePHiAxo0bv3eZzz///J13XrKxscHZs2d1tvvy5Uuo1WqdfP/VuHFjhISEICYmBoaGhihWrBgqVaqEzp07IzY2FsePH8eff/6J8PBw6YjJ3LlzERgYiB9//BFjx46VXuvp06dwdXXNcFspKSlITEzEiRMnkJqaqjMvISHhve/732R/QN5/bwGW1qHrs3xG9TRTpkyBr6+vNB0bGwsXFxd4enq+95BVbjLYWhprvjwKO8cSKDn0hCwZKH9Qq9UICgqCh4fHO8/rpMKP44DScCy8lZSUhAcPHsDCwgImJiZyx9GLkZERDA0NdfYzZs6ciQ4dOmD06NFwdHRE586dUbx4caxevRoeHh466//++++4c+cOvv76a1haWqJXr16YOXMmVq1apXM6VZqXL19meJ1F/fr18fjxY0RFRUmnKP2bk5MTnj17BktLS2k/6p9//oGBgYGUPaP3Arw57ad58+b47bffkJiYiDZt2qBChQrSPCcnJ0RFRaF27dqZ/ndr0KABNm/erLOtTZs2wdnZGbt379ZZ9siRI5g3bx4WLlwIQ0NDVK9eHQcOHEiX8+rVq6hcubJU//LLLzF16lTcuXMn3XUWqampSE5Ohrm5ebpslSpVwqVLl96b38rK6p37li1atMDixYsRHx8vfRG+f/9+GBsbo1mzZu9cTwiBuLg4lC1bFgqFAkeOHMGzZ8/w+eef66xjbW0NCwsLadrQ0BAqlUpnmRs3bqB79+4ZbispKQmmpqZo3rx5ut83vZqwD17enUuSk5OFUqkUu3fv1qmPGjVKNG/ePMN1mjVrJkaNGqVT2717tzA0NMz0HTTyw12heNcPSsOxQEJwHNBbHAtvFba7QgkhRL169cTw4cOl6R07dgilUikGDx4s/v77bxEeHi7Wrl0rbGxsRNeuXcXz58+luwGtWLFCKBQKMWDAAHHs2DFx7949cerUKfHVV18JX1/fd2Zp2bKlqF69uggMDBR3794VAQEBYv/+/UIIIcLCwoRCoRDz5s0Tt2/fFsuXLxc2NjYZ3hUqIz/++KNwdHQUJUqUED///LPOvDVr1ghTU1OxdOlScePGDXH58mWxfv16sXjx4ndmvXz5sjA0NBTPnz+XarVq1RKTJk1Kt2xsbKwwNjYWe/fuFUIIER4eLkxNTYWPj48IDQ0VN27cEMuXLxfGxsbi119/ldZLSkoSzZo1EzY2NmL58uUiNDRU3LlzR2zfvl3UrVtXhISEvDNfdqSmporq1auLNm3aiEuXLolDhw4JZ2dnMWLECGmZhw8fikqVKom//vpLqq1du1YEBgaKmzdvip9//lkUL15c5+f97Nkz6e5iae97/PjxwsjISISGhkrLhYeHC4VCIe7du5dhvpy6K5Sst5tt0KCBGDZsmE6tSpUqYvLkyRkuP3HiRFGlShWd2tChQ0WjRo0yvU02FpSfcCyQEBwH9BbHwluFsbHYvHmzUKlUIiIiQqqdOHFCtGvXThQrVkyoVCpRtWpVsWjRIpGSkiJevHghNRZCCBEUFCS8vLyEjY2NMDExEZUrVxbjx48Xjx8/fmeWmJgY0b9/f2FraytMTExE9erVxR9//CHN9/f3Fy4uLsLc3Fz06dNHzJ49O9ONxYsXL4SxsbEwMzMTcXFxGb7f2rVrC5VKJWxsbETz5s3TfaH8X40aNRKrVq0SQggRHBwsAIjz589nuGzHjh1Fx44dpeng4GDh5eUl7OzshJWVlXB3dxdbt25Nt15SUpKYO3euqFGjhjAxMRHFixcXTZs2FRs3bhRqtfq9+bLj/v37okOHDsLU1FQUL15cjBgxQiQlJUnzw8PDBQBx9OhRqTZx4kRhZ2cnjIyMRMWKFcXixYuFVqvVed0LFy4IT09PUbx4cWFpaSkaNWokAgICdJaZM2eO8PLyeme2QtFYbNu2TRgZGYl169aJsLAwMWbMGGFubi51U5MnTxa9e/eWlr97964wMzMTY8eOFWFhYWLdunXCyMhI7Ny5M9PbZGNB+QnHAgnBcUBvcSy8VZAbi5yg0WjSNRZFwZ9//imqVKlS5N73u+TEOEhKShIuLi7i1KlT71ymUDzHonv37oiJicGsWbMQGRmJ6tWrIyAgAGXKlAEAREZGIiIiQlre1dUVAQEBGDt2LFasWAFHR0f88MMPBepWs0RERESUMW9vb9y6dQuPHj2Ci4uL3HEKhfv372Pq1Klo2rRprm9L9ou3fXx80j3iPs3GjRvT1Vq0aPHBi2eIiIiIqGAaPXq03BEKFTc3twwv3s8NBnmyFSIiIiIiKtTYWBARERERUbaxsSAiIqJ8S/z/86qIKPfk1O8ZGwsiIiLKd9IeEKjPU3+JKGvSfs+y+2BO2S/eJiIiIvovpVIJa2trPH36FABgZmYmPR26KNBqtUhJSUFSUhIMDPg9cFGV2+NACIGEhAQ8ffoU1tbWUCqV2Xo9NhZERESUL5UqVQoApOaiKBFCIDExEaampkWqoSJdeTUOrK2tpd+37GBjQURERPmSQqGAg4MD7OzsoFar5Y6Tp9RqNU6cOIHmzZtn+/QUKrjyYhwYGRll+0hFGjYWRERElK8plcoc2/EpKJRKJVJTU2FiYsLGoggraOOAJ+0REREREVG2sbEgIiIiIqJsY2NBRERERETZVuSusUh7AEhsbKxsGdRqNRISEhAbG1sgzpej3MOxQADHAb3FsUBpOBYIyB/jIG2fOTMP0StyjUVcXBwAwMXFReYkREREREQFQ1xcHIoVK/beZRQip57hXUBotVo8fvwYlpaWst0XOjY2Fi4uLnjw4AGsrKxkyUD5A8cCARwH9BbHAqXhWCAgf4wDIQTi4uLg6Oj4wYf0FbkjFgYGBnB2dpY7BgDAysqKHxYEgGOB3uA4oDQcC5SGY4EA+cfBh45UpOHF20RERERElG1sLIiIiIiIKNvYWMjA2NgY06dPh7GxsdxRSGYcCwRwHNBbHAuUhmOBgII3DorcxdtERERERJTzeMSCiIiIiIiyjY0FERERERFlGxsLIiIiIiLKNjYWuWTlypVwdXWFiYkJ6tWrh5MnT753+ePHj6NevXowMTFBuXLlsGrVqjxKSrlNn7Gwe/dueHh4oGTJkrCyskLjxo1x8ODBPExLuUXfz4Q0p0+fhqGhIWrXrp27ASnP6DsWkpOTMXXqVJQpUwbGxsYoX7481q9fn0dpKbfoOw42b96MWrVqwczMDA4ODujfvz9iYmLyKC3llhMnTqBjx45wdHSEQqHA3r17P7hOvt5nFJTjtm3bJoyMjMSaNWtEWFiYGD16tDA3Nxf379/PcPm7d+8KMzMzMXr0aBEWFibWrFkjjIyMxM6dO/M4OeU0fcfC6NGjxfz588X58+fFzZs3xZQpU4SRkZG4dOlSHiennKTvOEjz8uVLUa5cOeHp6Slq1aqVN2EpV2VlLHTq1Ek0bNhQBAUFifDwcPHXX3+J06dP52Fqymn6joOTJ08KAwMD8f3334u7d++KkydPimrVqolPPvkkj5NTTgsICBBTp04Vu3btEgDEnj173rt8ft9nZGORCxo0aCCGDh2qU6tcubKYPHlyhstPnDhRVK5cWac2ZMgQ0ahRo1zLSHlD37GQkapVq4qZM2fmdDTKQ1kdB927dxfffPONmD59OhuLQkLfsbB//35RrFgxERMTkxfxKI/oOw4WLlwoypUrp1P74YcfhLOzc65lpLyXmcYiv+8z8lSoHJaSkoKLFy/C09NTp+7p6YkzZ85kuM7Zs2fTLe/l5YXg4GCo1epcy0q5Kytj4b+0Wi3i4uJQvHjx3IhIeSCr42DDhg24c+cOpk+fntsRKY9kZSz8/vvvcHd3x4IFC+Dk5AQ3NzeMHz8eiYmJeRGZckFWxkGTJk3w8OFDBAQEQAiBJ0+eYOfOnejQoUNeRKZ8JL/vMxrKHaCwiY6Ohkajgb29vU7d3t4eUVFRGa4TFRWV4fKpqamIjo6Gg4NDruWl3JOVsfBfixcvRnx8PLp165YbESkPZGUc3Lp1C5MnT8bJkydhaMiP6cIiK2Ph7t27OHXqFExMTLBnzx5ER0fDx8cHz58/53UWBVRWxkGTJk2wefNmdO/eHUlJSUhNTUWnTp2wbNmyvIhM+Uh+32fkEYtcolAodKaFEOlqH1o+ozoVPPqOhTRbt27FjBkzsH37dtjZ2eVWPMojmR0HGo0GPXv2xMyZM+Hm5pZX8SgP6fOZoNVqoVAosHnzZjRo0ADe3t7w8/PDxo0bedSigNNnHISFhWHUqFGYNm0aLl68iAMHDiA8PBxDhw7Ni6iUz+TnfUZ+FZbDSpQoAaVSme5bh6dPn6brMNOUKlUqw+UNDQ1ha2uba1kpd2VlLKTZvn07Bg4ciB07dqBt27a5GZNymb7jIC4uDsHBwQgJCcGIESMAvNm5FELA0NAQgYGBaN26dZ5kp5yVlc8EBwcHODk5oVixYlKtSpUqEELg4cOHqFixYq5mppyXlXEwd+5cNG3aFBMmTAAA1KxZE+bm5mjWrBm+++472b+lpryT3/cZecQih6lUKtSrVw9BQUE69aCgIDRp0iTDdRo3bpxu+cDAQLi7u8PIyCjXslLuyspYAN4cqejXrx+2bNnC82cLAX3HgZWVFa5cuYLQ0FDpz9ChQ1GpUiWEhoaiYcOGeRWdclhWPhOaNm2Kx48f4/Xr11Lt5s2bMDAwgLOzc67mpdyRlXGQkJAAAwPdXTalUgng7bfVVDTk+31GmS4aL9TSbiO3bt06ERYWJsaMGSPMzc3FvXv3hBBCTJ48WfTu3VtaPu3WYWPHjhVhYWFi3bp1+erWYZR1+o6FLVu2CENDQ7FixQoRGRkp/Xn58qVcb4FygL7j4L94V6jCQ9+xEBcXJ5ydncVnn30mrl27Jo4fPy4qVqwoBg0aJNdboByg7zjYsGGDMDQ0FCtXrhR37twRp06dEu7u7qJBgwZyvQXKIXFxcSIkJESEhIQIAMLPz0+EhIRItx4uaPuMbCxyyYoVK0SZMmWESqUSdevWFcePH5fm9e3bV7Ro0UJn+WPHjok6deoIlUolypYtK/z9/fM4MeUWfcZCixYtBIB0f/r27Zv3wSlH6fuZ8G9sLAoXfcfC9evXRdu2bYWpqalwdnYWvr6+IiEhIY9TU07Tdxz88MMPomrVqsLU1FQ4ODiIXr16iYcPH+ZxasppR48efe//+wVtn1EhBI+hERERERFR9vAaCyIiIiIiyjY2FkRERERElG1sLIiIiIiIKNvYWBARERERUbaxsSAiIiIiomxjY0FERERERNnGxoKIiIiIiLKNjQUREREREWUbGwsiokJi48aNsLa2ljtGlpUtWxZLly597zIzZsxA7dq18yQPERHph40FEVE+0q9fPygUinR/bt++LXc0bNy4USeTg4MDunXrhvDw8Bx5/QsXLuCrr76SphUKBfbu3auzzPjx43H48OEc2d67/Pd92tvbo2PHjrh27Zrer1OQGz0iIn2xsSAiymfatWuHyMhInT+urq5yxwIAWFlZITIyEo8fP8aWLVsQGhqKTp06QaPRZPu1S5YsCTMzs/cuY2FhAVtb22xv60P+/T7//PNPxMfHo0OHDkhJScn1bRMRFVRsLIiI8hljY2OUKlVK549SqYSfnx9q1KgBc3NzuLi4wMfHB69fv37n6/z9999o1aoVLC0tYWVlhXr16iE4OFiaf+bMGTRv3hympqZwcXHBqFGjEB8f/95sCoUCpUqVgoODA1q1aoXp06fj6tWr0hEVf39/lC9fHiqVCpUqVcLPP/+ss/6MGTNQunRpGBsbw9HREaNGjZLm/ftUqLJlywIAunTpAoVCIU3/+1SogwcPwsTEBC9fvtTZxqhRo9CiRYsce5/u7u4YO3Ys7t+/jxs3bkjLvO/ncezYMfTv3x+vXr2SjnzMmDEDAJCSkoKJEyfCyckJ5ubmaNiwIY4dO/bePEREBQEbCyKiAsLAwAA//PADrl69ip9++glHjhzBxIkT37l8r1694OzsjAsXLuDixYuYPHkyjIyMAABXrlyBl5cXPv30U1y+fBnbt2/HqVOnMGLECL0ymZqaAgDUajX27NmD0aNHY9y4cbh69SqGDBmC/v374+jRowCAnTt3YsmSJVi9ejVu3bqFvXv3okaNGhm+7oULFwAAGzZsQGRkpDT9b23btoW1tTV27dol1TQaDX799Vf06tUrx97ny5cvsWXLFgCQ/v2A9/88mjRpgqVLl0pHPiIjIzF+/HgAQP/+/XH69Gls27YNly9fxueff4527drh1q1bmc5ERJQvCSIiyjf69u0rlEqlMDc3l/589tlnGS7766+/CltbW2l6w4YNolixYtK0paWl2LhxY4br9u7dW3z11Vc6tZMnTwoDAwORmJiY4Tr/ff0HDx6IRo0aCWdnZ5GcnCyaNGkiBg8erLPO559/Lry9vYUQQixevFi4ubmJlJSUDF+/TJkyYsmSJdI0ALFnzx6dZaZPny5q1aolTY8aNUq0bt1amj548KBQqVTi+fPn2XqfAIS5ubkwMzMTAAQA0alTpwyXT/Ohn4cQQty+fVsoFArx6NEjnXqbNm3ElClT3vv6RET5naG8bQ0REf1Xq1at4O/vL02bm5sDAI4ePYo5c+YgLCwMsbGxSE1NRVJSEuLj46Vl/s3X1xeDBg3Czz//jLZt2+Lzzz9H+fLlAQAXL17E7du3sXnzZml5IQS0Wi3Cw8NRpUqVDLO9evUKFhYWEEIgISEBdevWxe7du6FSqXD9+nWdi68BoGnTpvj+++8BAJ9//jmWLl2KcuXKoV27dvD29kbHjh1haJj1/4p69eqFxo0b4/Hjx3B0dMTmzZvh7e0NGxubbL1PS0tLXLp0CampqTh+/DgWLlyIVatW6Syj788DAC5dugQhBNzc3HTqycnJeXLtCBFRbmJjQUSUz5ibm6NChQo6tfv378Pb2xtDhw7Ft99+i+LFi+PUqVMYOHAg1Gp1hq8zY8YM9OzZE3/++Sf279+P6dOnY9u2bejSpQu0Wi2GDBmic41DmtKlS78zW9oOt4GBAezt7dPtQCsUCp1pIYRUc3FxwY0bNxAUFIRDhw7Bx8cHCxcuxPHjx3VOMdJHgwYNUL58eWzbtg3Dhg3Dnj17sGHDBml+Vt+ngYGB9DOoXLkyoqKi0L17d5w4cQJA1n4eaXmUSiUuXrwIpVKpM8/CwkKv905ElN+wsSAiKgCCg4ORmpqKxYsXw8DgzeVxv/766wfXc3Nzg5ubG8aOHYsvvvgCGzZsQJcuXVC3bl1cu3YtXQPzIf/e4f6vKlWq4NSpU+jTp49UO3PmjM5RAVNTU3Tq1AmdOnXC8OHDUblyZVy5cgV169ZN93pGRkaZuttUz549sXnzZjg7O8PAwAAdOnSQ5mX1ff7X2LFj4efnhz179qBLly6Z+nmoVKp0+evUqQONRoOnT5+iWbNm2cpERJTf8OJtIqICoHz58khNTcWyZctw9+5d/Pzzz+lOzfm3xMREjBgxAseOHcP9+/dx+vRpXLhwQdrJnzRpEs6ePYvhw4cjNDQUt27dwu+//46RI0dmOeOECROwceNGrFq1Crdu3YKfnx92794tXbS8ceNGrFu3DlevXpXeg6mpKcqUKZPh65UtWxaHDx9GVFQUXrx48c7t9urVC5cuXcLs2bPx2WefwcTERJqXU+/TysoKgwYNwvTp0yGEyNTPo2zZsnj9+jUOHz6M6OhoJCQkwM3NDb169UKfPn2we/duhIeH48KFC5g/fz4CAgL0ykRElO/IeYEHERHp6tu3r+jcuXOG8/z8/ISDg4MwNTUVXl5eYtOmTQKAePHihRBC92Lh5ORk0aNHD+Hi4iJUKpVwdHQUI0aM0Llg+fz588LDw0NYWFgIc3NzUbNmTTF79ux3ZsvoYuT/WrlypShXrpwwMjISbm5uYtOmTdK8PXv2iIYNGworKythbm4uGjVqJA4dOiTN/+/F27///ruoUKGCMDQ0FGXKlBFCpL94O039+vUFAHHkyJF083Lqfd6/f18YGhqK7du3CyE+/PMQQoihQ4cKW1tbAUBMnz5dCCFESkqKmDZtmihbtqwwMjISpUqVEl26dBGXL19+ZyYiooJAIYQQ8rY2RERERERU0PFUKCIiIiIiyjY2FkRERERElG1sLIiIiIiIKNvYWBARERERUbaxsSAiIiIiomxjY0FERERERNnGxoKIiIiIiLKNjQUREREREWUbGwsiIiIiIso2NhZERERERJRtbCyIiIiIiCjb2FgQEREREVG2/R+EzcqLtxpZCQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from scipy import io\n",
    "from scipy.stats import norm\n",
    "from scipy.special import expit  # Sigmoid\n",
    "\n",
    "# data\n",
    "data_path = \"../data/\"\n",
    "suv_file = data_path + \"suv_percentilesSLOthenUWM.mat\"\n",
    "flags_file = data_path + \"flags_combined.mat\"\n",
    "\n",
    "suv_dict = io.loadmat(suv_file)\n",
    "flags_dict = io.loadmat(flags_file)\n",
    "\n",
    "suv = suv_dict['lung_SUVperc_COMBINED'][0:58, :, :]\n",
    "flags = flags_dict['flags'][0:58, 3]\n",
    "\n",
    "p = 94  # Percentile index\n",
    "X_raw = np.nanmax(suv[:, :, p], axis=1).reshape(-1, 1)  # Raw SUV\n",
    "X = X_raw  # Use raw SUV (not log-transformed)\n",
    "\n",
    "#  Logistic Regression \n",
    "logr = LogisticRegression(penalty=None, solver='lbfgs')\n",
    "logr.fit(X, flags)\n",
    "\n",
    "#  Parameters and Confidence Intervals (Delta Method)\n",
    "params = np.insert(logr.coef_[0], 0, logr.intercept_[0])  # [intercept, coef]\n",
    "X_design = np.hstack([np.ones((X.shape[0], 1)), X])\n",
    "pred_probs = logr.predict_proba(X)[:, 1]\n",
    "V = np.diagflat(pred_probs * (1 - pred_probs))\n",
    "cov_matrix = np.linalg.inv(X_design.T @ V @ X_design)\n",
    "\n",
    "se_params = np.sqrt(np.diag(cov_matrix))\n",
    "z = norm.ppf(1 - 0.05 / 2)\n",
    "ci_lower = params - z * se_params\n",
    "ci_upper = params + z * se_params\n",
    "\n",
    "# --- Print estimated parameters and their CIs ---\n",
    "print(\"\\nFitted Parameters:\")\n",
    "print(f\"Intercept (w0): {params[0]:.4f} [{ci_lower[0]:.4f}, {ci_upper[0]:.4f}]\")\n",
    "print(f\"Coefficient (w1): {params[1]:.4f} [{ci_lower[1]:.4f}, {ci_upper[1]:.4f}]\")\n",
    "\n",
    "# Function to compute probability and CI at any x \n",
    "def predict_prob_ci(x, beta, cov, alpha=0.05):\n",
    "    x_vec = np.array([1, x])\n",
    "    f_hat = x_vec @ beta\n",
    "    var_f = x_vec @ cov @ x_vec.T\n",
    "    se_f = np.sqrt(var_f)\n",
    "    z = norm.ppf(1 - alpha / 2)\n",
    "    ci_logit = [f_hat - z * se_f, f_hat + z * se_f]\n",
    "    return expit(f_hat), expit(ci_logit[0]), expit(ci_logit[1])\n",
    "\n",
    "#  Evaluate predicted probabilities and CIs \n",
    "x_vals = np.linspace(X.min(), X.max(), 500)\n",
    "results = np.array([predict_prob_ci(x, params, cov_matrix) for x in x_vals])\n",
    "mean_probs, lower_ci, upper_ci = results.T\n",
    "\n",
    "#  Plotting \n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.title(\"AE Probability from Logistic Regression (Delta Method CI)\")\n",
    "plt.xlabel(r\"$x = \\max_{visit} SUV(visit, p=94)$\")\n",
    "plt.ylabel(\"P(AE | X = x)\")\n",
    "\n",
    "plt.scatter(X[flags == 0], flags[flags == 0], label=\"NC\", color='blue', alpha=0.6)\n",
    "plt.scatter(X[flags == 1], flags[flags == 1], label=\"AE\", color='red', alpha=0.6)\n",
    "\n",
    "plt.plot(x_vals, mean_probs, color='darkgreen', linewidth=2, label=\"Predicted Probability\")\n",
    "plt.fill_between(x_vals, lower_ci, upper_ci, color='green', alpha=0.2, label=\"95% CI (Delta Method)\")\n",
    "\n",
    "plt.legend(loc='center right')\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "# Compute log-likelihood\n",
    "y_true = flags\n",
    "eps = 1e-15  # to avoid log(0)\n",
    "log_likelihood = np.sum(\n",
    "    y_true * np.log(pred_probs + eps) + (1 - y_true) * np.log(1 - pred_probs + eps)\n",
    ")\n",
    "\n",
    "# Number of parameters (intercept + 1 coef)\n",
    "k = len(params)\n",
    "# Number of observations\n",
    "n = len(y_true)\n",
    "\n",
    "# Compute AIC and BIC\n",
    "AIC = 2 * k - 2 * log_likelihood\n",
    "BIC = k * np.log(n) - 2 * log_likelihood\n",
    "\n",
    "print(\"\\nModel Selection Criteria:\")\n",
    "print(f\"Log-Likelihood: {log_likelihood:.4f}\")\n",
    "print(f\"AIC: {AIC:.4f}\")\n",
    "print(f\"BIC: {BIC:.4f}\")\n",
    "\n",
    "# Raw residuals (for diagnostics)\n",
    "residuals = y_true - pred_probs\n",
    "\n",
    "# Optional: Plot residuals\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.title(\"Residuals of Logistic Regression Model\")\n",
    "plt.xlabel(\"Predicted Probability\")\n",
    "plt.ylabel(\"Residual (Observed - Predicted)\")\n",
    "plt.scatter(pred_probs, residuals, alpha=0.7)\n",
    "plt.axhline(0, linestyle='--', color='gray')\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, roc_auc_score\n",
    "\n",
    "# Convert predicted probabilities to binary predictions using 0.5 threshold\n",
    "y_pred_binary = (pred_probs >= 0.5).astype(int)\n",
    "\n",
    "# Accuracy\n",
    "accuracy = accuracy_score(flags, y_pred_binary)\n",
    "print(f\"\\nAccuracy: {accuracy:.4f}\")\n",
    "\n",
    "# Confusion matrix\n",
    "cm = confusion_matrix(flags, y_pred_binary)\n",
    "print(\"\\nConfusion Matrix:\")\n",
    "print(cm)\n",
    "\n",
    "# Precision, Recall, F1-score\n",
    "print(\"\\nClassification Report:\")\n",
    "print(classification_report(flags, y_pred_binary, digits=4))\n",
    "\n",
    "# ROC AUC Score\n",
    "roc_auc = roc_auc_score(flags, pred_probs)\n",
    "print(f\"ROC AUC Score: {roc_auc:.4f}\")\n",
    "\n",
    "\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "fpr, tpr, _ = roc_curve(flags, pred_probs)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC curve (AUC = {roc_auc:.2f})')\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "490cf666",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from scipy import io\n",
    "from scipy.special import expit  # Sigmoid\n",
    "\n",
    "# Load data\n",
    "suv = io.loadmat(\"../data/suv_percentilesSLOthenUWM.mat\")['lung_SUVperc_COMBINED'][0:58, :, :]\n",
    "flags = io.loadmat(\"../data/flags_combined.mat\")['flags'][0:58, 3]\n",
    "\n",
    "# Prepare input features\n",
    "p = 94  # Percentile index\n",
    "X = np.nanmax(suv[:, :, p], axis=1).reshape(-1, 1)\n",
    "y = flags\n",
    "\n",
    "# Fit logistic regression\n",
    "model = LogisticRegression(penalty=None, solver='lbfgs')\n",
    "model.fit(X, y)\n",
    "\n",
    "# Predict over range\n",
    "x_vals = np.linspace(X.min(), X.max(), 500)\n",
    "probs = model.predict_proba(x_vals.reshape(-1, 1))[:, 1]\n",
    "\n",
    "# Plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(x_vals, probs, color='darkgreen', linewidth=2, label=\"Predicted Probability\")\n",
    "\n",
    "# Scatter points\n",
    "plt.scatter(X[y == 0], y[y == 0], color='blue', alpha=0.6, label=\"NC (y=0)\")\n",
    "plt.scatter(X[y == 1], y[y == 1], color='red', alpha=0.6, label=\"AE (y=1)\")\n",
    "\n",
    "plt.xlabel(r\"$x = \\max_{visit} SUV(visit, p=94)$\")\n",
    "plt.ylabel(\"P(AE | X = x)\")\n",
    "plt.title(\"S-Curve with AE and NC Samples\")\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "5b842f0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Fitted Parameters (using log(SUV)):\n",
      "Intercept (w0): -7.5253 [-11.9289, -3.1217]\n",
      "Coefficient (w1): 10.7318 [3.3999, 18.0636]\n"
     ]
    },
    {
     "data": {
      "image/png": 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rsX79euHv729/H/bz8xMPP/xwmdfZ7RTdJbf59NNP7csyMjKERqO54fO4U6dOon379uVed62KvkeXZOjcuXOp9QwGg/D19RX9+vUrtdxqtYqWLVuW+oKkffv2IjQ0VBQUFNiX5ebmCl9f31sW3e+9954AIDZt2nTDx3Kz9+677rpLBAYGiry8PPsyi8UimjVrJsLDw+1fVpe8xh977LEbbmfEiBEiKCjohtcTUfVi93IiqpQ5c+ZAp9Nh2LBhAAB3d3c8/PDD2LlzZ7kjIw8ZMgT79+8v83P//fdXaHtDhw6FSqWCXq9H586dYbVasWLFCrRo0aLUeoMGDSp1ecuWLQBQpuvfww8/DDc3tzJdCps2bWo/37DE8OHDkZubi0OHDtmXLV++HJ06dYK7uzuUSiVUKhXmzJlT7ojqPXr0QFBQkP2yQqHA0KFDce7cuTJdR6vSI488gsDAQHz55Zf2ZTNnzkRAQMAdD+p0p/u5VatWCAsLs1+OiYkBUNyd99pzD0uWXzvy8u3asmULevTogTp16pRaPnr0aBiNRvtgW9u3b0ezZs3KnD//yCOP3HIbJV3HhwwZgmXLlt3xQH+bNm2C1WrFc889d9v3cdddd0GlUsHDwwO9e/eGj48P1qxZYz+39dy5czh16hRGjBgBALBYLPaf+++/H0lJSTh9+jSA4n3TvXt3+Pv72+9fLpdjyJAh5W67RYsWaNiwYall69atQ7du3RAaGlpqW3369LFvAwDatWuH7OxsPPLII1izZg3S09PL3H+7du3wyy+/4D//+Q+2bduGgoKCW+6Pkm7M1z9X27Vrh5iYmDLP1eDgYLRr167M47rVczI7OxtGoxGBgYFVlmnv3r0oKioqs7/vuuuuMqc5JCYmAkC527///vtx5coVrFq1Cq+//jqaNm2K1atXo3///hWabeFmunTpgnr16pXqYr5o0SIUFRWV6VpeIjAwsMKvlYq+RwNl36d2796NzMxMjBo1qtRzz2azoXfv3ti/fz8MBgMMBgP279+PgQMHQqvV2m/v4eGBfv363TLjL7/8goYNG+Lee++t0GO6lsFgwJ9//onBgwfD3d3dvlyhUGDkyJGIj4+3vx5v9DivFRgYiNTU1DKnbhCRNFh0E1GFnTt3Djt27EDfvn0hhEB2djays7MxePBgAP+OaH6tgIAAxMXFlfnx9fWt0DanT5+O/fv349ChQ7hy5QouXLiABx98sMx615+nmJGRAaVSaT9/roRMJkNwcLD9/MYSwcHBZe6zZFnJuitXrsSQIUMQFhaGH374AXv27MH+/fvx+OOPo7Cw8Ia3v9l9OoJGo8HTTz+NxYsXIzs7G2lpaVi2bBmeeOIJaDSaO7rvO93P1x93tVp90+Xl7dfKysjIKPc81tDQUPv1Jb+v/ZKkRHnLrte5c2esXr0aFosFjz32GMLDw9GsWbNyz62viLS0NAC4o4HAFixYgP3792PLli14+umncfLkyVJfIJSc2/36669DpVKV+hk3bhwA2Aveyu6b8vZ3SkoK1q5dW2ZbTZs2LbWtkSNHYu7cubh8+TIGDRqEwMBAtG/fHps2bbLf1//+9z+89dZbWL16Nbp16wZfX188+OCDN50S7WbnNIeGhpZ5rvr5+ZVZT6PR3LLAL7n+2qLtTjOV/K7IMbjV9nU6HR588EF8/PHH2L59O86dO4cmTZrgyy+/xPHjxwHA/sXMjUYjt1gsZQYmk8lkePzxx/H333/bx2P4/vvvERUVhW7dupV7P1qttkJfmACVez+9fn+WPNcHDx5c5vk3ffp0CCGQmZmJrKws2Gy2m27rZtLS0m77NZuVlQUhRIXeq0rc6Px8oHjfCiGq5D2UiO4cRy8nogqbO3cuhBBYsWJFufO3zp8/H++//z4UCkWVbTM6Oto+ANTNXD9gkp+fHywWC9LS0koVhEIIJCcn21smSyQnJ5e5z5JlJR++f/jhB0RFRWHp0qWltlcyeM2Nbn+z+3SUZ599FtOmTcPcuXNRWFgIi8WCZ5555o7v9073sxT8/PyQlJRUZnlJi2BJ662fn1+ZQcaA8o9jeQYMGIABAwagqKgIe/fuxUcffYThw4cjMjISHTp0qFTmkn0ZHx9fpoW+omJiYuyvnW7dusFqtWL27NlYsWIFBg8ebH/cb7/9NgYOHFjufZQMxFXZfVPeoHD+/v5o0aIFPvjgg3JvU1JYAMWDzY0ZMwYGgwE7duzApEmT8MADD+DMmTOIiIiAm5sbpkyZgilTpiAlJcXe6t2vXz+cOnWq3Psvec0lJSWVKYwSExNLteLfiZLtZGZmVnjdW2UqWe9Gx+Da1u6S21Rk+wBQt25dPPXUU3j55Zdx/PhxNG3a1F7I36gVOiEhodwvAEaPHo2JEydi7ty5UKlUOHz4MKZOnXrD0ckzMzMrvN8r8356/fZKtjFz5swbzhYRFBRkH+n8Ztu6mYCAgNvuxeTj4wO5XF6h96oSNxv1PTMzExqNplSrORFJhy3dRFQhVqsV8+fPR7169bB169YyP6+99hqSkpLwyy+/SB0VAOzTXf3www+llv/0008wGAxlpsM6fvw4jh49WmrZ4sWL4eHhYZ9DWyaTQa1Wl/qgk5ycfMPRy3///fdSH5KtViuWLl2KevXq3fFURiUt1jdqJQoJCcHDDz+MWbNm4euvv0a/fv1Qt27dO9pmeSq7n6XQo0cPbNmyxf7BtcSCBQug1+vtH8K7dOmCY8eO4cSJE6XWq+wIwBqNBl26dMH06dMBAIcPH7YvB258zK7Vs2dPKBQKfPXVV5Xa9s3MmDEDPj4+mDhxImw2Gxo1aoQGDRrg6NGj5fZGiYuLg4eHB4DifbNly5ZSXb1tNhuWL19e4e0/8MADOHbsGOrVq1futq4tuku4ubmhT58+GD9+PEwmk70l9lpBQUEYPXo0HnnkEZw+ffqGMyl0794dQNnn6v79+3Hy5Mkqe66q1WpER0fj/Pnzt1y3opnat28PjUaDpUuXllpv7969Zbq7l5yacf328/LykJ+fX26OktNjSo5BgwYNEBERgeXLl0MIUWrdtLQ0bN26tdwu1KGhoejduzeWLFmCL7/8EnK5HKNGjbrh479w4UKFp0OsyHv0jXTq1Ane3t44ceLEDZ/rarXaPnPCypUrS7UQ5+XlYe3atbfM2KdPH5w5c8Z+2k15bvQ+4Obmhvbt22PlypWlrrPZbPjhhx8QHh5e5pSNm6nMviUix2NLNxFVyC+//ILExERMnz69zPQ0QPGcwF988QXmzJlTap7WlJQU7N27t8z6np6eDv1AcN9996FXr1546623kJubi06dOuGvv/7CpEmTEBsbi5EjR5ZaPzQ0FP3798fkyZMREhKCH374AZs2bcL06dPt5xqXTIM0btw4DB48GFevXsXUqVMREhJSbrdWf39/dO/eHRMmTICbmxtmzZqFU6dOVck0Lh4eHoiIiMCaNWvQo0cP+Pr6wt/fv1SL10svvWSfP728qXyqQmX3s6OU9xwDiovFSZMm2c8nnjhxInx9fbFo0SKsX78eM2bMgJeXFwDg5Zdfxty5c9GnTx+89957CAoKwuLFi+0tpzebUmnixImIj49Hjx49EB4ejuzsbHz++edQqVTo0qULgOK5vXU6HRYtWoSYmBi4u7sjNDS03GIzMjIS77zzDqZOnYqCggI88sgj8PLywokTJ5Cenl5qnuWK8vHxwdtvv40333wTixcvxqOPPopvvvkGffr0Qa9evTB69GiEhYUhMzMTJ0+exKFDh+xF9fjx47F27Vr06NED48ePh06nw9dffw2DwXDLfVPivffew6ZNm9CxY0e8+OKLaNSoEQoLC3Hp0iVs2LABX3/9NcLDw/Hkk09Cp9OhU6dOCAkJQXJyMj766CN4eXnZe060b98eDzzwAFq0aAEfHx+cPHkSCxcuRIcOHW44L3GjRo3w1FNPYebMmZDL5ejTpw8uXbqECRMmoE6dOnjllVcqvU9vpGvXrhX6ArKimXx9ffHqq6/io48+go+PDx566CHEx8djypQpCAkJKbX/w8PDER0djb179+LFF1+0Lz99+jR69eqFYcOGoUuXLggJCUFWVhbWr1+Pb7/9Fl27dkXHjh3t63/yyScYMmSIfR7v4OBgnD17FtOmTYNarcaECRPKfUxjx47F+vXrMXv2bPTq1euGPTUyMjJw9uxZvPDCCxXapxV5j74Rd3d3zJw5E6NGjUJmZiYGDx6MwMBApKWl4ejRo0hLS7N/wTV16lT07t0b9913H1577TVYrVZMnz4dbm5ut+w98PLLL2Pp0qUYMGAA/vOf/6Bdu3YoKCjA9u3b8cADD6Bbt243fe/+6KOPcN9996Fbt254/fXXoVarMWvWLBw7dgxLliyp8HzmNpsN+/btw9ixYyu0PhFVA+nGcCOimuTBBx8UarW6zHQw1xo2bJhQKpX2Ebtxk9HLy5vS6loVGZVaiH9HA752VOUSBQUF4q233hIRERFCpVKJkJAQ8eyzz5aZuiYiIkL07dtXrFixQjRt2lSo1WoRGRlZahTeEtOmTRORkZFCo9GImJgY8d1335U72jgA8dxzz4lZs2aJevXqCZVKJRo3biwWLVpU7uOs7OjlQhRPbRMbGys0Go0AUGoU3RKRkZEiJiamnD1XvluNXl4V+/l6JfuqIjmuV7L/bvRTsl///vtv0a9fP+Hl5SXUarVo2bJluaOIHzt2TNx7771Cq9UKX19fMXbsWDF//nwBwD5ivhBlRy9ft26d6NOnjwgLCxNqtVoEBgaK+++/X+zcubPU/S9ZskQ0btxYqFSqUiMYl3fMhSieLaBt27ZCq9UKd3d3ERsbW27ua91oyjAhio9V3bp1RYMGDezTCx09elQMGTJEBAYGCpVKJYKDg0X37t3tMxSU2Llzp2jfvr3QaDQiODhYvPHGG2L69OllRm2/0XEWonjk5hdffFFERUUJlUolfH19RZs2bcT48eNFfn6+EEKI+fPni27duomgoCChVqtFaGioGDJkiPjrr7/s9/Of//xHxMXFCR8fH6HRaER0dLR45ZVXRHp6un2d8vap1WoV06dPFw0bNhQqlUr4+/uLRx991D51W4kuXbqIpk2blsl//XG/kd9//10AEPv27Su1/E4y2Ww28f7774vw8HChVqtFixYtxLp160TLli3FQw89VGrdCRMmCB8fn1JT9mVlZYn3339fdO/e3f48dXNzE61atRLvv/9+udPzbd68WfTs2VN4e3sLpVIpQkJCxKOPPirOnj17w8duMplEUFCQACCWLVt2w/XmzJkjVCpVqRkebqSi79G3+r+xfft20bdvX+Hr6ytUKpUICwsTffv2LbP+zz//LFq0aCHUarWoW7eumDZtWrnH7vrRy4Uo3s8vvfSSqFu3rlCpVCIwMFD07dtXnDp1yr7Ozd67d+7cKbp37y7c3NyETqcTd911l1i7dm2pbdzsNS7Ev8+/gwcPlns9EVU/mRDX9RsiIqJa4a+//kLLli3x5Zdf2gfGosp76qmnsGTJEmRkZNgHeKNiPXv2xKVLl3DmzBmpozidFi1aoFOnTlV6isD1Ll68iMaNG2PSpEl455137MsTExMRFRWFBQsW3PGMBY5yzz33oG7duli0aNEt142MjESzZs2wbt26akhW840cORIXLlzAH3/8IXUUIvoHu5cTEdUy58+fx+XLl/HOO+8gJCSkzHREdGPvvfceQkNDER0djfz8fKxbtw6zZ8/Gu+++6/IF96uvvorY2FjUqVMHmZmZWLRoETZt2oQ5c+ZIHc0pzZgxAw899BDGjx9/x2M4AMDRo0exZMkSdOzYEZ6enjh9+jRmzJgBT0/PMt2IQ0ND8fLLL+ODDz7Aww8/XKHu/9Vpx44d2L9/P+bPny91lFrn/PnzWLp06U3PKyei6seim4iolpk6dSoWLlyImJgYLF++/JbnO9K/VCoVPv74Y8THx8NisaBBgwb49NNP8dJLL0kdTXJWqxUTJ05EcnIyZDIZmjRpgoULF+LRRx+VOppT6t27Nz7++GNcvHixSopuNzc3HDhwAHPmzEF2dja8vLzQtWtXfPDBB+WOJP7uu+9Cr9cjISHhtkfAd5SMjAwsWLAA0dHRUkepda5cuYIvvvgCd999t9RRiOga7F5ORERERERE5CDO1d+IiIiIiIiIqBZh0U1ERERERETkICy6iYiIiIiIiBzEJQdSs9lsSExMhIeHB2QymdRxiIiIiIiISGJCCOTl5SE0NLRKZ35wyaI7MTHR6UbyJCIiIiIiIuldvXq1SmaeKOGSRbeHhweA4p3p6ekpcZqKMZvN2LhxI3r27AmVSiV1HHIQHmfXwWPtGnicXQePtWvgcXYdPNau4frjnJubizp16tjrxarikkV3SZdyT0/PGlV06/V6eHp68oVfi/E4uw4ea9fA4+w6eKxdA4+z6+Cxdg03Os5VfQoyB1IjIiIiIiIichAW3UREREREREQOwqKbiIiIiIiIyEFYdBMRERERERE5CItuIiIiIiIiIgdh0U1ERERERETkICy6iYiIiIiIiByERTcRERERERGRg7DoJiIiIiIiInIQFt1EREREREREDsKim4iIiIiIiMhBWHQTEREREREROQiLbiIiIiIiIiIHYdFNRERERERE5CAsuomIiIiIiIgcRPKie8eOHejXrx9CQ0Mhk8mwevXqW95m+/btaNOmDbRaLaKjo/H11187PigREVFVstmAM2eA/fuLf9tsUieiW7n2mJ06VfxTXcePzxciqslc/D1MKXUAg8GAli1bYsyYMRg0aNAt17948SLuv/9+PPnkk/jhhx/wxx9/YNy4cQgICKjQ7YmIiCR3+DAwfz5w8iRQWAhotUBMDDBqFBAbK3U6Ks+1xyw9vfgHAPz9i3+aNQM6d3b8tvl8IaKahu9h0hfdffr0QZ8+fSq8/tdff426devis88+AwDExMTgwIED+OSTT1h0ExGR8zt8GHjvveKiLTwccHMDDAbg4EHg8mVg4kSX+RBSY1x7zNzcgIwMwGgsvi49HQgIAI4cKS66//oLaNPGMdvm84WIahq+hwFwgqK7svbs2YOePXuWWtarVy/MmTMHZrMZKpVKomRERES3YLMVf9ufnl78Lb9MVrzc07P48smTwIIFQMuWgFzyM8AIKH3MGjcG9u4FioqKW7eFALKzgcREoFOn4vWXLCn+AFkVx4/PFyL6h81mg8VmgdVmhcVmgcVa/LdN2P79sdlKX67ostu93XXLhBAQELDZbBAQEDYrxJIlsJkvQTQOgZAlQAclHvVs4HLvYTWu6E5OTkZQUFCpZUFBQbBYLEhPT0dISEiZ2xQVFaGoqMh+OTc3FwBgNpthNpsdG7iKlOSsKXnp9vA4uw4ea9dQ5jifO1f8ExkJlPclcWQkcPYscPo0UL9+teWkm7j2mBUUFHeN9PX99/j5+gIFBTD/0/JtPn++6o4fny9Op7Lv3SVFSHl/A7Bf/veXKHXbGy0rs/yav8u7fP1tb7berW5TmdvfyX07itlqRqG5EAWmAhSYC4r/NhegyFIEk8UEk9UEs8WMQlMhTiecxvmt52EVVpgsJpitZvv1Jus/l6+5zbXLzFYzLDZL2WL5mmUlhXOpZdesa7VZ7X9Ltb+qRAgAnAMEEAQdhqpiipc7wXvY9a9pR30ukwknOoIymQyrVq3Cgw8+eMN1GjZsiDFjxuDtt9+2L/vjjz9w9913IykpCcHBwWVuM3nyZEyZMqXM8sWLF0Ov11dJdiIiIiIiunMWmwUFloJ/f6zFvwsthWWWlSwvtBbCZDXBZCsugk1WE4qsRfbLJX/bhGsN4OVsvNRemN9zvtQxbshoNGL48OHIycmBp6dnld1vjWvpDg4ORnJycqllqampUCqV8PPzK/c2b7/9Nl599VX75dzcXNSpUwc9e/as0p3pSGazGZs2bcJ9993HLvS1GI+z6+Cxdg1ljvO5c8AbbwA+PsVdhK+XmwtkZQEff8yWS2dx7TETAvjzT0Ct/rfl2WQCzGaYO3bEpoEDcd+8eVB9+GHVtXS72PPl2pY9i/in1U9Y7ctL/jbbzDBbzfbfJevZu7uiuKvrtd1ebbAB4poWVlnJLxmEEJDJZMUttyVXl3TnByCXyYuvtwrYztugrK+EXCEvtY4MMshkMsj+ueOS68q7fO3trl3n+u3edBlkN13n+utvdD83Ut7ty12vnPsUQqDAXIBsQzZyC3ORW5CLvMI85BXmIbcgF/mF+cgtzEVewT/LCouvzy/Mh9FkhNlae3uBKeQKKOVKKOQKKOQKyGXyssvkcvvfSrkSClnZZSW3K3luXvu7vGW3e11F1peh+DJkKP13VjZkW7ZAptVBplEjR2ZCpM0D92/cWLwznOA97Pr/0yU9oqtajSu6O3TogLVr15ZatnHjRsTFxd3wg6tGo4FGoymzXKVS1bgPuzUxM1Uej7Pr4LF2Dfbj3KhR8QeLgwdLn6MLFBd0ly4BcXHF69Xy89tqjGuPWePGxaPupqcD3t7/ntMdGAj803NOVa8eVFV1/GrR88XeLdf2bxdcs624G26htbj1sshSBLPVbC+sbcIGi/inW61AmQK3pOAo9SOXQ6lU2q8vKQDsxUI5BW9l2Kw2JCIRgZ6BkCuce59XNavNisz8TGTkZyDLmIVMQyay8v/5bfjntzHL/neRuejWd+pASoUSGqUGWpUWGqUGGpXm38v//K1RaaBV/ntZrVRDqVBCpVBBKVPCmGSEf6Q/1Co1VApV8XKFEmpF6cslf5e6/T9F9PXFtEsRNuB4LnDyFBBVB/FyA5rYfKCyWJzuPazk/7SjPpNJXnTn5+fj3Llz9ssXL17EkSNH4Ovri7p16+Ltt99GQkICFixYAAB45pln8MUXX+DVV1/Fk08+iT179mDOnDlYsmSJVA+BiIioYuTy4ilSLl8uHkAmPLy4WDMagfj44sG5HntM8g8fdI1rj9mpU0BYWHHrTMmUYXo9EBpaPO9s797AI49U3fGrQc8Xq82KQkshiqxFxefGWk0osBTAYDLAYDaUapW22qzFrcz/FNIlLXklxYlKroJC9W9rnlwm/eOr7UwWE1JzU8v+5BX/TstNQ3p+evGxcwC5TA4PrQfcte5w07hBp9bBTe0GvUYPvVpvX1byt16jL3W9Xq2HTq0rVVwrFXdW5tisNiQeSkRo61CX+4KlysjkwAMPAEnJwMVLQKg7oPAsfg91svcwR5O86D5w4AC6detmv1zSDXzUqFGYN28ekpKScOXKFfv1UVFR2LBhA1555RV8+eWXCA0Nxf/+9z9OF0ZERDVDbGzxFCklc5YmJha3nsbFFX/4cIGpU2qc64+Zv3/pebqF+Pe4tWjh2G1L/Hyx2CwoMP97Hm1+UT5yTbkwmo3282htwmYvqNUKNVTy4lZArVILpVwJpVzyj58ux2azIT0/HYlZiYjPikdiViISshKQmF38Oy0vrUoG6pLL5PDSe8HHzQe+br7w0fvAU+cJT50nPLQexb91HvDU/vvbU+cJvVrveq3ArqJRY+DJJ4B164CEU0B2ISB8Xe5/nuTvel27dr3pi3zevHlllnXp0gWHDh1yYCoiIiIHio0tniLl3DkgJwfw8iruRswPnc7r+mPm4VG8PC+v+PhFRAC//lo9266m54vZaobBbIDRbITBZEBWYRbyivKKB6SymAAUF9YahQZqhRoeao/iVmq5wqG56MYKTAW4nHEZl9Iv4XJa8e9L6ZdwJeMKiiy3393b180XgZ6BCPAMgL+7f3FB7eYDX3fff/9284WnzpPHn8pq1Bho2BA4vR+QBQFhzV3uf57kRTcREZFLksuLP4RQzXGzY+bo6f8c/HwRQqDAUoB8Uz7yTfnIMGYgtygXBeYCWGwWe6u1VqmFt8Ybar36js6NpjtjsVpwMe0izqScwdnkszibchaX0i8hJSel0vflrfdGqE8oQrxCEOQVhECPQAR6BRb/9gyEv4c/1Eq1Ax4FuRSZHAgNAwKaAH4NpE5T7Vh0ExEREbmgAnMB8kx5yCnMQZoxDXlFeSgwFwAyQC1XQ6fSwV/vD5WCgz1KKa8gD6eTT+N00mmcTTmLM8lncCHtAixWS4Vur5ArEO4bjrq+dRHqE4own7Di397Fv900bg5+BETEopuIiIjIBdiEDblFufYiO7MgE0aTEQCgU+mgV+nhp/NjC7aELFYLzqeex9/xf+N4/HH8Hf83LqVfqtBtPbQeiAqIQoR/BCL8IhAZEIlI/0iE+4Tf8aBiRHRn+AokIiIiqqWsNityinKQacxEUn4ScotyUWQtgkahgbvaHT6ePhwdXEKFpkIcvXoUhy4dwuHLh3Ei8QQKzYU3vY1cJkeEfwQaBjdEg6AGaBjcEA2DG8LPnV+YEDkrFt1EREREtYgQAjlFOUg3pCMxPxE5hTmw2CxwU7nBR+sDjVIjdUSXVWAqwNErR3Hw0kEcvHQQJxJP3LSbuEKuQKPgRmga1hSNQhqhYXBDRAdGQ6vSVmNqIrpTLLqJiIiIaoECcwHSjelIyEtAhjEDJqsJbio3BOgDeF62RIQQuJh2EbvP7sbuc7tx+PJhmK03HnQv1DsUTcObonl4czQLb4ZGwY2gUfFLEqKajkU3ERERUQ0lhEBWYRaS8pKQmJeIfFM+NAoNvLXe0CrZGiqFAlMB/jz/J/44+wf2nNuD5JzkG65b168u2kS2QZvINmgd2RqBnoHVmJSIqguLbiIiIqIaxmKzIM2Qhqu5V5GanwqLzQJvrTfqeNbheb0SyC/Mx64zu/D7id+x+9xuFJnLnxM7xDsEd9W7C3FRcWgT2Qb+Hv7VnJSIpMCim4iIiKiGMFvNSM5PxuXsy8goyIBSroSvzpfnaUsgx5iDHad34PcTv+PP83+W221cpVAhNiIWHRt0RKcGnRDpH8kvRYhcEItuIiIiIidntpqRlJ+Ei1kXkVmQCZ1Sh2D3YCjl/ChXnUwWE/Yk7cGepXuw+9zucgdB83HzQdfGXdG5UWfERcVBp9ZJkJSInAnfqYmIiIiclNVmRXJ+Mi5kXUCaMQ1uKjeEeYRBIVdIHc1lCCFwLP4Y1h9dj41/b0RuYW6ZdQI8AtAtphu6N+mO2IhYHh8iKoVFNxEREZGTEUIgzZiGC1kXkJyfDK1Ci3CPcBZz1Si/MB8bjm7AyoMrcS7lXJnr/T380bNZT/Ro0gPNw5tDLud850RUPhbdRERERE4krygP57PO42rOVcggQ4h7CLuRV6OTiSexYv8K/Pb3byg0F5a6TqPU4K6guzCo6yC0r9+eX4IQUYXwHZyIiIjICZitZlzNuYpzWedgNBsRoA/gtF/VxGqzYufpnfhh9w84cuVImetb1GmBB9s8iG6NuiHneA5C64WyZZuIKoxFNxEREZHEMowZOJN5Bsl5yfDUeKKOZx2pI7mEAlMB1h5eiyV7l+Bq5tVS17lp3HB/i/sxMG4gGgQ3AADYrDbkIEeKqERUg7HoJiIiIpKI2WrGxeyLOJd5DlabFaEeoexKXg3yC/Px458/YsmeJcgpKF1ERwVE4ZG7HkHv5r2h1+glSkhEtQnf1YmIiIgkkF2YjVPpp5CYlwg/nR/c1e5SR6r1cgtysWTvEvy490fkFeaVuq5ddDuM6DgCHep1YNdxIqpSLLqJiIiIqpFN2HA15ypOZZxCoaUQYR5hbN12sPzCfPyw+wcs2bsEhiKDfblCrkDPZj3xWKfH7F3IiYiqGt/hiYiIiKpJoaUQZzLO4GLWRbir3RHuES51pFrNZDHhp/0/YfaO2cgx/tuNXCFXoG/LvhhzzxjU8eP580TkWCy6iYiIiKpBdmE2TqSdQHJ+MoLcgjgyuQPZbDZsPLYRX235CglZCfblSoUS/Vr1w+h7RiPMJ0zChETkSlh0ExERETlYUl4Sjqceh8FiQLhHOOd3dqC/rv6Fjzd8jJOJJ0st79OiD57p/gyLbSKqdiy6iYiIiBzEJmy4mHURp9JPQSlXsju5A2XmZ2Lm5plYe3htqeV31bsLz9/3PBqHNJYoGRG5OhbdRERERA5gsVlwOv00zmaehbfGGx4aD6kj1UoWqwUr9q/A11u/Rn5hvn15g6AGeKnXS7ir3l0SpiMiYtFNREREVOWKLEU4kXYCF7Mv8vxtBzqZeBJT10zFmeQz9mXuWnc82/1ZDIobBKWCH3WJSHp8JyIiIiKqQkazEX+n/I2EvASEuodCpVBJHanWKTIX4btt32Hh7oWw2qz25f1j++P5e5+Hr7uvhOmIiEpj0U1ERERURQwmA05mnkRSfhLn33aQo1eO4r017+Fy+mX7sgZBDfBOv3fQvE5zCZMREZWP/wmIiIiIqshfKX8hsyiTI5Q7QJG5CF9s/gI//vkjhBAAiqcAe6LLExh992h2JScip8V3JyIiIqI7lGfKAwBkFWQhzDsMcplc4kS1y/nU8xi/YjzOpZyzL2sa1hQTH5yIeoH1JExGRHRrLLqJiIiI7kBeUR6OpRwDAAR7BLPgrkJCCKzYvwKf/fYZiixFAAC1Uo1nuz+L4R2GszcBEdUILLqJiIiIblO+KR9Hk48iqyALAFhwV6EsQxbeW/Medp7eaV9WL7AePhj8AeoH1ZcwGRFR5bDoJiIiIroNBeYC/J3yN9KMaQj1CEUykqWOVGv8dfUvvLX0LaTlpdmXDW0/FC/c9wK0Kk6/RkQ1C4tuIiIiokoqshTh79S/kZyfjDCPMMiETOpItUJJd/L/+/X/YLFaAADeem9Mfmgy7m54t8TpiIhuD4tuIiIiokqw2Cw4kXYCV3Ov2kcpt1ltUseq8QrNhfho7UdYf3S9fVnriNb48OEP4e/hL2EyIqI7w6KbiIiIqIJswobT6adxMfsiQt1DOQ93FUnISsAbP76BM8ln7MtGdBiBF+57gVOBEVGNx3cxIiIiogoQQuBC5gWczTyLILcgqBVqqSPVCkcuH8FrP76GHGMOAECn1mHCgAno2aynxMmIiKoGi24iIiKiCkjMS8TJ9JPw0fpAq+RgXlXh179/xZRVU2C2mgEAdf3qYsbQGRydnIhqFRbdRERERLeQYczA8bTj0Cg0cFe7Sx2nxhNCYM72Ofh669f2Ze2i22H6kOnw0HlImIyIqOqx6CYiIiK6CYPJgGOpx1BkKUKoR6jUcWo8s8WM939+v9SAaQ+2fhD/eeA/PH+biGolvrMRERER3YDZasbJtJPILMhEHc86Usep8QxFBry+5HXsv7jfvuyF+17AY50eg0zGadeIqHZi0U1ERERUDiEEzmedx5XcK8VzcbMovCPZxmy8+MOLOJFwAgCgUWowZeAU3Nv0XomTERE5FotuIiIionIk5iXibMZZBOgDODXYHUrLTcNzC57DhbQLAABPnSc+H/E5mtdpLnEyIiLH438QIiIiouvkFObgZNpJaBQa6FV6qePUaPGZ8XhuwXNIyEoAAPh7+OOLkV9whHIichlyqQMQEREROROT1YST6SdhMBvgp/eTOk6Ndj71PJ6Y+4S94A7zCcPsx2ez4CYil8KWbiIiIqJ/CCFwLuMcEvMSEeYRJnWcGu1cyjk8M+8ZZBuzAQDRAdH48rEvEeAZIG0wIqJqxqKbiIiI6B/J+ck4n3We53HfoQupF/Ds/GftBXeT0Cb438j/wVvvLWkuIiIp8L8JEREREYrn4z6VfgoquYrncd+BS+mX8Oz8Z5FlyAIANAlrglmPzYK71l3iZERE0uA53UREROTybMKGMxlnkF2YDX+9v9RxaqwrGVfwzLxnkJGfAQBoHNIYX478kgU3Ebk0Ft1ERETk8uJz43E55zKC3YM5H/dtis+MxzPznkF6XjoAoGFwQ3z52Jfw0HlInIyISFosuomIiMil5Rbl4nT6abir3KFWqKWOUyOl56Vj3PxxSM1NBQDUD6qPWY/NgpfeS+JkRETSY9FNRERELstqs+JsxlkYzUb46HykjlMj5Rfm48UfXkRidiKA4lHKZz02C95u3tIGIyJyEiy6iYiIyGXF58bjas5VBLkFSR2lRioyF+G1Ja/hTPIZAECIdwi+fOxL+Lr7SpyMiMh5sOgmIiIil5RXlIczGWfgrnaHSqGSOk6NY7VZMWHlBBy8dBAA4KX3wsyRMzkPNxHRdVh0ExERkcuxCRvOZZ5Dvimf3cpvgxACH2/4GFtObAEAaFVafD7ic0T6R0objIjICbHoJiIiIpeTmJeIKzlX2K38Ns3dMRcr9q8AACjkCswYOgPNwptJnIqIyDmx6CYiIiKXUmAuwNmMs9AqtdAoNVLHqXE2HduEr7Z8Zb888cGJ6Nigo4SJiIicG4tuIiIicikXsi4gqzALfjo/qaPUOMcTjmPyqsn2y8/f+zz6tuwrXSAiohqARTcRERG5jHRjOi5lX0KAPgAymUzqODVKam4qXlvyGoosRQCAB1o9gFF3j5I4FRGR82PRTURERC7BYrPgXOY52IQNepVe6jg1SoGpAK8ufhXpeekAgFZ1W+Gdfu/wiwsiogpg0U1EREQuISE3AUl5SQh0C5Q6So1is9kwadUknEo6BQAI9Q7Fx8M+hlqpljgZEVHNwKKbiIiIaj2DyYBzmefgofaAUq6UOk6N8t227+xTg7lp3PDfEf+FjxunWSMiqigW3URERFTrXcq+hJyiHHhrvaWOUqPsOrML323/DgAgl8nx4cMfol5gPYlTERHVLCy6iYiIqFbLMGbgUvYl+Ov8eQ5yJSRkJWDCTxPsl5+/93l0atBJwkRERDUTi24iIiKqtWzChgtZF2AVVrip3aSOU2MUmgvx5tI3kVeYBwDoFtMNIzuNlDgVEVHNxKKbiIiIaq3k/GQk5iUiQB8gdZQa5eMNH+N00mkAQF2/upj04CT2EiAiuk0suomIiKhWMllNOJ95HmqFGmoFR9quqNUHV2PNoTUAAK1KixlDZ8Bd6y5xKiKimotFNxEREdVKiXmJSDOmwU/nJ3WUGuNU0inM2DDDfnl8//GoH1RfwkRERDUfi24iIiKqdQrMBTifeR6eak8o5Aqp49QIhiID3l72NkwWEwDg4bYPo0+LPhKnIiKq+Vh0ExERUa1zJecKpwirpE82fIKrmVcBAE3CmuCV3q9InIiIqHZg0U1ERES1Sm5RLi7lXIKv1peDf1XQb3//hrVH1gIA3DRu+HDwh1AreR48EVFVYNFNREREtcrl7Mswmozw0HhIHaVGSMxKxIdrP7RffqvvWwj3DZcwERFR7cKim4iIiGqNrIIsXM29ysHTKshiteDdn96FocgAAOjTog/ub3m/xKmIiGoXFt1ERERUKwghcDnnMsxWM9zUblLHqRHmbJ+Dv67+BQAI8wnDW33fkjgREVHtw6KbiIiIaoXMgkwk5CbAX+8vdZQa4fDlw5izYw4AQCFX4P1B73M+biIiB2DRTURERDWeEAKXsi/BKqzQKrVSx3F6xiIjJq+aDJuwAQCe6voUmtdpLnEqIqLaiUU3ERER1XjpxnQk5iXCX8dW7oqYuWkmErISAACxEbEYfc9oaQMREdViLLqJiIioRrMJGy5lXwIAaJQaacPUAPsu7MPy/csBAFqVFpMenASFXCFxKiKi2otFNxEREdVo6cZ0JOcn81zuCsgvzMd7q9+zX37xvhc5PRgRkYOx6CYiIqIayyZsuJx9GQCgVqglTuP8Pt/4OZJzkgEAcVFxGNx2sMSJiIhqPxbdREREVGOxlbvi9p7bi1UHVwEAdGodJgyYALmcHwWJiBzNKd5pZ82ahaioKGi1WrRp0wY7d+686fqLFi1Cy5YtodfrERISgjFjxiAjI6Oa0hIREZEzYCt3xeUX5mPqmqn2yy/1fAlhPmESJiIich2SF91Lly7Fyy+/jPHjx+Pw4cO455570KdPH1y5cqXc9Xft2oXHHnsMY8eOxfHjx7F8+XLs378fTzzxRDUnJyIiIillGDOQlJ/EVu4K+Hzj50jJTQEAtItuh0FxgyRORETkOiQvuj/99FOMHTsWTzzxBGJiYvDZZ5+hTp06+Oqrr8pdf+/evYiMjMSLL76IqKgo3H333Xj66adx4MCBak5OREREUhFC4ErOFcggYyv3LRy+fNjerVyv1mPCgAmQyWQSpyIich2SFt0mkwkHDx5Ez549Sy3v2bMndu/eXe5tOnbsiPj4eGzYsAFCCKSkpGDFihXo27dvdUQmIiIiJ5BRkIGkvCT46fykjuLUTBYTPlz7of3yuB7jEOIdImEiIiLXo5Ry4+np6bBarQgKCiq1PCgoCMnJyeXepmPHjli0aBGGDh2KwsJCWCwW9O/fHzNnzrzhdoqKilBUVGS/nJubCwAwm80wm81V8EgcryRnTclLt4fH2XXwWLsGHmfHEELgcuZlWK1WqGQq2Kw2qSPZMzhDlmvN3zkfF9MuAgCahjbFoDaDnC5jTeKsx5mqHo+1A1gBq8XqVP8Tr/8/7ahsMiGEcMg9V0BiYiLCwsKwe/dudOjQwb78gw8+wMKFC3Hq1Kkytzlx4gTuvfdevPLKK+jVqxeSkpLwxhtvoG3btpgzZ06525k8eTKmTJlSZvnixYuh1+ur7gEREREROYmE/AS8vONlmG1myGVy/N89/4cozyipYxEROS2j0Yjhw4cjJycHnp6eVXa/khbdJpMJer0ey5cvx0MPPWRf/tJLL+HIkSPYvn17mduMHDkShYWFWL58uX3Zrl27cM899yAxMREhIWW7TJXX0l2nTh2kp6dX6c50JLPZjE2bNuG+++6DSqWSOg45CI+z6+Cxdg08zo5xPPU4LmdfRqhnqNRR7GxWG5KPJiO4ZTDkCsmHzIEQAuMWjsPBSwcBAI92eBQv3veixKlqPmc7zuQ4PNZVLzE3EY38G6Gebz2po9hd/386NzcX/v7+VV50S9q9XK1Wo02bNti0aVOponvTpk0YMGBAubcxGo1QKkvHVigUAIr/wZRHo9FAo9GUWa5SqWrch6CamJkqj8fZdfBYuwYe56qTU5iDJGMSfNx8nPKDsFwhd4pcaw+vtRfcod6heLr7006Rq7ZwluNMjsdjXYUUgEKpcMr/hyX/px2VTfJn0KuvvorZs2dj7ty5OHnyJF555RVcuXIFzzzzDADg7bffxmOPPWZfv1+/fli5ciW++uorXLhwAX/88QdefPFFtGvXDqGhzvONNxEREVW9hNwEFFoK4aZ2kzqK08oyZOGz3z6zX37rgbegU+ukC0RE5OIkbekGgKFDhyIjIwPvvfcekpKS0KxZM2zYsAEREREAgKSkpFJzdo8ePRp5eXn44osv8Nprr8Hb2xvdu3fH9OnTpXoIREREVA3yTfm4mncVPlofqaM4tf9t+h9yCnIAAPc1uw+dGnSSOBERkWuTvOgGgHHjxmHcuHHlXjdv3rwyy1544QW88MILDk5FREREziQ5LxkGkwH+Xv5SR3Fax+KPYe3htQAAd607Xuv9msSJiIjIKYpuIiIiopsptBTics5leGm8pI7itGw2G2asn2G//HTXp+HvwS8oiEhaVpsVGQUZAACVwvnO564OLLqJiIjI6aXkpyCnKAd1PetKHcVp/Xz4Z5xIPAEAqBdYDw+3e1jiRETkyoQQyCnKQU5RDgL0AYj2iUawe7DUsSTBopuIiIicmtlqxqXsS3BTuUEmk0kdxynlFuTii81f2C+/cf8bUCr4MY+IpJFvykdmQSY8NZ5oFdQK4V7hUCvUUseSDN+NiYiIyKmlGdOQWZCJMI8wqaM4rW+2foNsYzYA4L6m9yEuKk7aQETkkoosRUgzpkGtUKORXyNE+kRCr9JLHUtyLLqJiIjIadmEDVdyrkCtUEMhV0gdxymdSzmHFftXAAC0Ki1e6vWSxImIyNVYbVakGdNgFVbU8aqDKO8o+Og400QJFt1ERETktDKMGUg1pCJQHyh1FKckhMCMDTNgtVkBAGPuGYNgL9c8Z5KIqp8QAtmF2cgz5SHQLRD1fOsh0C0Qcplc6mhOhUU3EREROa343HgArjvi7a1sPr4Zhy4dAgCE+4bj0Y6PSpyIiFyFwWRAekE6vDReiA2JRZhHGN+rb4BFNxERETmlnMIcJOUnwVfrK3UUp1RkLsLMTTPtl1/r/Ro0Ko2EiYjIFZitZqQaU6GQKYrP2/aOhJvaTepYTo1FNxERETmlpPwkFFmKoHPTSR3FKS3btwyJ2YkAgPb12uPuhndLnIiIajObsCGzIBMFlgKEeYQh2icafno/qWPVCCy6iYiIyOkUmAtwNecqvLXeUkdxStmGbMzZMQcAIJPJ8HLPlzmdGhE5TL4pHxkFGfDV+qJpYFOEuIdwcMtKYNFNRERETifFkII8Ux7qetaVOopTmr19NvIL8wEA/Vr1Q4PgBhInIqLayGQ1IdWQCo1SgyYBTRDpHQmtUit1rBqHRTcRERE5FYvNgsvZl+GmcmPrbTkup1/G8v3LARRPEfZs92clTkREtU1JV/JCSyHCPcMR7RPNKcDuAItuIiIicipphjRkF2Yj2J1TX5Vn5qaZ9inCRnYaiQDPAIkTEVFtUtKV3E/nh2aBzRDiEcIpwO4Qi24iIiJyGkIIxOfGQyFTQCnnx5TrHbp0CNtObQMA+Hv447FOj0kbiIhqjZJRyVVyFbuSVzH+NyMiIiKnkVWYhVRDKrsxlsNms+Gz3z6zX36m2zPQqTmyOxHdGSEEMgsyYTAbEOYZhvq+9eGr41SNVYlFNxERETmNpLwkmK1mtq6UY+PxjTiReAIAUD+oPvrF9pM4ERHVdAXmAqQaU+Gl8UKb0DYI8wjjqOQOwKKbiIiInILRbERCXgKnCSuHxWrB11u+tl9+uefL/GBMRLfNarMizZgGIQQa+DZAPd960Kv0UseqtVh0ExERkVNINaQi35TPacLKsfrQasRnxgMA2kW3w13175I4ERHVVLlFucgqzEKQWxAa+DVAgD6AM0U4GItuIiIikhynCbuxQlMhZm+fbb88rsc4CdMQUU1ltpqRYkiBVqlFi8AWqOtdF2qFWupYLoFFNxEREUku3ZjOacJuYOm+pUjPSwcAdIvphmbhzSROREQ1iRACWYVZxQOleYShgV8DnsZTzVh0ExERkaSEEEjITYBcJuc0YdfJK8jD/F3zAQBymRzPdn9W4kREVJMUWgqRakiFp8YTrUNac6A0ifA/GxEREUkqtygXKYYU+Gg5Tdj1FvyxALkFuQCA+1vej+jAaIkTEVFNYBM2ZBgzYLKZEOUThfq+9eGudpc6lsti0U1ERESSSjGkoMhSBJ0b55y+VnpeOpbsXQIAUCqUeKrrUxInIqKawGg2Is2YBj+dH1r4tUCIewjHypAYi24iIiKSTJGlCFdzrsJT4yl1FKczd8dcFJoLAQCD4gYh1CdU4kRE5MxswoZUQyqEEGjk1wjRPtHQqfhlpjNg0U1ERESSSTOmIbcoF+Ge4VJHcSqJWYlYeXAlAECn1uHxzo9LnIiInFm+KR8ZBRkI1AeioX9DTgPmZFh0ExERkSRswob43HhoFBrIZXKp4ziV2dtnw2K1AAAeuesR+Ln7SZyIiJyR1WZFqiEVMpkMTQKaIMo7ChqlRupYdB0W3URERCSJ7MJspBvS4avzlTqKU4nPjMf6o+sBAO5ad4zsOFLiRETkjEpat4Pdg9HQryH89f5SR6IbYNFNREREkkjOS4bFZmGrzHXm7JgDq80KABjRYQQ8dB4SJyIiZ2K1WZFiSIFCrkDTgKaI8omCWqGWOhbdBItuIiIiqnYF5gIk5CXAS+sldRSnEp8Zjw1HNwAAPLQeeOSuRyRORETOpKR1O8Q9BA38GrB1u4Zg0U1ERETVLs2YhnxTPup41pE6ilOZs710K7e7lvPqEhFbt2s6Ft1ERERUrUoGUNMqtRxd9xpXM65iw1//tnIPu2uYxImIyBnw3O2aj0U3ERERVavMgkxkGDMQoA+QOopTKXUud0e2chO5upJ5twGgSUATRPtEs3W7hmLRTURERNUqOT8ZNmGDSqGSOorTuJJxxX4ut6fOE8Pas5WbyJUZzUakGdMQqA9EI/9GCHDjl5Q1GYtuIiIiqjZGsxFJeUnw0nAAtWvN2T4HNmEDwHO5iVyZEAJpxjRYbBY09muMer71OMNDLcCim4iIiKpNujEdeaY81PWsK3UUp3El4wp++esXAICXzgtD2w+VOBERSaHQUogUQwr8dH5o5N8IQW5BHPeilmDRTURERNWiZAA1vVLPD5LX+H7n9/ZW7uEdhrOVm8jFCCGQUZCBQksh6vvWRwPfBtCpdFLHoirEopuIiIiqRWZBJtKN6QjUB0odxWkkZSeVmpebrdxErsVkNSHFkAIPtQfiQuMQ6hHKLyVrIRbdREREVC1S8lMghOAAatdY8McC+4jlQ9sPZSs3kQvJLsxGXlEe6njVQSP/RnBX8/VfW7HoJiIiIocrMBcgMS+RA6hdIz0vHWsOrQEA6NQ6zstN5CIsNgtSDCnQKDRoGdwSdb3qQiFXSB2LHIhFNxERETlcujEd+aZ81PGsI3UUp7F4z2KYLCYAwKC4QfDWe0sbiIgczmAyIL0gHSEeIWjs1xg+Oh+pI1E1YNFNREREDlUygJpWqeW5iv/IMeZgxf4VAACVQoURHUZInIiIHEkIgVRDKmzChiYBTRDtEw21Qi11LKomLLqJiIjIobILs5FhzICf3k/qKE5j6Z9LYTQZAQD9Y/sjwDNA4kRE5ChFliKkGFLgq/NFY//GCHIPkjoSVTMW3URERORQKfkpsNgsbNX5h6HIgB///BEAoJAr8Njdj0mciIgcJbswG/mmfET5RKGhX0PoVXqpI5EEWHQTERGRwxRaCpGYlwhPjafUUZzGT/t/Qm5BLgCgd/PeCPMJkzgREVU1i82CZEMydEodYkNiEe4ZDrlMLnUskgiLbiIiInKYDGMGcotyEe4ZLnUUp1BoLsSiPYsAADKZDGPuGSNxIiKqatcOlhbjHwNvrbfUkUhiLLqJiIjIIYQQSMhLgFqhZgvPP9YdWYeM/AwAQPeY7ogMiJQ2EBFVGSEE0o3pMNvMHCyNSmHRTURERA6RU5SDNEMaW3n+YbVZ8cPuH+yXR98zWrowRFSlzFYzkg3J8NJ4oaV/SwS7B3O2BrJj0U1EREQOkWZIg8lqglaplTqKU9h2ahviM+MBAO2i2yEmNEbiRERUFfKK8pBVmIW6XnXRyL8R3NXuUkciJ8Oim4iIiKqc2WpGfG48PNQeUkdxCkIILPxjof3yyE4jJUxDRFXBJmxINaRCJpOheWBzRPlEQSFXSB2LnBCLbiIiIqpyGQXFA6iFuIdIHcUpHLlyBMfijwEAGgQ1wF317pI4ERHdiSJLEZINyfDT+aFJQBMEuAVIHYmcGItuIiIiqnJJeUmQy+Rs9fnHgl0L7H+P7DSS53oS1WA5hTnINeUi2icajfwaQafSSR2JnByLbiIiIqpSeUV5SDGkwEvjJXUUp3Ah9QJ2ntkJAAjyCkLPZj0lTkREt8MmbEjOT4ZKoULLoJaI8I7gzAxUISy6iYiIqEplFGTAaDYiQM/ulgBKjVj+yF2PQKngxy+imqakO3mgPhAxATHw0/tJHYlqEL7rExERUZWx2qy4mnMVbio3qaM4hbTcNGz4awMAwF3rjofaPCRxIiKqrOzCbOSZ8hDtE43G/o05IwNVGotuIiIiqjJZhVnILMxEsFuw1FGcwo9//giL1QIAGBw3GG4afhlBVJMk5SVBrVajVXAr1PWqy+7kdFtYdBMREVGVSclPAQSglPMjRn5hPn468BMAQKVQYdhdwyROREQVVWQpAgB4a73RNLgpu5PTHeF/RCIiIqoSBeYCJOYlcgC1f6w+uBr5hfkAgPtb3g9/D3+JExFRReQU5iDHmAM55IgNjoWH3kPqSFTDsX8EERERVYmMggzkm/LhrnaXOorkLFYLluxdYr/8aMdHJUxDRBUhhEByfjKKrEVoFtgMAKBV8fxtunMsuomIiOiOCSGQkJsAtULNOagB/HbsN6TkpgAA7ml0D6ICoiROREQ3Y7aacSX3CtzV7ogLjUOkT6TUkagWqXT38qKiIuzbtw+XLl2C0WhEQEAAYmNjERXFfyZERESuKqcoB+nGdHhrvaWOIjkhBBb+sdB+eVSnURKmIaJbyTflI6MgA5HekWjs3xh6lR5ms1nqWFSLVLjo3r17N2bOnInVq1fDZDLB29sbOp0OmZmZKCoqQnR0NJ566ik888wz8PDgeQ9ERESuJM2QBpPVxKl0AOy/uB/nUs4BAJqFN0PLui0lTkRE5RFCIN2YDquwonlgc0T5RHEQSHKICnUvHzBgAAYPHoywsDD89ttvyMvLQ0ZGBuLj42E0GnH27Fm8++67+P3339GwYUNs2rTJ0bmJiIjISZitZiTkJsBDzS/dAWDJnn/P5R7RYQS72xM5IYvNgoS8BKgUKrQJbYMGfg1YcJPDVOiZ1bNnTyxfvhxqtbrc66OjoxEdHY1Ro0bh+PHjSExMrNKQRERE5LwyCzKRU5SDEPcQqaNI7nLGZew8sxMAEOQVhG4x3SRORETXKzAXINWYijCPMDQJaAIPDb8wJMeqUNH93HPPVfgOmzZtiqZNm952ICIiIqpZkvKTIJfJoZArpI4iuWX7ltn/HtpuKJQKtpwROZOsgiwYzAY08muEBn4NoFaU36hIVJXu6D9BUVERNBpNVWUhIiKiGsZgMiDFkAJPjafUUSSXb8rH2iNrARRPM/RgmwelDUREdjZhQ3J+MtRKNWJDYlHHsw5P/aBqU6kpw3777TeMHj0a9erVg0qlgl6vh4eHB7p06YIPPviA3cqJiIhcTLoxHUaTEW4qN6mjSG7T1U0oNBcCAPrF9oOnjl9EEDkDk9WE+Nx4eGu90Ta0Lep61WXBTdWqQkX36tWr0ahRI4waNQpyuRxvvPEGVq5cid9++w1z5sxBly5dsHnzZkRHR+OZZ55BWlqao3MTERGRxGzChoS8BOiUOpf/AGuxWbD+4nr75WHth0mYhohK5JvykZSfhEjvSMSFxsFX5yt1JHJBFepe/uGHH+KTTz5B3759IZeXrdOHDBkCAEhISMDnn3+OBQsW4LXXXqvapERERORUsguzkWnMhJ/eT+ooktt2ahvSC9MBAHc3vBsR/hESJyKidGM6TFYTmgU2Qz2fehx3giRToaJ73759FbqzsLAwzJgx444CERERUc2QakiFRVg4EBGAH/f+aP/7kbsekTAJEVltViQZkuCmdEOb0DYI9QiVOhK5uAqf0/3yyy/j2LFjjsxCRERENYTJauLc3P84Fn8Mf8X/BQCoF1gP7aLbSZyIyHUVWYoQnxePQH0g2oa1ZcFNTqHCRfevv/6Kli1bol27dvj222+Rm5vryFxERETkxDKMGcgz5XHUcgBL9i6x/z2s/TCXP7+dSCp5RXlINaQi2icasSGx8NJ6SR2JCEAliu5Tp05hx44daN68OV5//XWEhobisccew44dOxyZj4iIiJxQYl4iFDIF5LJKTYRS66TkpGDz8c0AAE+1J3o16yVxIiLXlGZIg8FsQLPAZmge2BxapVbqSER2lfpP2alTJ8yZMwfJycmYOXMmLl26hK5du6JBgwaYNm0apwwjIiJyAXlFeUgzpsFb6y11FMkt37ccVpsVANA7oje0Kn7QJ6pOVpsV8bnxUClUaBPaBvX96nPANHI6t/X1tF6vx5gxY7Bjxw6cPXsWQ4YMwYwZMxAZGVnF8YiIiMjZZBRkwGg2Qq/SSx1FUoWmQqw8uBIAoJQr0Tuit8SJiFyL/fxtt0DEhcYh2D1Y6khE5arQ6OU3YjAYsH37dmzfvh3Z2dlo1KhRVeUiIiIiJ2S1WXE15yrcVG5SR5Hc+qPrkVtQPMbNfc3ug6+W8/8SVZd8Uz4yCzIR7RONxv6N2Z2cnNpttXTv2LEDY8aMQXBwMF566SU0bNgQO3fuxMmTJ6s6HxERETmRrMIsZBVmwUvj2gMUCSGwdN9S++VH2nOaMKLqkm5MR15RHpoHNuf521QjVLilOz4+HvPnz8e8efNw/vx5tG/fHv/9738xbNgwuLu7OzIjEREROYnU/FQIIaBSqKSOIqmDlw7iQuoFAEDLui3ROKQxEpM4tg2RI9mEDUn5SdAr9Wge2pzTgVGNUeGiOzIyEn5+fhg5ciTGjh2LmJgYR+YiIiIiJ1NoKURSfhLn5gawbN8y+99D2g2RMAmRazBbzUjMT0SgPhDNgppxIEeqUSpcdC9btgz9+/eHUnlHp4ETERFRDZVhzEBuUS7CPcOljiKp5JxkbD+1HQDg5+6H7jHdJU5EVLsZzUakGdMQ5R2Fxv6NoVPppI5EVCkVPqc7Li4OiYmJuHLlSqmfnJycOw4xa9YsREVFQavVok2bNti5c+dN1y8qKsL48eMREREBjUaDevXqYe7cuXecg4iIiMonhEBiXiLUCrXLz8298sBK+zRhA+MGQqV07a72RI6UVVA8jkSTgCZoHtScBTfVSJXqXi6Tycq9LiAgAG+++SZeffXVSgdYunQpXn75ZcyaNQudOnXCN998gz59+uDEiROoW7duubcZMmQIUlJSMGfOHNSvXx+pqamwWCyV3jYRERFVTJ4pD+nGdJcfQM1kMWHVwVUAAIVcgYFtBkqciKh2EkIgxZACpVyJ2OBYhHuG37AWIXJ2FS66Dx8+XO7y7Oxs7Nu3Dx988AH0ej2eeeaZSgX49NNPMXbsWDzxxBMAgM8++wy//fYbvvrqK3z00Udl1v/111+xfft2XLhwAb6+xVNzcH5wIiIix0ozpKHQUohAt0Cpo0hq8/HNyDJkAQC6N+mOAM8AiRMR1T4WmwVJeUnw1nqjWVAz+Ov9pY5EdEcqXHS3bNnyhtd16dIFISEh+OSTTypVdJtMJhw8eBD/+c9/Si3v2bMndu/eXe5tfv75Z8TFxWHGjBlYuHAh3Nzc0L9/f0ydOhU6HbubEBERVTWLzYL43HjOzQ1g6Z//ThM2tN1QCZMQ1U6FlkKkGFIQ5hGGpoFN4a7mLElU81XZqGgdO3bEhQsXKnWb9PR0WK1WBAUFlVoeFBSE5OTkcm9z4cIF7Nq1C1qtFqtWrUJ6ejrGjRuHzMzMG57XXVRUhKKiIvvl3NxcAIDZbIbZbK5UZqmU5Kwpeen28Di7Dh5r11BbjnO6MR3ZxmwEugXCZrVJHUcyxxOO43jCcQBAg6AGaB7W3L4/rv9NtROPs2MZTAZkF2Qj2icaDfwaQC1TS/b+WVvev+nmrj/OjjreVVZ0Z2Vlwdvb+7Zue/35GUKIG56zYbPZIJPJsGjRInh5FZ9X9umnn2Lw4MH48ssvy23t/uijjzBlypQyyzdu3Ai9Xn9bmaWyadMmqSNQNeBxdh081q6hthznVKRKHUFSC44ssP/dM6gnkg4nlVkn+Wj5jQZUuzjTcZbL5ZDLa8/ghiqokHA5AQlIkDoKlEoltm7dKnUMqkI2mw02W9kvzUr+TxuNRodst0qKbpPJhBkzZuCuu+6q1O38/f2hUCjKtGqnpqaWaf0uERISgrCwMHvBDQAxMTEQQiA+Ph4NGjQoc5u333671CBvubm5qFOnDnr27AlPT89KZZaK2WzGpk2bcN9990Gl4iiptRWPs+vgsXYNteE4F5oLsSd+D1RyFdw1rtvNM8uQhV2/7AIAeGo9MbTvUGhVWvv1NqsNyUeTEdwyGHJF7SmAqDSnOs5WQG6UQ26VQ4aaPcCYVVghgwwqhQoKmULqOACKGwELCwuh1Wo5gFsto9PpEBQUBJVKVeb/dEmP6KpW4aJ74MDyR+fMycnBsWPHoFQqbznV1/XUajXatGmDTZs24aGHHrIv37RpEwYMGFDubTp16oTly5cjPz8f7u7F//zPnDkDuVyO8PDy5w3VaDTQaDRllqtUqhr3IagmZqbK43F2HTzWrqEmH+eUghQYrAbUcavj0h88fz7yM8zW4m6HA1oPgF5bfk85uUIufTFGDif5cRaALFsGnUYH3yDfGvv+IiBgsVmglCuhUWqglFdZJ9w7ZrPZ7PVGbepJ4MqEEDCZTEhLS8PVq1dLNdaW/J921Gupws/sa1uWr1WnTh0MHjwYI0aMuK1W41dffRUjR45EXFwcOnTogG+//RZXrlyxD8j29ttvIyEhAQsWFHfpGj58OKZOnYoxY8ZgypQpSE9PxxtvvIHHH3+cA6kRERFVISEE4nPjoVFoXLrgtlgtWHFgBYDiU+IGtR0kcSJyeVZADjkCgwKh1Wlvvb4TEkLAbDPDXeEOrVILhdw5WrhL2Gw2mEwmaLVaFt21iE6ng0qlwuXLl2EymaBQVM/zrsJF9/fff++QAEOHDkVGRgbee+89JCUloVmzZtiwYQMiIiIAAElJSbhy5Yp9fXd3d2zatAkvvPAC4uLi4OfnhyFDhuD99993SD4iIiJXlVOUgwxjBry13lJHkdTOMzuRkpMCALi7wd0I9y2/Zx1RdZLJZDX2yzCbsMFis0Cj1ECr1EIuY1FL1UeKL1HuuA/H9u3bYTAY0KFDB/j4+NzWfYwbNw7jxo0r97p58+aVWda4ceNaMygNERGRs0ozpMFkNUGrrJktaVXl2mnChrQfImESoprPYrNAQECn0rl8LxpyHRUuuj/++GPk5+fbRwEXQqBPnz7YuHEjACAwMBC///47mjZt6pikREREVG3MVjMSchPgofaQOoqkzqeex4GLBwAAdf3qon10e4kTEdVcZpsZMsigV+mhVqiljkNUbSrctr5kyRI0adLEfnnFihXYsWMHdu7cifT0dMTFxZU7LRcRERHVPJkFmcguzIanpmbM8uEoy/ctt//9cNuHeW4n0W0yWU1QyBR44ekXoFFqMG3atFLXr169ulSrtxAC3377Ldq3bw93d3d4e3sjLi4On332mcOmdSJylAr/57h48SJatGhhv7xhwwYMGjQInTp1gq+vL959913s2bPHISGJiIioeiXlJ0EhVzjd4EbVKb8wH+uPrgcA6NQ69IvtJ3EioqplswFnz8hw8IAcZ8/IUM70xXdMQMBkNUGlUEGv0kMuk0Or1WL69OnIysq64e1GjhyJl19+GQMGDMDWrVtx5MgRTJgwAWvWrLH3tCWqKSrcvdxsNpeadmvPnj146aWX7JdDQ0ORnp5etemIiIio2hlMBqQYUly+lXvdkXUoMBUAAPq27At3revOU061z9EjcixaqMDpU3IUFsqg1Qo0amzDiJFWtGxVNdX3jQZMu/fee3Hu3Dl89NFHmDFjRpnbLVu2DIsWLcLq1atLTSMcGRmJ/v37O2wuZSJHqXBLd/369bFjxw4AwJUrV3DmzBl06dLFfn18fDz8/PyqPiERERFVq3RjOgwmA9xUblJHkYwQAsv3X9O1vN3DEqYhqlpHj8jx0QcqHD6kgI+PQL36Nvj4CBw+pMBHH6hw9Midn0ZhFVZYbVZolVrolLpSI5QrFAp8+OGHmDlzJuLj48vcdtGiRWjUqFGpgruETCa74VTGRM6qwq+oZ599Fs8//zzGjh2LPn36oEOHDqXO8d6yZQtiY2MdEpKIiIiqh03YkJCXAL1S79KjCh+4eACX0y8DAFpHtka9wHoSJyKqGjYbsGihAhnpMjRqbIOHJ6BQAB6eQKPGNmSky7D4B8UddTW32CwQoniEcq1SW+57yUMPPYRWrVph0qRJZa47e/YsGjVqdPsBiJxMhYvup59+Gp9//jkyMzPRuXNn/PTTT6WuT0xMxOOPP17lAYmIiKj6ZBVkIcOYAS+ta7ckrdi/wv734LaDJUxCVLXOn5Ph9Ck5wsJsuL4WlsmAsDAbTp2U4/y52/vSzWwzQyYrHqFco7z5lGDTp0/H/PnzceLEiVLLhRAu/aUf1T6Vmqd77NixGDt2bLnXzZo1q0oCERERkXTSjGmw2qwuPZ1Pel46tp3aBgDwc/dDt8bdpA1EVIVyc2UoLJRB7ybKvV6nB5KSZMjNlQEof53yCAiYrWYo5UroVDoo5bcuMzp37oxevXrhnXfewejRo+3LGzZsiJMnT1Z420TOrkIt3QaDoVJ3Wtn1iYiISHpFliIk5Ca4/ABqqw+thtVmBQD0j+0PlVIlcSKiquPpKaDVChhv8HG9wAhotQKenpUouEVxwa1WqKFX6StUcJeYNm0a1q5di927d9uXDR8+HGfOnMGaNWvK3VZOTk6F75/IGVSo6K5fvz4+/PBDJCYm3nAdIQQ2bdqEPn364H//+1+VBSQiIqLqkVGQgdyiXHhoPKSOIhmrzYpVB1cBKB6waWDcQIkTEVWtevWLRylPSJBDXFdXCwEkJMjROMaGevUrVnTbhA1mmxkapQY6la7S0ww2b94cI0aMwMyZM+3LhgwZgqFDh+KRRx7BRx99hAMHDuDy5ctYt24d7r33XmzdurVS2yCSWoW+htq2bRveffddTJkyBa1atUJcXBxCQ0Oh1WqRlZWFEydOYM+ePVCpVHj77bfx1FNPOTo3ERERVSEhBBJyE6BWqEuNMuxqdp3ZhZScFABApwadEOIdInEioqollwMjRlpx5Yrcfm63Tl/cwp2QIIe/v8DwR62QV+BtwCqssAkbdErdLc/fvpmpU6di2bJl9ssymQyLFy/Gt99+i7lz5+L999+HUqlEgwYN8Nhjj6FXr163tR0iqVSo6G7UqBGWL1+O+Ph4LF++HDt27MDu3btRUFAAf39/xMbG4rvvvsP9998PeUVeoURERORUcotykWZMg7fWW+ookvrpwL8DxXIANaqtWray4e3xZvs83UlJxfN0t25jxfBHKzZPt8VmAQDolDqoFeoKF9zz5s0rsywiIgKFhYWllsnlcjzzzDN45plnKnS/RM6sUgOphYeH45VXXsErr7ziqDxEREQkgXRjOoosRdC6aaWOIpmErATsObcHABDiHYIO9TtInIjIcVq2sqF5CxvOnyseNM3TU6BefVGhFm6zzQy5TA6dUgeVgmMeEN1KpYpuIiIiqn0sNgvic+PhrnaXOoqkVh5YCfHPSa4D2wys9LmpRDWNXA40aChQ0VHKb2eEciK6jaL71VdfLXe5TCaDVqtF/fr1MWDAAPj6+t5xOCIiInK8DGMGsguzEeweLHUUyZgsJqw5VDxSslKhRP/W/SVORORcSgpulUIFnbLyA6YRubJKF92HDx/GoUOHYLVa0ahRIwghcPbsWSgUCjRu3BizZs3Ca6+9hl27dqFJkyaOyExERERVKCk/CTKZzKVbrbac3IJsYzYAoFtMN/i5+0kbiMiJ2IQNFpsFGqUGWqXWpQdbJLodlX7FDBgwAPfeey8SExNx8OBBHDp0CAkJCbjvvvvwyCOPICEhAZ07d+Z530RERDWAwWRAiiEFXhovqaNI6qf9HECNqDxWYYXVZoVWqYVOqWPBTXQbKv2q+fjjjzF16lR4enral3l6emLy5MmYMWMG9Ho9Jk6ciIMHD1ZpUCIiIqp66cZ0GEwGlz6f+1zKORy+fBgAEBUQhdYRrSVOROQcLDZL8ZRgKh20Su1tTwlG5OoqXXTn5OQgNTW1zPK0tDTk5uYCALy9vWEyme48HRERETmMTdhwNecq3FRuUkeR1MoDK+1/D4obxMKCCMUjlAOAXqW/ozm4ieg2u5c//vjjWLVqFeLj45GQkIBVq1Zh7NixePDBBwEA+/btQ8OGDas6KxEREVWhzIJMZBZmunTXcmOREeuPrgcAaFVa9G3ZV+JERNIzW81QyBTQq/RQK9RSxyGq8So9Yso333yDV155BcOGDYPFYim+E6USo0aNwn//+18AQOPGjTF79uyqTUpERERVKiU/BUIIl55n97e/f4OhyAAA6NW8Fzx0HhInIpKOfYRyuQo6FUcoJ6oqlS663d3d8d133+G///0vLly4ACEE6tWrB3f3f88Fa9WqVVVmJCIioipWYC5AYl6iS7dyCyGw4sAK+2UOoEauTAgBs80MtUINrVLLgpuoCt328IPu7u7w9fWFv79/qYKbiIiInF+6MR15pjyXHkDteMJxnE46DQBoEtoEMaExEicikoZN2GC2maFRaqqlhXv37t1QKBTo3bt3qeWXLl2CTCYr92fv3r0OzUTkSJUuum02G9577z14eXkhIiICdevWhbe3N6ZOnQqbzeaIjERERFSFbMKGhNwEaBWuPRrxtdOEDWo7SMIkRBKx2SDOnAb27YfuwlXo5JpqmRJs7ty5eOGFF7Br1y5cuXKlzPWbN29GUlJSqZ82bdo4PBeRo1S6e/n48eMxZ84cTJs2DZ06dYIQAn/88QcmT56MwsJCfPDBB47ISURERFUkuzAb6cZ0+Op8pY4imRxjDjYe2wgA8NB6oFezXhInIqpe8iNHIV/wA+SnTkFuMkOu1UHWpAkwahQQG+uw7RoMBixbtgz79+9HcnIy5s2bh4kTJ5Zax8/PD8HBwQ7LQFTdKv1V1vz58zF79mw8++yzaNGiBVq2bIlx48bhu+++w7x58xwQkYiIiKpSqiHV3pXUVa07sg5FliIAwAOtHoBWrZU4EVH1kR85CsX7H0B++DDk/v5QNGwEmZ8fcPAg8N57wOHDDtv20qVL0ahRIzRq1AiPPvoovv/+ewghHLY9ImdQ6aI7MzMTjRs3LrO8cePGyMzMrJJQRERE5BhFliIk5Ca4/ABqPx34t2v5wLiBEqYhqmY2G2QLFkKekQl5k6ZQePkACgXg6QnExADp6cCCBYCDThudM2cOHn30UQBA7969kZ+fj99//73UOh07doS7u3upH6vV6pA8RNWh0kV3y5Yt8cUXX5RZ/sUXX6Bly5ZVEoqIiIgcI92YjtyiXHhoXHdqrAMXD+BKRvF5pG0i2yAqIEriRETVQ0DAcuYUlKfPQF6nbtkB02QyIDwcOHECOHeuyrd/+vRp7Nu3D8OGDQNQPO3w0KFDMXfu3FLrLV26FEeOHCn1o1BwNHWquSp9TveMGTPQt29fbN68GR06dIBMJsPu3btx9epVbNiwwREZiYiIqAoIIRCfGw+1Ql0tgyU5qxX7OU0YuZ6SObg1+UYoisyQud/gize9HkhMBHJyqjzDnDlzYLFYEBYW9m8uIaBSqZCVlWVfVqdOHdSvX7/Kt08klUr/x+3SpQvOnDmDhx56CNnZ2cjMzMTAgQNx+vRp3HPPPY7ISERERFWgZAA1b6231FEkk56Xjm2ntgEA/Nz90LVxV0nzEFUHIf4puBUaaP2CINPpAIOh/JWNRkCrBbyq9hQUi8WCBQsW4P/+7/9KtWAfPXoUERERWLRoUZVuj8iZVLqlGwBCQ0M5SjkREVENUzKAmlbpuoOGrT60GlZb8bmh/WP7Q6VUSZyIyLFswgaLzQKtUgutUgtZg4bF524fPFj8+9ppA4UA4uOBuDigilua161bh6ysLIwdOxZe1xX0gwcPxpw5c/DAAw8AADIyMpCcnFxqHW9vb2i1rvveRTVbhYruv/76q8J32KJFi9sOQ0RERI5RZClCfG48PNSuey631WbFqoOrAAAymYwDqFGtZxVW2IQNOpUOGoUGMpmsuMgeNQq4fBk4ebL4HG69vriFOz4e8PcHHnsMkFftKShz5szBvffeW6bgBoBBgwbhww8/tA/KfO+995ZZZ8mSJfZzwYlqmgoV3a1atYJMJrvlcP4ymYwjCxIRETmhkgHUwj3DpY4imV1ndiElJwUAcHeDuxHiHSJxIiLHsdgsEBDQKXVQK9TFBXeJ2Fhg4kRg/vziwjsxsbhLeVxcccHtgHm6165de8PrWrduba8zOH0Y1UYVKrovXrzo6BxERETkIBxArdi104QNajtIwiREjmW2mSGDDHqVHmqFuvyVYmOBli2LRynPySk+h7t+/Spv4SaiChbdERERjs5BREREDlIygJqP1kfqKJJJyErAnnN7AAAh3iHoUL+DxImIHMNsM0Muk0On1EGluMWYBXI50LBh9QQjcmG3NZAaERER1RwphhSYbWZolBqpo0hm5YGV9m6rA9sMLDs/MVENVzIlmFKuhE6lg1LOj/lEzoKvRiIiolqs0FKIhNwEeKo9pY4iGZPFhDWH1gAAlAolBrQeIHEioqpVUnCrFCrolDp+qUTkZHjSBhERUS1WMoCah8Z1Ry3fcnILso3ZAIDuMd3h6+4rbSCiKlQyB7daoYZepWfBTeSE2NJNRERUS9mEDfE58dAoNK49gNp+DqBGtZOAsJ86olVqXfp1TuTM+MokIiKqpbIKsooHUNO57gBq51LO4fDlwwCA6IBotI5oLXEioqphEzZYbVZolVrolDoW3ERO7LZenX379kVSUlKZv4mIiMh5JOcnwyqsN54yyAVcO03YwLiBpecqJqqhsguzIYSAVqmFVqnl85rIyd1W0b1jxw4UFBSU+ZuIiIicg9FsRGJeIrw0XlJHkYyxyIgNRzcAADQqDfq27CtxIqI7l2pIhU3YoFKooFaqWXAT1QDsh0JERFQLpRnSkGfKg7vaXeookvnt799gKDIAAHo37w0PnesOJkc1nxACiXmJUMlViPGP4ZRgtzB58mS0atXKfnn06NF48MEHqz3HpUuXIJPJcOTIkWrfdlU85ork37ZtG2QyGbKzswEA8+bNg7e3t/3664+FK2LRTUREVMtYbVZczbkKN5Wby7aCCSGw4sAK++XBbQdLmIboztiEDQl5CfBQe6B1aGv4u/lLHem2jB49GjKZDDKZDCqVCtHR0Xj99ddhMBgcvu3PP/8c8+bNq9C6ly5dgkKhwN9//+3YUP/o2rWrfb9oNBo0bNgQH374IaxWa7Vs/0517NgRSUlJ8PIqv2fV66+/jt9//91+WaovQKTEr8iIiIhqmYyCDGQWZiLILUjqKJI5nnAcp5NOAwCahDVBTGiMxImIbo/FZkFiXiIC3QLRPKg5PDWeKCwslDrWbevduze+//57mM1m7Ny5E0888QQMBgO++uqrMuuazWaoVKoq2e6NCkJn8eSTT+K9995DYWEh1q1bhxdffBEKhQJvvfVWmXVNJhPUaucZq0OtViM4OPiG17u7u8Pd3XV7XQFs6SYiIqp1EvMSAcClu5+WmiYsjtOEUc1ktpqRkJeAUI9QxIbEwlPjKXWkO6bRaBAcHIw6depg+PDhGDFiBFavXg3g327Ic+fORXR0NDQaDYQQyMnJwVNPPYXAwEB4enqie/fuOHr0aKn7nTZtGoKCguDh4YGxY8eW+WLi+tZVm82G6dOno379+tBoNKhbty4++OADAEBUVBQAoHPnzlAoFOjatav9dt9//z1iYmKg1WrRuHFjzJo1q9R29u3bh9jYWGi1WsTFxeHw4cMV2i96vR7BwcGIjIzE888/jx49etj3S0n2jz76CKGhoWjYsCEA4O+//0b37t2h0+ng5+eHp556Cvn5+WXue8qUKfZ99/TTT8NkMtmv+/XXX3H33XfD29sbfn5+eOCBB3D+/Pky93Hq1Cl07NgRWq0WTZs2xbZt2+zXXd+9/HrXdi+fPHky5s+fjzVr1thb97dt24bu3bvj+eefL3W7jIwMaDQabNmypUL70Jmx6CYiIqpF8orykJyfDG+Nt9RRJJNjzMHGYxsBAO5ad/Rq1kviRESVV2gpRGJ+IqK8o9AquBX0Kr3UkRxCp9PBbDbbL587dw7Lli3DTz/9ZD+PuG/fvkhOTsaGDRtw8OBBtG7dGj169EBmZiYAYNmyZZg0aRI++OADHDhwACEhIWWK4eu9/fbbmD59OiZMmIATJ05g8eLFCAoq7h20b98+AMDq1auRkJCAlStXAgC+++47jB8/Hh988AFOnjyJDz/8EBMmTMD8+fMBAAaDAQ888AAaNWqEgwcPYvLkyXj99derZL/8/vvvOHnyJDZt2oR169bBaDSid+/e8PHxwf79+7F8+XJs3ry5TOFacrutW7diyZIlWLVqFaZMmWK/3mAw4NVXX8X+/fvx+++/Qy6X46GHHoLNZit1P2+88QZee+01HD58GB07dkT//v2RkZFR6cf1+uuvY8iQIejduzeSkpKQlJSEjh074oknnsDixYtRVFRkX3fRokUIDQ1Ft27dKr0dZ3NbX4Ffe36Yq54rRkRE5IxS8lNQYC5AgD5A6iiSWX90PYosxR/cHmj5ALRqrcSJiCrHaDYi3ZiOBr4N0Ni/MVSKW3exjns/Dsk5ydWQrrRgr2AcePfAbd123759WLx4MXr06GFfZjKZsHDhQgQEFL+HbdmyBX///TdSU1Oh0WgAAJ988glWr16NFStW4KmnnsJnn32Gxx9/HE888QQA4P3338fmzZtv2A0/Ly8Pn3/+Ob744guMGjUKAFCvXj3cfffdAGDftq+vL4KDgyGXF7dTTp06Ff/3f/+HgQMHAihuET9x4gS++eYbjBo1CosWLYLVasXcuXOh1+vRtGlTxMfH49lnn63wPrHZbNi4cSN+++03vPzyy/blbm5umD17tr1b+XfffYeCggIsWLAAbm5uAIAvvvgC/fr1w/Tp0+1fIKjV6lJ53nvvPbzxxhuYOnUq5HI5Bg0q3RNozpw5CAwMxIkTJ9CsWTP78ueff96+7ldffYVff/0Vc+bMwZtvvlnhxwYUdzXX6XQoKioq1SV90KBBeOGFF7BmzRoMGTIEQHGvgpJxAGq62yq6hRDl/k1ERETSMVlNuJJ7BR5q1x2lWwhRam7uQW3ZtZxqlryiPOQU5SAmIAYNfBtAIVdU6HbJOclIyE5wcLo7t27dOri7u8NiscBsNmPAgAGYOXOm/fqIiAh70QsABw8eRH5+Pvz8/ErdT0FBgb0b9MmTJ/HMM8+Uur5Dhw7YunVruRlOnjyJoqKiUsX+raSlpeHq1asYO3YsnnzySftyi8ViP1/85MmTaNmyJfT6f3sldOjQoUL3P2vWLMyePdve9XvkyJGYNGmS/frmzZuXOo+7ZFslBTcAdOrUCTabDadPn7YX3eXlyc/Px9WrVxEREYHz589jwoQJ2Lt3L9LT0+0t3FeuXClVdF/7OJRKJeLi4nDy5MkKPbaK0Gg0ePTRRzF37lwMGTIER44cwdGjR+1d7Gu62yq68/Lyyv2biIiIpJNmSENuYS7CPMOkjiKZAxcP4HL6ZQBAm8g2iAqIkjgRUcVlFWShwFKAZoHNEO0TXakWvmCvGw9k5UiV3W63bt3w1VdfQaVSITQ0tMxAadcWkUBxy29ISEipc4hLXDstVWXodLpK36akGP3uu+/Qvn37UtcpFMVfjNxJY+SIESMwfvx4aDQahIaG2u+zxPX7RQhxw+dHRZ43Jev069cPderUwXfffYfQ0FDYbDY0a9as1Hnft7qPqvLEE0+gVatWiI+Px9y5c9GjRw9ERERU6Tak4rojrBAREdUiNmHD1dyrUClUkMtcd8gWtnJTTZVuTIdVWNEquBXqeNWp9O1vt4t3dXNzc0P9+vUrvH7r1q2RnJwMpVKJyMjIcteJiYnB3r178dhjj9mX7d2794b32aBBA+h0Ovz+++/2LunXKmlRvnbKrqCgIISFheHChQsYMWJEuffbpEkTLFy4EAUFBfbC/mY5ruXl5VWp/dKkSRPMnz8fBoPBXpD/8ccfkMvl9oHWAODo0aNl8ri7uyM8PBwZGRk4efIkvvnmG9xzzz0AgF27dpW7vb1796Jz584Ailv3Dx48WOb88YpSq9XlTofWvHlzxMXF4bvvvsPixYtL9YCo6Vz3vzIREVEtklWQhXRDOny0PlJHkUx6Xjq2nizuTurn7odujWv+4DtU+wkhkJyfDJlMhtjg2NsquGuze++9Fx06dMCDDz6I3377DZcuXcLu3bvx7rvv4sCB4i8aXnrpJcydOxdz587FmTNnMGnSJBw/fvyG96nVavHWW2/hzTffxIIFC3D+/Hns3bsXc+bMAQAEBgZCp9Nh8+bNSElJQU5ODoDikbc/+ugjfP755zhz5gz+/vtvfP/99/j0008BAMOHD4dcLsfYsWNx4sQJbNiwAZ988olD9suIESOg1WoxatQoHDt2DFu3bsULL7yAkSNH2ruWA8XnyJfk+eWXXzBp0iQ8//zzkMvl8PHxgZ+fH7799lucO3cOW7Zswauvvlru9r788kusWrUKp06dwnPPPYesrCw8/vjjt5U9MjISf/31F06fPo309PRSA8Y98cQTmDZtGqxWKx566KHbun9nxKKbiIioFkjMS4RVWKFRaqSOIpnVh1bDaituPekf2x8qZdXM70vkKDZhQ2J+InQqHVqHtEaIR4jUkZyOTCbDhg0b0LlzZzz++ONo2LAhhg0bhkuXLtmLy6FDh2LixIl466230KZNG1y+fPmWg5dNmDABr732GiZOnIiYmBgMHToUqampAIrPWf7ss88wb948hIeHY8CAAQCKC8LZs2dj3rx5aN68Obp06YJ58+bZpxhzd3fH2rVrceLECcTGxmL8+PGYPn26Q/aLXq/Hb7/9hszMTLRt2xaDBw9Gjx498MUXX5Rar0ePHmjQoAE6d+6MIUOGoF+/fpg8eTIAQC6X48cff8TBgwfRrFkzvPLKK/j444/L3d60adMwffp0tGzZEjt37sSaNWvg7+9/W9mffPJJNGrUCHFxcQgICMAff/xhv+6RRx6BUqnE8OHDodXWnkEwZcIFR0LLzc2Fl5cXcnJy4OlZM+Y7NJvN2LBhA+6///4y575Q7cHj7Dp4rF1DdR3nfFM+/rj6B7QKLdzV7g7bjjOz2qzo/1l/pOSkQCaTYc1LaxDqE1pt27dZbUg8lIjQ1qGQK9imUVtV5XG22qxIzEuEn94PzYOaw1vrXeHbFhYW4uLFi4iKiqpVhYkzsdlsyM3Nhaenp330cnK8q1evIjIyEvv370fr1q0dso1rXz8KhaLU/2lH1Yk8p5uIiKiGS8lPgcFkgL/X7bU61AZ/nP0DKTkpAIBODTpVa8FNVFkWmwWJeYkIdg9G86DmLvtlGVEJs9mMpKQk/Oc//8Fdd93lsIJbKhX+2ubEiRO3XOeHH364ozBERERUOWarGfG58S49TRgArNi/wv734LaDJUxCdHMmqwkJeQkI9wxHq+BWLLiJUDwIXEREBA4ePIivv/5a6jhVrsJFd5s2bfDJJ5+UOxR+SkoK+vfvX6mJ34mIiOjOpRpSkVWQBS+Nl9RRJJOQlYA95/YAAEK8Q9ChfsXmxSWqboWWQiTnJyPaJxotg1tCp6r81FVEtVHXrl0hhMDp06fRvHlzqeNUuQoX3T/88ANmzJiBzp072yeiL1nepEkT5OTk4MiRI47ISEREROW4dpowhVxx6xvUUqsOrLI3CjzU5iGX3hfkvAwmA9IMaWjo1xDNAptBrVBLHYmIqkmFi+5Bgwbh2LFj8Pf3R8uWLfHJJ59gwIABePrppzFx4kRs27YN9erVc2RWIiIiukZmQSbSDGnw1flKHUUyJosJaw6vAQAo5AoMaD1A4kREZeUW5SKrMAsxATGICYiBUs5hlYhcSaVe8YGBgVi1ahVGjBiBN998E25ubti7d2+t7AJARETk7BJyE2ATNpduMdt6ciuyDFkAgO4x3eHn7idxIqLSsgqyUGApQPPA5ojyiYJMJpM6EhFVs0qNf5+VlYXhw4dj9erV+M9//oPAwEAMHToU+/fvd1Q+IiIiKkduUS6S8pPgo/WROoqkftr/k/3vQW0HSZiEqKx0YzrMNjNaBbdCtG80C24iF1XhonvdunVo0qQJzp8/j4MHD+LDDz/EX3/9hS5duuDuu+/Gu+++C4vF4sisRERE9I/k/GQYzUa4qd2kjiKZ86nncejyIQBApH8k2kS2kTgRUTEhBJLzkyGTydAquBXqeNWROhIRSajCRffgwYPxwgsvYM+ePWjcuDEAwM3NDV999RXWrVuHhQsXIi4uzmFBiYiIqFihpRBXc6669IjlALDywEr734PiBrEVkZyCEAKJ+YnQKrVoHdIaIR4hUkciIolVuOjet28f3nnnHcjlZW9y33334e+//0abNvyGmYiIyNFS8lOQW5Tr0kV3gakA646sAwBoVBo80OoBiRMRAVabFQl5CfDSeKFNaBv46/2rdfsmqwlGs7HafkxWU7U+Pmc2cuRIfPjhhw6579GjR+PBBx90yH1Xh23btkEmkyE7O7vK71smk2H16tUAgNTUVAQEBCAhIaHKt3OnKjyQWosWLW56vaenJ+bMmXPHgYiIiOjGLDYLruRcgV6ld+mW3d/+/g2GIgMAoFezXvDQeUiciFydxWZBYl4igt2D0SywGTw01fucNFlN2Be/D/nm/GrbprvKHe3C21V4MMe8vDxMmDABq1atQmpqKmJjY/H555+jbdu29nVGjx6N+fPnl7pd+/btsXfvXvvlV199FfPmzYO7uztmzJiBYcOG2a9btmwZFi5ciLVr194yj8lkwmeffYZFixbh7Nmz0Ov1aNSoEZ544gkMHz4cADBmzBjk5OTYC7vy/PXXX1i/fj1mzZplX9a1a1ds374dAKBWq+Hv74/WrVtjzJgxGDhw4C2z3UzXrl3RqlUrfPbZZ3d0P5cuXUJUVBQUCgUuX76MsLAw+3VJSUmoU6cOrFYrLl68iMjIyGrNdjsCAwMxcuRITJo0CbNnz6727d8M5ysgIiKqQdIMacgsyESwe7DUUSQjhMCK/SvslzmAGknNbDUjMT8RdTzroGlgU+hV+mrPYLFZkG/Oh1quhkapcfj2iixFyDfnw2KzVLjofuKJJ3Ds2DEsXLgQoaGh+OGHH3DvvffixIkTpQq+3r174/vvv7dfVqv/vf+1a9di8eLF2LhxI86ePYsxY8bgvvvug5+fH7KzszF+/Hj8/vvvt8xiMpnQq1cvHD16FFOnTkWnTp3g6emJvXv34pNPPkHLli0RHR1docf1xRdf4OGHH4aHR+kvWp588km89957MJvNSEhIwKpVqzBs2DCMHj0a3377bYXuuzqEhoZiwYIFePvtt+3L5s+fj7CwMFy5ckXCZJU3ZswYtGvXDh9//DF8fJxnoNFKjV5ORERE0rEJG67kXIFSrnTpeX5PJJ7AqaRTAICY0Bg0DWsqcSJyZYWWQiTmJyLKOwotglpIUnBfS6PUQKvUOvynsoV9QUEBfvrpJ8yYMQOdO3dG/fr1MXnyZERFReGrr74q/Rg0GgQHB9t/fH197dedPHkSXbt2RVxcHB555BF4enriwoULAIA333wT48aNQ926dW+Z57PPPsOOHTvw+++/47nnnkOrVq0QHR2N4cOH488//0SDBg0q9LhsNhuWL1+O/v37l7lOr9cjODgYderUwV133YXp06fjm2++wXfffYfNmzfb10tISMDQoUPh4+MDPz8/DBgwAJcuXSp3e6NHj8b27dvx+eefQyaTQSaT4dKlS7BarRg7diyioqKg0+nQqFEjfP755xV6DKNGjSr1JQcAzJs3D6NGjSqz7okTJ3D//ffD3d0dQUFBGDlyJNLT02+arcTBgwcRFxcHvV6Pjh074vTp06Xu+6uvvkK9evWgVqvRqFEjLFy4sNT1Z8+eRefOnaHVatGkSRNs2rSpTL7mzZsjODgYq1atqtBjry4suomIiGqIzIJMpBpS4avzvfXKtVipVu44tnKTdIxmI1INqWjg2wDNAptVSwtzTWWxWGC1WqHVakst1+l02LVrV6ll27ZtQ2BgIBo2bIgnn3wSqamp9utatmyJAwcOICsrCwcPHkRBQQHq16+PXbt24dChQ3jxxRcrlGfRokW49957ERsbW+Y6lUoFN7eKzQzx119/ITs7u8IDSo8aNQo+Pj5YubJ4IEij0Yhu3brB3d0dO3bswK5du+Du7o7evXvDZCp7zvznn3+ODh064Mknn0RSUpK9G7jNZkN4eDiWLVuGEydOYOLEiXjnnXewbNmyW2bq378/srKy7Mdh165dyMzMRL9+/Uqtl5SUhC5duqBVq1Y4cOAAfv31V6SkpGDIkCE3zVZi/Pjx+L//+z8cOHAASqUSjz/+uP26VatW4aWXXsJrr72GY8eO4emnn8aYMWOwdetWAMVfbgwcOBAKhQJ79+7F119/jbfeeqvcx9OuXTvs3Lnzlo+7Ornu1+REREQ1THxuPGywVbgrZ22UW5CLjcc2AgDcte7o1byXxInIVeWb8pFdmI2YgBg08G0AhVwhdSSn5uHhgQ4dOmDq1KmIiYlBUFAQlixZUqZVuU+fPnj44YcRERGBixcvYsKECejevTsOHjwIjUaDXr164dFHH0Xbtm2h0+kwf/58uLm54dlnn8W8efPw1VdfYebMmfD398e3336Lpk3L7wlz9uxZdO3a9Y4f16VLl6BQKP6/vfsOj6pK/wD+nT7JZNJ7AiG0hBBQAiJVdEVcFbuiItjQXVd3FV3Ftv4EdwXXwlp2bay9ACq4rhWwgCJICWWpQiAQ0gtJJtPLPb8/LjMkJIEkJrmTzPfzPPMk986ZO++9Z2buvHPOPQeJiYltKq9WqzF48OBAC/CSJUugVqvx73//OzBOx5tvvono6GisXr0aU6ZMafL4qKgo6PX6QCu6n0ajwbx58wLLmZmZWLduHT788MNAUtwanU6HGTNm4I033sCECRPwxhtvYMaMGdDpdE3Kvfzyy8jLy2syYNwbb7yBPn36YN++fRg8eHCLsfk98cQTmDRpEgDgwQcfxEUXXQSn0wmj0YhnnnkGN910E+644w4A8nX7/q7+55xzDr755hvs2bMHhw4dQnp6OgBg/vz5uOCCC5o9T1paGrZu3XrSfe5ubOkmIiLqAeqcdShtKEWcMU7pUBT12dbP4PK4AAAXnXYRwvRhCkdEoajeWQ+Ly4KhCUMxOG4wE+42evfddyGEQFpaGgwGA1544QVMnz4dGs3x43fNNdfgoosuQm5uLi6++GJ89dVX2LdvH7744otAmblz56KgoAA7duzA5Zdfjvnz52Py5MnQ6XT429/+hrVr1+LWW2/FDTfc0GosQohOGYzS4XDAYDC0a1uNnzs/Px8FBQUwm82IiIhAREQEYmNj4XQ6ceDAgXbF8sorr2DUqFFISEhAREQEFi1a1OZrsmfNmoWPPvoI5eXl+Oijj5q0Qvvl5+fj+++/D8QZERERmEq6LbE2Hpg7JUWeSs/fi2HPnj0YP358k/Ljx4/Hnj17Avf37ds3kHADwNixY1t8nrCwMNjt9lPG053a1NL9+OOPd2jjZ599Ns4666wOPZaIiIiOK7WUwu1zI0wXukmmJEn4aNNHgeWrz7hawWgolDm9TgxPGY6+UX1DehaB9howYADWrFkDm80Gi8WClJQUXHPNNcjMzGz1MSkpKcjIyMD+/ftbvH/v3r14//33sXXrVrzxxhs466yzkJCQgGnTpuGWW26BxWJBZGRks8cNHjw4kND9GvHx8bDb7XC73U0GfGuNz+fD/v37AyO2S5KEkSNH4v33329WNiEhoc1xfPjhh7jnnnvw7LPPYuzYsTCbzXj66aexYcOGNj0+NzcX2dnZuO666zBkyBDk5uZi27ZtTcpIkoSLL74Yf//735s93p9En0zjlnP/+0aSpGbr/Br/OCGEaLa91t57R48ebdex6w5tSroLCws7tPHTTz+9Q48jIiKi46xuK440HEGMMXhGYlXC+gPrUXy0GAAwuv9o9Evop2xAFHIqrXKr3PCk4egbferBuqhlJpMJJpMJtbW1WLFiBZ566qlWy9bU1ODIkSMtJnVCCPzud7/Ds88+i4iICPh8Png8HgAI/G2c1DU2ffp0PPzww9i6dWuz67q9Xi8cDkeb9sWf7+zevbtNuc/bb7+N2tpaXHmlPB5FXl4eli5disTExBZ/HGiJXq+Hz+drsu7HH3/EuHHjAt2zgba1Pjd2yy234I477mg2sJ1fXl4eli1bhn79+kGrbTmNbCm2thgyZAjWrl3bpHfCunXrMGTIEABATk4OioqKUFpaitTUVADA+vXrW9zWzp07O+XSgc7UpqT7xNHsiIiIqPuUNZTB5rYhPipe6VAU9dHG463c00af/BpFos4khECZtQxGjRFeeJFiPnWrHjW3YsUKCCGQlZWFgoIC3H///cjKysLNN98MALBarZg7dy6uvPJKpKSk4NChQ3j44YcRHx+Pyy+/vNn2Fi1ahMTExMDI4ePHj8fcuXPx888/46uvvkJOTg6io6NbjGX27Nn44osvcO655+Kvf/0rJkyYALPZjM2bN+Pvf/87Fi1a1KYpwxISEpCXl4e1a9c2S7rtdjvKy8vh9XpRUlKC5cuX4x//+Af+8Ic/4JxzzgEAXH/99Xj66adx6aWX4vHHH0d6ejqKioqwfPly3H///U26U/v169cPGzZswKFDhwLd0QcOHIh33nkHK1asQGZmJt59911s2rTppL0ITnTbbbfh6quvbvWY3XnnnVi0aBGuu+463H///YiPj0dBQQGWLFmCRYsWQaPRtBhbW9x///2YNm0a8vLycO655+Kzzz7D8uXLA6O8T548GVlZWbjhhhvw7LPPwmKx4JFHHmm2Hbvdjvz8/CbXnQcDXtNNREQUxJxeJ4rqixBliFI6FEUVHy3GT/t/AgAkRyVjwuAJCkdEoUISEkqsJTDrzTgt5TSlwzkll9cFp9fZ5TeX19Xu2Orr63HnnXciOzsbN9xwAyZMmICVK1cGuh1rNBrs2LEDl156KQYPHowbb7wRgwcPxvr165vNgV1RUYH58+fjhRdeCKwbPXo0/vznP+Oiiy7Chx9+eNKGQ4PBgFWrVmHOnDl49dVXMWbMGJxxxhl44YUXcNdddyE3N7fN+/W73/2uxe7hixYtQkpKCgYMGIDLL78cu3fvxtKlS/HSSy8FyoSHh+OHH35A3759ccUVV2DIkCG45ZZb4HA4Wm35vu+++6DRaJCTk4OEhAQUFRXh9ttvxxVXXIFrrrkGZ555Jmpqapq0ereFVqtFfHx8q63Yqamp+Omnn+Dz+XD++ecjNzcXd999N6KioqBWq1uNrS0uu+wyPP/883j66acxdOhQvPrqq3jzzTcDLdZqtRqffPIJXC4XRo8ejVtvvRVPPPFEs+18+umn6Nu3LyZOnNiufe9qKtFSB/lezmKxICoqCvX19W3uxqE0j8eDL7/8EhdeeGGzkQSp92A9hw7WdWjojHo+VHcIW8u2ok9kn5C+dvT5Fc/j3XXynK13nnsnbj7rZoUjakrySSjdUorUvFSoNWzT6C18kg+l1lLEh8VjWNIwhGvCg+Kz2+l0orCwEJmZmYEpuNw+NzYWb4TVY+22OCJ0ERidPrpXzqggSVLgenB/Qtkap9OJrKwsLFmypNXBvah7jB49GrNnz8b06dNbLdP4/aPRaJq8p7sqT+SUYUREREHK7XPjUO0hROgjQjrhdrqd+HTrpwAAnUaHy0ZepmxAFBK8khclDSVIMadgeOJwmPSmwHXCwUiv0WN0+mh4JW+3PadWre2VCXd7GY1GvPPOO6iurlY6lJBWWVmJq666Ctddd53SoTTDpJuIiChIVVgrUOusRXpk82v6QsmKnStgcVgAAOflnocYU2gPKEddz+1zo8xahj6RfTAsaRiMWqPSIbWJXqNnEqwQ//zTpJzExETMmTNH6TBaxKSbiIgoCHklLw7VHUK4LhxqVeh2VxZCYOmGpYFlDqBGXc3pdaLCVoH+Mf2Rk5DDJJaIfrU2n8U3btzYZPj3Ey8Fd7lc+PDDDzsvMiIiohBWYa1Atb0a0cZopUNR1P+O/A/7yvcBAHJSc5Cb3vbBjYjay+a2ocpWhay4LOQm5jLhJqJO0eake+zYsaipqQksR0VF4eDBg4Hlurq6Dveff+mllwIDQYwcORI//vhjmx73008/QavVcj5wIiLqVXySD4frD8OoNUKrDu1OaU2mCTuTrdzUdRpcDah11mJIwhBkx2eH/HuPiDpPm5PuE1u2Wxr0vCMDoS9duhSzZ8/GI488gq1bt2LixIm44IILTjm8fH19PW644Qace+657X5OIiKiYFZpq0SVrQqxYW2b37S3qrHW4Jvd8hytUeFROG/oeQpHRL1VnbMODe4G5CbmYnDcYGjUGqVDapMQnISI6FdT4n3TqReJdWRk1YULF2LWrFm49dZbMWTIEDz33HPo06cPXn755ZM+7ve//z2mT5/OYfmJiKhX8bdya9XakG9p+yT/E3h98kjMl+VdBoPOoHBE1BtV26vh8rlwWvJp6B/Tv0fMFOCfrsxutyscCVHP43/fdOe0f4qezd1uN/Lz8/Hggw82WT9lyhSsW7eu1ce9+eabOHDgAN577z387W9/O+XzuFwuuFyuwLLFIo+A6vF4gnrqh8b8cfaUeKljWM+hg3UdGjpSzxW2ClTUVyDBlADJJ3VVaEHPK3mxfNNyAIBapcbleZcH9fHwxxbMMVJzFdYKaDVaDE8YjqTwJHi9J59uK5g+u81mMyoqKiBJEsLDw3vEjwU9iRACbrcbDoeDx7aXEELAbrejqqoKkZGRkCSp2Xu6q97b7Uq6d+/ejfLycgBy0Hv37oXVagWADs1LV11dDZ/Ph6SkpCbrk5KSAs9zov379+PBBx/Ejz/+CK22beEvWLAA8+bNa7Z+5cqVCA8Pb3fcSlq1apXSIVA3YD2HDtZ1aOhIPVehqgsi6TnWla1DZUMlAGBU4ijgIFCKUoWjOrXy7S1/f6Hg5YMP+Xvy2/WYYPnsNpvNsNlsUKtDd4YDovaQJAkNDQ3Yv39/k/X+93RX9R5pV9J97rnnNukDP3XqVAByt3IhRId/BTrxca1ty+fzYfr06Zg3bx4GDx7c5u0/9NBDuPfeewPLFosFffr0wZQpUxAZGdmhmLubx+PBqlWrcN5553VrVwjqXqzn0MG6Dg3tredKWyU2l25GQngCdJrQfl18+/a3gf9nTp6J1AGpCkZzapJPQvn2ciSflgy1hglQMJOEhPKGckQZozA0cWi7ZggIxs9un88Hr9fL67s7mdfrxbp16zBu3Lg2N/RRcFOpVNBqtdBojo/ZcOJ72t8jurO1+RVUWFjY6U8eHx8PjUbTrFW7srKyWes3ADQ0NGDz5s3YunUr/vjHPwKQf60QQkCr1WLlypX4zW9+0+xxBoMBBkPz68B0Ol3QfGC2VU+MmdqP9Rw6WNehoS31LAkJxdZiaDQaGPShfe1yQUUB8g/LLY994/rizIFn9piWPLVGzaQ7iHklL0qtpUg0J2JY0jBEGjrW+BJMn93BEkdv4/F44PV6ERERwWMcAvzv6a6q6zYn3RkZGacss23btjaV89Pr9Rg5ciRWrVqFyy+/PLB+1apVuPTSS5uVj4yMxI4dO5qse+mll/Ddd9/h448/RmZmZpufm4iIKJhU2apQYatAQniC0qEo7uNNHwf+nzZ6Wo9JuCm4eXwelFpLkWZOQ25iLkx6k9IhEVGI+NV9Jerr6/H+++/j3//+N7Zv3w6fz9eux997772YOXMmRo0ahbFjx+K1115DUVERbr/9dgBy1/CSkhK88847UKvVyM3NbfL4xMREGI3GZuuJiIh6CklIOFx/GGqoodfolQ5HUVanFV9s/wIAEKYPw9TTpyocEfUGLq8L5bZyZERlYGjiUBi1RqVDIqIQ0uGk+7vvvsMbb7yB5cuXIyMjA1deeSVef/31dm/nmmuuQU1NDR5//HGUlZUhNzcXX375ZaDFvKys7JRzdhMREfVkVbYqlFvLkRieqHQoivt82+dwuB0AgAuHX4gIY4TCEVFP5/A4UGWvwoCYAchJyAn58RKIqPu1K+kuLi7GW2+9hTfeeAM2mw3Tpk2Dx+PBsmXLkJOT0+Eg7rjjDtxxxx0t3vfWW2+d9LFz587F3LlzO/zcRERESpKEhMK6QmhUmpBPBiRJwpINSwLLV4++WsFoqDewuq2oddYiKy4Lg+MHQ6vmgFhE1P3afJHUhRdeiJycHOzevRsvvvgiSktL8eKLL3ZlbERERL1epa0SFdYKxIXFKR2K4tbuX4vio8UAgNH9R2Ng0kCFI6KerN5ZjzpnHYYmDEV2QjYTbiJSTJs/fVauXIm77roLf/jDHzBo0KCujImIiCgk+CQfCmsLoVVrQ76VGwCW/Hy8lfu6MdcpGAn1dEcdR+HyunBa8mnIiMro8LS2RESdoc0t3T/++CMaGhowatQonHnmmfjnP/+JqqqqroyNiIioV6uwVaDSVon48HilQ1FcQUUBNh7cCADoE9sH4weNVzgi6qmqbFXwCR9OTzkd/aL7MeEmIsW1OekeO3YsFi1ahLKyMvz+97/HkiVLkJaWBkmSsGrVKjQ0NHRlnERERL2KV/KisLYQOrWO3V7RtJX7mjOv4TRh1G5CCJQ1lEGj1uD05NORHpmudEhERADakXT7hYeH45ZbbsHatWuxY8cO/PnPf8aTTz6JxMREXHLJJV0RIxERUa9Tbi1Hla0KceG8lrvOVoev/vcVAMBkMOHiERcrHBH1NJKQUGItgUlvQl5KHpIjkpUOiYgo4Ff9jJyVlYWnnnoKxcXFWLx4cWfFRERE1Ku5fW4cOHoAYbowtnIDWJ6/HC6vCwBwad6lMBlMCkdEPYlP8qHEUoJYYyzyUvL4QxYRBZ1O6bul0Whw2WWX4b///W9nbI6IiKhXK2soQ42jBjHGGKVDUZzX58VHGz8CAKhUKkwbPU3hiKgn8UpeFDcUIykiCSNSRiDKGKV0SEREzbQp6b799ttx5MiRNm1w6dKleP/9939VUERERL2V0+vEgdoDMOvN0Kg1SoejuG92f4OqBnlg1klZk5Aey+twqW3cPjdKGkrQJ7IPTk8+HRH6CKVDIiJqUZv6tCUkJCA3Nxfjxo3DJZdcglGjRiE1NRVGoxG1tbXYvXs31q5di8WLFyM9PR2vvfZaV8dNRETUIxXXF6PWUYuMqAylQwkKi9cfvzzt2jHXKhgJ9SROrxMVtgr0j+mPnIQc6DV6pUMiImpVm5Luv/71r/jTn/6E119/Ha+88gp27tzZ5H6z2YzJkyfj9ddfx5QpU7okUCIiop7O6raisK4QMcYYTmMEYMeRHdhVsgsAMDh5MEb2G6lwRNQT2Nw2HHUcRVZcFrLiszguAhEFvTZ/SiUmJuKhhx7CQw89hLq6Ohw+fBgOhwPx8fEYMGAAvzwQERGdwuG6w7C6regb1VfpUILC4p+btnLzuwSdisVlgcVlwZCEIRgUNwhqFaeWI6Lg16GfBqOjoxEdHd3JoRAREfVetY5aFNUXITYsVulQgkJFfQW+3f0tACDGFIPzc89XOCIKdrWOWji9TgxLHIbMmEz+SENEPUabfx602+248847kZaWhsTEREyfPh3V1dVdGRsREVGvIITAobpDcPvcHOzpmI83fQyf5AMAXDnqShh0BoUjomBWZauCR/LgtOTT0D+2PxNuIupR2px0P/bYY3jrrbdw0UUX4dprr8WqVavwhz/8oStjIyIi6hWqHdU4YjmC+PB4pUMJCk63E8s3LwcAaDVaXHXGVQpHRMFKCIGyhjJo1BqMSBmBPlF9lA6JiKjd2ty9fPny5Xj99ddx7bXyyKIzZszA+PHj4fP5oNFwyhMiIqLWFNYVQg01jFqj0qEEhc+2fYZ6Rz0A4Lyh5yHezB8jqDlJSCi1liJSH4nhScMRFx6ndEhERB3S5pbuI0eOYOLEiYHl0aNHQ6vVorS0tEsCIyIi6i0qGyqRYEpQOoyg4JN8eH/9+4HlGeNmKBgNBSuf5EOxpRixxljkpeQx4SaiHq3NLd0+nw96fdM5ELVaLbxeb6cHRURE1Bu4vC4AQJgujNMaHbNm7xoUHy0GAIzuPxpZKVkKR0TBxit5UdJQghRzCoYlDuM4CETU47X5G4AQAjfddBMMhuMDnTidTtx+++0wmUyBdcuXL+/cCImIiHqoI5YjAICYsBiFIwkOQgi8+9O7geWZ42cqGA0FI5fXhXJbOTKiMjA0cSgvySCiXqHNSfeNN97YbN2MGewSRkRE1BKLy4LD9YcBgHMJH7P9yHbsKN4BABiYNBBjBoxROCIKJg6PA1X2KvSP6Y+chBzoNfpTP4iIqAdoc9L95ptvdmUcREREvYYQAgdrD8LpdkIFTm3k995P7wX+nzFuBqd9ogCr24paZy0Gxw1GVnwWL8cgol6FP70TERF1skpbJYrqixBn4uBPfoerD2PNL2sAAAnmBJyfe77CEVGwqHPWod5Vj6EJQzEkYQgTbiLqdfipRkRE1Ik8Pg8O1B6ARqXh9aiNfLD+AwghAADXjrkWOq1O4YgoGNTYa+CRPDgt6TT0jerL3g9E1CuxpZuIiKgTHak/gnJrOeLDOfe0X62tFp9v+xwAEK4PxxUjr1A4IgoGFdYKCJXAiJQRyIjOYMJNRL0WW7qJiIg6idVtxYHaA4gyREGr1kLySUqHFBQ+3PhhYPq0y0deDnOYWeGISElCCJRZyxCuC8fwpOGcw56Iej22dBMREXUC/+BpVrcV0cZopcMJGk63Ex9t/AgAoFFrcN2Y6xSOiJTkk3woaShBpCESeSl5TLiJKCSwpZuIiKgTVNoqcajuEBJNiUqHElQ+3/456ux1AIDzhp6H5OhkZQMixXglL0obSpEckYzcxFyYDezxQEShgUk3ERHRr+T2ubG/Zj8HTzuBT/Lhg/UfBJZnjJuhYDSkJLfPjTJrGfpE9kFuYi7CdGFKh0RE1G2YdBMREf1KRXVFqLRXIt2crnQoQeXHX35EUU0RAOCMzDOQnZqtcESkBIfHgUp7JfrH9EdOQg70Gr3SIRERdSsm3URERL9CnbMOB2oPIMYYA41ao3Q4QUMIgbfWvhVYnjl+pnLBkGKsbitqnbXIistCVnwW5+AmopDETz4iIqIO8kk+FNQUwOl1coqwE+QfysfO4p0AgIFJAzF24FiFI6LuVuesg81jQ25CLvrH9odaxfF7iSg0MekmIiLqoJKGEhQ3FCPZxMHBTvTGD28E/r9p4k2cgznEVNur4ZW8OC3pNPSN6sv6J6KQxqSbiIioA6xuK/bX7IdJZ4JOo1M6nKCyq2QXNh7cCABIj03H5JzJCkdE3UUIgQpbBXQaHfJS8pBiTlE6JCIixbGfDxERUTsJIVBwtAAWlwUxxhilwwk6b/34VuD/G8bfAK2Gv/GHAklIKLGWIEwXxoSbiKgRngWJiIjaqcxahsN1h5FoSmS32RMcrDyI7/d8DwCIN8dj6ulTFY6IuoNX8qLMWoa4sDgMTxqOKGOU0iEREQUNJt1ERETtYPfYsa96H4xaI+fkbsHba98O/D9j7AzotZweqrfz+DwotZYi1ZyKYYnDYNKblA6JiCioMOkmIiJqI3+38qPOo+gb2VfpcIJOaW0pvt7xNQAgMiwSV4y6QuGIqKs5vU5U2CqQGZ2JIQlD+EMUEVELmHQTERG1UZm1DIfqDiHJlMRu5S14d9278Ek+AMA1Z16DcEO4whFRV7K5bTjqOIpBsYOQHZ/NAQWJiFrBpJuIiKgN/N3K9Wo9W/NaUGOtwX+3/BcAEKYPwzVnXqNwRNSV6p31aHA3YEjCEAyMHQiNWqN0SEREQYtJNxER0SlIQsK+mn2oddaiT2QfpcMJSh+s/wAurwsAcMXIKxAdHq1sQNRlAnNwJ5+GjKgM9vogIjoFJt1ERESnUNpQytHKT6LOVocPN34IANBpdLh+3PUKR0RdwT8Ht1ajxYiUEUg1pyodEhFRj8Ckm4iI6CQaXA3YW7UXYdowditvxfvr34fD7QAAXJZ3GRIjExWOiDqbJCSUWkth1psxPGk44sPjlQ6JiKjHUCsdABERUbDyST7sq9kHq9uKuPA4pcMJSnX2OizdsBQAoNVocePEGxWOiDqbV/Ki2FKMOGMcRqaMZMJNRNRObOkmIiJqxaG6QyiqL0JKRIrSoQStxesXw+62AwAuGXEJkqOSFY6IOpPb50aZtQxp5jTkJuZyDm4iog5g0k1ERNSCGnsN9tXsQ7QxmlMhtcLisGDJhiUA5FbumyferHBE1JkcHgcq7ZXIjM5ETkIODFqD0iEREfVITLqJiIhO4PQ6sadqD7ySF5GGSKXDCVqL1y+GzWUDAFx8+sVIiWaPgN7C6rai1lmLrLgsZMVnQavmV0Yioo7iJygREVEjQgjsr9mPSnslpwc7iQZHAxZvWAwA0Kg1bOXuRWodtbB77chNyEX/2P5QqzgEEBHRr8Gkm4iIqJFiSzEO1h5EkimJycZJLP55MaxOKwBg6ulTkRrD6aN6g0pbJQBgRPIIpEemc4o8IqJOwKSbiIjomDpnHfZW70W4LpzTg52E1WnF4p/Zyt2bCCFQZi1DuC4cuYm5SIpIUjokIqJegz/hExERAXB5XdhTtQd2rx2xYbFKhxPUFv+8GA3OBgDAhaddiPTYdIUjol/DJ/lQbClGpCESeSl5TLiJiDoZW7qJiCjkCSGwr2YfyqxlSDczgTyZens93lv3HgC5lXvWWbMUjoh+DY/Pg1JrKVLNqchNzEWEPkLpkIiIeh0m3UREFPKOWI4EruPWqDVKhxPU3vnpncCI5ZeMuISt3D2Y0+tEha0CmdGZGJIwhJdUEBF1ESbdREQU0mrsNdhTvQcmnYlJxylUN1QH5uXWaXSYNYmt3D0VpwQjIuo+/IQlIqKQ5fA4sLtqN9xeN1LNHH37VN788U24PC4AwFVnXIXkqGSFI6KOqHXUwuF1cEowIqJuwqSbiIhCklfyYm/1XlTbq5EeyS7Sp1JWV4Zlm5cBAIw6I26aeJOyAVGHVFgroFarcXry6ZwSjIiomzDpJiKikCOEwIGjB3Co7hBSIlLY0tcGi1YvgtfnBQBcN+Y6xEXEKRwRtYckJJRZy2DSmTAsaRgSTYlKh0REFDKYdBMRUcgpaSjBvpp9iAuLg06jUzqcoHe4+jC+2P4FACDCGIGZ42cqHBG1h1fyorShFAnhCchNykW0MVrpkIiIQgqTbiIiCik19hrsqtoFo9YIk96kdDg9wqvfvwqf5AMAzBw3E5FhkQpHRG3l8rpQbitHemQ6hiYM5WueiEgBTLqJiChkWN1W7KzcCY/XgxRzitLh9Aj7yvdh5c6VAIAYUwyuG3OdwhFRW9ncNlQ7qtE/pj9yEnKg1+iVDomIKCQx6SYiopDg9rmxq3IXap21SDdz4LS2+uc3/wz8f/PEmxFuCFcwGmqremc9GtwNyEnIwaDYQZx/nohIQUy6iYio1/NJPuyt3otiSzFHbG6HjQc3Yt3+dQCA5KhkXDnqSoUjoraotlfDK3lxWvJpyIjK4OudiEhhTLqJiKhXE0Kg4GgBDhw9gJSIFGjVPPW1hSRJeH7l84HlO869AwadQcGI6FSEECizlsGoNSIvJY+XUBARBQl+8yAiol6tqL4Iv9T8gtiwWBi0TBrb6usdX+OXsl8AAFkpWfjtsN8qHBGdjH+E8tiwWAxLGobYsFilQyIiomOYdBMRUa9Vbi3HrqpdMOlMiNBHKB1Oj+HyuPDSty8Flu+ecjfUas5lHqzcPjfKrGVINaciNzGXr3UioiDDpJuIiHqlGnsNdlTsgBpqzkvcTks3LEV5fTkAYNzAcRjdf7TCEVFr7B47qu3yCOVD4oewNwcRURBi0k1ERL2OxWXBjoodcHldvK61nersdXjjxzcAACqVCndNuUvhiKg1/hHKhyQM4QjlRERBjEk3ERH1Kja3Df8r/x/qXfVIM6cpHU6P88YPb8DqtAIApp4+FQOTBiocEbWkylYFCRJHKCci6gGYdBMRUa/h8Diwo3IHqh3VSDOnMRFpp+Kjxfhw44cAAIPOgNvPuV3hiOhEkpBQbi1HmDYMuUm5SI5IVjokIiI6BSbdRETUK7i8Luys3InShlKkm9OhVnHgr/Z6fuXz8Pq8AIDpY6YjKSpJ4YioMf8I5fHh8chNzEVMWIzSIRERURsw6SYioh7P4/Ngd9VuHLEcQZo5jde2dsCmg5vw/Z7vAQBxEXG4ccKNCkdEjTm9TlTYKtAnsg9yEnJg0puUDomIiNqISTcREfVoXsmLXVW7UFhXiNSIVGjVPLW1l9fnxTNfPRNYvnPynYgwctqpYGF1W1HrqMXA2IHIjs+GXqNXOiQiImoHfjMhIqIeyyt5sbtqNwprC5FsSoZOo1M6pB5p+eblOFB5AACQk5qDqadNVTgi8jvqOAqn14mhiUPRP6Y/e3EQEfVATLqJiKhH8kk+7Knag4KjBUgyJXF+4g6qs9fhle9fCSzfd+F9UKt5PbzShBCosFVAq9ZiRMoIDgxIRNSDMekmIqIe58SE26g1Kh1Sj/Xq96/C4rAAAC4YfgGG9xmucETkk3wos5YhyhCF3KRcxIfHKx0SERH9Cky6iYioR/En3PuP7keiKZEJ969QUFGAZZuWAQDC9GG467y7FI6I3D43yqxlSDGnIDchF2aDWemQiIjoV2LSTUREPYZX8gZauJlw/zpCCDzz1TOQhAQAuGXiLUiITFA4qtBmc9tQ46hB/5j+GBI/hJdMEBH1Eky6iYioR/D4PNhTzS7lneXb3d9ic+FmAEBaTBqmj52ucEShrc5ZB5vHhpyEHAyMHcgB04iIehEm3UREFPTcPjd2V+3GwdqDSDYlswXwV7I6rXj2q2cDy/ecfw8MOh5TJfgHTNOoNTg9+XT0iezDAdOIiHoZJt1ERBTUXF4XdlbuxOH6w0iJSOEcxZ3g1e9fRVVDFQBgwuAJmJQ9SeGIQpN/wLRIQyRyE3ORYGL3fiKi3ohJNxERBS27x46dlTtRbClGakQq5+HuBHtL92LphqUAAIPOgDkXzmHLqgICA6ZFpGBo4lBEGiKVDomIiLpIUEzE+dJLLyEzMxNGoxEjR47Ejz/+2GrZ5cuX47zzzkNCQgIiIyMxduxYrFixohujJSKi7mB1W7G9fDuKLcVIM6cx4e4EPsmH+Z/NDwyedtuk25Aak6pwVKHH6raizFqG/jH9kZeSx4SbiKiXUzzpXrp0KWbPno1HHnkEW7duxcSJE3HBBRegqKioxfI//PADzjvvPHz55ZfIz8/HOeecg4svvhhbt27t5siJiKir1DnrkF+ajwprBdLN6dCq2TGrMyzbtAy7S3cDAPon9seMcTMUjij01DpqYXFZMDRhKIYlDuP4BEREIUDxbzELFy7ErFmzcOuttwIAnnvuOaxYsQIvv/wyFixY0Kz8c88912R5/vz5+PTTT/HZZ59hxIgR3REyERF1oSpbFf5X8T/YPDakR6az63MnqbJU4V/f/iuw/PDUh6HVKP41IGQIIVBuLYdOo8PpyafztU1EFEIUPdu63W7k5+fjwQcfbLJ+ypQpWLduXZu2IUkSGhoaEBsb22oZl8sFl8sVWLZYLAAAj8cDj8fTgci7nz/OnhIvdQzrOXSwrltW2lCKPVV74JN8SIlIgZAEBITSYXWY5JOa/FXSwq8XwuayAQAuGXEJhqcPD4q4eouT1bVX8qK8oRwxYTHITshGXFgcvF5vd4dInYCf3aGDdR0aTqznrqpvRZPu6upq+Hw+JCUlNVmflJSE8vLyNm3j2Wefhc1mw7Rp01ots2DBAsybN6/Z+pUrVyI8PLx9QSts1apVSodA3YD1HDpY160rRanSIXSa8u1tO6d1lfzKfKzaJb/WzDozrkq4CqVbes/xDSat1bUaatSjHhuwoZsjoq7Az+7QwboODf56ttvtXbL9oOhXdmL3KiFEm7pcLV68GHPnzsWnn36KxMTEVss99NBDuPfeewPLFosFffr0wZQpUxAZ2TMGL/F4PFi1ahXOO+886HQcTKi3Yj2HDtb1cR6fBweOHsCB2gOIMkQhwhChdEidRvJJKN9ejuTTkqHWKDOMitVpxatrXg0s33PBPcg+PVuRWHqzlura6rKi3lWPzOhMDIwbyOnuegF+docO1nVoOLGe/T2iO5uiSXd8fDw0Gk2zVu3Kyspmrd8nWrp0KWbNmoWPPvoIkydPPmlZg8EAg6H5QCU6na7HvYl6YszUfqzn0BHqde3wOLDn6B4crj+MJHMSjFqj0iF1CbVGrVjS/cI3L6CyoRIAMGbAGFycdzGvJe5C/rquslXBJ3wYljIMmTGZUKsUH7uWOlGof3aHEtZ1aPDXc1fVtaJnAL1ej5EjRzbrtrFq1SqMGzeu1cctXrwYN910Ez744ANcdNFFXR0mERF1Af8I5UX1RUiNSO21CbeSfj7wM/6z5T8AgHB9OP5yyV+YcHcxn+RDcUMxdBodRqaOxIDYAUy4iYhCnOLdy++9917MnDkTo0aNwtixY/Haa6+hqKgIt99+OwC5a3hJSQneeecdAHLCfcMNN+D555/HmDFjAq3kYWFhiIqKUmw/iIio7coayrCrchfsXjvSI9OZlHQBm8uGv336t8DyXVPuQnJ0soIRhYayhjIkRSVhaMJQRBn5vYSIiIIg6b7mmmtQU1ODxx9/HGVlZcjNzcWXX36JjIwMAEBZWVmTObtfffVVeL1e3HnnnbjzzjsD62+88Ua89dZb3R0+ERG1g0/yobC2EL/U/AKtWos0c5rSIfVaL656EeX18g/TI/uNxBUjr1A4ot7N5pZHhu8b3RdDk4ey5wYREQUonnQDwB133IE77rijxftOTKRXr17d9QEREVGnc3qd2Fu9F4W1hYgNi0WEvvcMmBZsNhduxsebPgYAGHVGPHrpo1Cr2Zugq9TYa+BwOwAAQxOYcBMRUVM8AxMRUZerddRic8lmFNYWIsmUxIS7CzncDvz1078Glv84+Y9Ij01XMKLeSxISShtKARUwInkEAECj1igcFRERBZugaOkmIqLeSQiBYksx9lTvgdPr5PXb3WDh1wtRUlsCADi97+mYNnqawhH1Th6fB2XWMiSEJyAnMQdmrRlbsVXpsIiIKAgx6SYioi7h9rmxv2Y/Co4WIFwXzuu3u8HqvavxSf4nAORu5f932f+xW3kXsLltqHZUo190P2THZyNcFw6Px6N0WEREFKSYdBMRUaerddRib/VelDaUIsmUhDBdmNIh9XrVDdVNRiv/82//jL5xfRWMqHc66jgKp9eJnIQcDIwdCK2aX6WIiOjkeKYgIqJOIwkJxZZi7K3eC6fXiT6RfXiNazcQQuDx/zyOOnsdAGBS9iRcNvIyRWPqbSQhodxaDoPWgBEpI5BmTuOc50RE1CZMuomIqFM4PA78UvMLDtcdRoQ+gt3Ju9HSDUuxrmAdACAuIg6PXvIoE8JO5Pa5UWYtQ2J4InIScxAbFqt0SERE1IMw6SYiol+twlqBvdV7UeOoQbIpGQatQemQQsaBygN4YdULgeW5l89FtClauYB6GavbihpHDTKjM5Edn81LJYiIqN2YdBMRUYe5fW4crD2IgpoCqFVq9I3syxbWbuT2uvHoskfh9roBANeeeS3GDhyrcFS9R7W9Gm6fG7mJuRgQM4CXShARUYcw6SYiog6ptldjX80+lFvLER8WD5PepHRIIWfh1wuxr3wfAGBA4gD86bw/KRxR7+CTfCizlcGkNWFk6kikRKTwxyQiIuowJt1ERNQubp8bhbWFOFB7AD7Jh3RzOlsAFfD1/77Gx5s+BgAYtAY8cdUTMOjYrf/XcnqdqLBVICUiBUMShiDaGK10SERE1MMx6SYiojartldjf81+lFnLEBcWhwh9hNIhhaRDVYfwxGdPBJbnXDQHA5MGKhhR72BxWVDvqsfA2IHIisvi2ARERNQpmHQTEdEpubwuFNYV4sDRAxBCsHVbQQ63A3M+nAOH2wEAuPj0i3Fp3qUKR9WzCSFQYauASqXC8MTh6BfTD2qVWumwiIiol2DSTURErfInI/tr9qPSVon48Hi2bitICIEnP38SBysPApCv437gogcUjqpn80pelFnLEG2IRk5iDhJNiUqHREREvQyTbiIiapHVbcXB2oM4VHcIWpUWfaP6svVPYZ9u+RRfbP8CABCuD8ffr/k7jHqjwlH1XDa3DdWOavSJ7IOchBwOBkhERF2CSTcRETXhlbwosZSg4GgBLC4LEk2JMGqZ2Cltd8luPP3l04HlRy55BP3i+ykXUA9Xba+Gy+dCTkIOBsYOhFbNr0RERNQ1eIYhIiIActflGkcNCo4WoKyhDGa9GX0i+3CqpCBQ3VCN+5bcB5fXBQC46oyrcP6w8xWOqmfyST6UWctg0pkwMmUkUs2pfI0TEVGXYtJNRESwuW04WHsQRfVFkISEVHMqW/6ChMvjwn1L7kOlpRIAcFrf03Dvb+9VOKqeyel1otxajtTIVOTE5yDKGKV0SEREFAL4jYqIKIR5fB4cqT+Cg7UH0eBuQHx4PMJ14UqHRccIIbDg8wXYWbwTAJAUlYSnrnkKeq1e4ch6nlpHLWweG7LjszEobhD0Gh5DIiLqHky6iYhCkCQkVFgrcODoAVTaKxFliGJX8iD0wfoP8Pm2zwEABp0Bz173LOIi4hSOqmfxST5U2Cqg1+qRl5KH9Mh0vs6JiKhbMekmIgohQggcdRzFwdqDKG0ohV6j55zbQWp9wXo8v/L5wPLcy+YiOyVbwYh6HqfXiQpbBZIjkjEkfghiwmKUDomIiEIQk24iohBR76zH4frDOFJ/BD7hQ6IpkV1sg9SBygN46KOHIAkJADDrrFk4L/c8haPqWeqcdbC6rRgYOxCD4wZzBH4iIlIMk24iol7O5rbhiOUIDtcdhsPrQHxYPMJ0YUqHRa2otFTirnfvgtVpBQBMyp6E35/ze4Wj6jkadyc/Pfl09Inqw/nliYhIUUy6iYh6KYfHgRJLCQrrCtHgbkBcWBziw+OVDotOwuq04q737kKFpQIAMCR1CP56xV+hVjNpbAt/d/IkUxKGJAxBbFis0iEREREx6SYi6m2cXidKLaUorCtEvase0YZo9I3sy8GjgpzH68H9S+5HQUUBACAtJg3PXf8cwg0cTb4t/KOTD4odhMFxg2HQGpQOiYiICACTbiKiXsOfbB+qP4Q6Zx2iDFFMtnsISZIw7z/zsKlwEwAgKjwKL8x4gSOVt4FP8qHcVg6j1oi8lDykRaaxOzkREQUVJt1ERD3cicm2WW9mst3D/PObf+LrHV8DAAxaA/4x/R/IiM9QOKrg5/A4UGmvREpECoYkDEG0MVrpkIiIiJph0k1E1EPZPXaUNZThUN0h1LvqEamPZLLdA72+5nW889M7AAC1So0nrn4Cw/sMVziq4CaEQI2jBm6fG9lx2RgYN5Aj8RMRUdBi0k1E1MM0uBpQ2lCKovoiNLgb2I28B3t/3ft4+buXA8tzLpqDs7PPVi6gHsAreVHWUAazwYxhScOQEpHC1z4REQU1Jt1ERD2AEAJ1zjqUNpSi2FIMm8fGAdJ6uI83fYx/rPhHYPmu8+7CVWdcpWBEwa/B1YBaZy3SI9ORHZ8Ns8GsdEhERESnxKSbiCiISUJCtb0aJZYSlDaUwuPzICYshlN/9XCfb/scT37+ZGD5d2f/DjdMuEHBiIKbJCRU2iqhUqkwLHEY+sX0g1bNrzBERNQz8IxFRBSE3D43qmxVKKovQpW9CgAQa4xFmC5M4cjo11q1cxUe/8/jgeUbJ9yI286+TcGIgpt/7u2E8ARkx2cjwZSgdEhERETtwqSbiCiIWN1WVFgrcKT+CGqdtTBoDEgMT4ROo1M6NOoEX/3vK8z9ZC4kIQEArjnzGvxx8h95iUArauw1cHgdGBQ7CIPiBsGoNSodEhERUbsx6SYiUpgkJBx1HEVpQynKrGWwu+2I0EcgzZwGjVqjdHjUST7Z/Anmfz4fQggAwGV5l+HPv/0zE+4WeHwelFvLYTaYMTJ1JNLMaTxORETUYzHpJiJSUImlBGX2MlTZqyCEQLQxGvFRvF67t1myYQkWrlgYWL76jKtx/4X3Q61WKxhVcKp31qPeVY++UX0xOG4wB0sjIqIej0k3EVE3koSEOmcdSmpLAABby7bCaDCyC3kv9tH+j/D+L+8HlmeOm4m7ptzFltsTeCUvKmwVMGgMOC3pNGREZ7CnBxER9QpMuomIuoHD4wiMQl5tr4bX44UKKqRFpkGjZWLRGwkh8NK3LzVJuH939u9w29m3MeE+gdVtRY2jBinmFGTHZSMmLEbpkIiIiDoNk24ioi7ik3w46jiKClsFyhrK0OBugFFjRGxYLHThOpSilMlXL+XxevDX//4VX27/MrDurvPu4rRgJ/BJvsBUYEMThqJ/TH/2+CAiol6HSTcRUScSQsDisqDaXo1iSzHqXfUQQiDKEIW+kX0DSbbkkxSOlLqK1WnFnKVzsPHgRgCACircf8H9mDZmmsKRBReb24ZqRzUSwxORFZ/FqcCIiKjXYtJNRNQJ7B47auw1KLOWodpeDZfXBZPOhCRTErRqftSGivL6ctz93t04UHkAAGDQGnDPaffgijOuUDiy4CEJCZW2SgghkB2XjQGxA2DQGpQOi4iIqMvwmyARUQe5vK5A9/FKWyWsbisMGgOijdEwmjifcKjZV74Ps9+fjUpLJQAgKjwKz17zLOKrOBq9n91jR5W9CgnhCRgcNxhJEUlKh0RERNTlmHQTEbWD2+dGraMWVfYqVFgr0OBugBpqRBmjEBsZy2u0Q9Q3u77BvP/Mg8PtAACkx6bjhRkvID06HaVVpQpHp7wTW7f7x/aHUcsfpoiIKDQw6SYiOgW3z406Zx2qbdUos5bB6rYCKiBSH4k0cxrUKs61HKp8kg+vfPcK3vzxzcC63PRcLLxuIWIjYnntPuRrt6vsVUg0Hbt2OzyBP04REVFIYdJNRNQCl9clJ9r2apRby+VEG0CEPgIpESmcP5jQ4GjAX5b9BT/t/ymw7oLhF+CRSx6BUcdW3CYjkycORWZ0Jq/dJiKikMSkm4joGIfHEUi0/ddoQwWYdWYm2tTEwcqDuG/JfSiqKQIAaNQa3D3lblw35jq24gKwuCyoddYiOSIZg+MGIz6c17UTEVHoYtJNRCFLCAGr24o6Zx2qbFWodlTD7rZDrVLDbDAj1ZzKruPUhBAC/936Xzz95dNwepwA5AHTFly9AKP7j1Y4OuV5fB5U2Ctg1BgxPHE4MqIzOO82ERGFPCbdRBRSvJIXFpcF9c56lFnLUO+sh8PrgEFjQIQ+goOhUausTiue/PxJfL3j68C6wcmD8fS1TyMtJk3ByJQnhECtsxZWtxXpkekYFDcI0cZopcMiIiIKCky6iajXs3vsqHfWo9ZRiwpbBaxuK7ySF2HaMEQaIpFoSlQ6RApyu0t24+GPH0bx0eLAustHXo4///bPMOpD+/ptp9eJSlslIg2RGJk6EmnmNF6KQURE1AiTbiLqdfyt2RaXBZW2StQ6a+FwO6BSqxChi0CiKRFaNT/+6NR8kg/vr3sfL333Erw+LwDAZDDhL5f8BeflnqdwdMryST5U2asgCQkDYweif0x/mPQmpcMiIiIKOvzWSUQ9niQk2Nw2WFwWHHUcRZW9Cja3DV7JC6PWCJPOhLjIOHYbp3Y5VH0Ij//ncfzvyP8C64amDcX8q+eHfHfyOmcd6l31SI5IxsDYgZwGjIiI6CSYdBNRjyOEgMPrCFyb7R9p3Ol1QqPSwKQ3sTWbOswn+bD458V4+duX4fK6AAAqlQozx83EHefeAa0mdF9X/q7kEfoInJ50OvpE9eFAaURERKcQut8ciKhHcXgcaHA3wOK0oNJeiQZXAxxeB1RQ8dps6jSHqg/hr5/+FduLtgfW9Yntg8cufwyn9z1ducAU5pW8qLJVQUCgf0x/9I/pD7PBrHRYREREPQKTbiIKOv6W7AZXAxpcDaiyV8HitsDhdgAAwnRhCNeFIy6MXcapczg9Trz5w5t456d34PF5AMit29eeeS3uPPfOkB0srfGo5MkRyRgQO4BdyYmIiNqJSTcRKc5/TbbVbYXFZUGNowYNrgY4vU4IIWDUGhGuC0dsZCznzaZOt3bfWjz95dMoqS0JrEuPTcdjlz2GERkjFIxMWVa3FTWOGkQZojAydSRSzam8ZIOIiKgDePYkom7n8XlgdVth89hQ56xDjb0Gdo8dLq8LKpUqkGSzJZu6UlldGRZ+vRDf7/k+sE6r0WLmuJmYddaskG3ddnqdqLJVIUwXhpyEHGREZSBMF6Z0WERERD0Wk24i6lKSkODwOGDzyC3ZRx1HUe+sh8PjgEfyQKPSIFwXjkhDJAzhBibZ1OWsTive+OENLNmwBG6vO7B+VOYoPHDRA8hMyFQwOuV4fB5U2augggr9YvohMzoTUcYopcMiIiLq8Zh0E1GncnqdsLltsHnkKbxq7DVweB1weV0QENCr9QjThSHBlMCuqtStvD4vlm1ehtdWv4Z6e31gfVxEHGafPxu/HfbbkPzRxyt5UWOvgUfyIMWcgszoTMSHx4fksSAiIuoK/MZLRB3m8rpg89hg99gDrdj+qbskIUGj0iBMGwaz3oz4MH6JJ2VIkoRvdn+DV757BUU1RYH1eq0e1425DjdPvBkRxggFI1SGT/Kh1lkLh8eBxIhEZEZnIikiieMmEBERdTIm3UR0SkIIuHwu2D32Jgm2zW2D0+uEV/JCrVLDqDUiTBuGaEM0NGqN0mFTiJMkCd/t+Q6vrX4NBysPNrnvguEX4I5z70BKdIpC0SlHEhJqHbWweWyIDYvF0MShSI5IZs8TIiKiLsIzLBE14b8G2+6xw+F1wOKyoM5RB7vXLrdgS1JgsDOj1ohIQyS/rFNQkSQJq/euxmurX0NBRUGT+/Iy8nD3+XdjaNpQhaJTjj/ZtnqsiDXGIjshG8kRydBr9EqHRkRE1KvxmzJRiPK3Xjs8Dji8Djg8DtQ562BxWeDyueDyugJdxP0JNluwKZi5vW58uf1LvLfuPRyqPtTkvuF9huN35/wOZ/Y/M+Quc/BJPtQ56wLJ9sj4kUiOSIZBa1A6NCIiopDApJsoBLh9bjg8Dji9zkDrtcVpCQxw5hVeQAA6jQ5GrZHXYFOPYnFYsGzTMizZsAQ11pom9w1NG4rbf3M7xgwYE3KvZ6/kRZ2zDnaPHbFhsciKz2KyTUREpAAm3US9hBACbp87kFg7vU5Y3VbUOerg9Drh8rngkTyAALRqLfQaPQxaA7uHU49VUFGAjzd9jC+3fwm7297kvryMPNww4QaMHzQ+5JJtj8+Do46jcEtuxIXFISchB0kRSexGTkREpBB+0ybqYXySD06vM5BIO71ONLgaUO+qh8vrgtvnhsfnAQBo1BoYNAYYtAZE6COg0+gUjp7o1/F4Pfh+7/f4aONH2Hp4a5P71Co1zhlyDmaOn4nc9FyFIlSO0+vEUcdRCAgkhCcgIzoDiaZE/qhGRESkMJ6JiYKQfzAzf1Lt8rpgc9vQ4GmAzW2D2+eGy+eCkAQAuVu4XqOHXqOHWW9mck29zoHKA/h86+f48n9fNutCHqYPw9TTpuL6cdcjPTZdoQiVIYSA1W1FrbMWeo0eqeZU9Inqg/jweE79RUREFCSYdBMpxCf5AgOW+f9anVYAwE9FP8Gj8sDtdcMn+QAVoIa6SWIdq47loGbUq9Xb67Fy50p8tu0z7C7Z3ez+zIRMXH3G1bjwtAtDbp5t//XaNo8NZr0ZWXFZSDGnINoYHXLd6YmIiIIdk26iLuK/xtrlk7t8+5Nrq9sKm9sGh9cBj88Dt+94Yq3yyV+WJSEhXBeOaEM0u4ZSSGlwNGDNL2vwza5v8POBn+H1eZvcr9VocXb22bjqjKswst/IkEsw7R47ah21EBCINkYjKz4LiaZEhOvClQ6NiIiIWsFv80QdJAkJbp+7yc3llafgavA0wOFxBK6v9kpeCIjAIGY6jQ46tQ4mnQnRxuOJteSTUIpSRBmjoNawayiFBqvTitV7V+PbXd9i/YH1zRJtAMhOycbU06fit8N/i+jw6O4PUkFeyYt6Zz1sHhvCdGHoE9UHqeZUxIXH8Uc5IiKiHoBna6IWCCHgkTyBlujGN5vHBrvbLrdUHysTSKpxvBu4TqODUSNPv6VT60KuRY7oZEpqS/DTvp+wdv9abDq4KTD4X2OJkYmYPHQyLj79YgxKHqRAlMoRQqDB3QCLywKVSoUoQxQGxg5EgikBZoNZ6fCIiIioHZh0U8iRhASPzwOP5Am0RLt9bngkD+weOxweB+wee6CMV/LCJ3yBx2tVcku1Vq1lUk3URl6fF9uKtgUS7cKqwhbLJZgTMHnoZJw79FwMTx8OtTq0enzY3DZYXBZ4JA/MejP6x/RHckQyYsM4hgMREVFPxaSbeo3GyfSJf91eNxxeOZl2ep3wSt5AQi0JCQCgggpqlRo6tU7u/q3RIUwXBp1axy+7RO0kSRL2V+zH5sLN2HxoM7Yc2gKby9Zi2cTIRPxmyG8weehkDO8Teom23WOHxWWBy+eCSWdCijkFyRHJiAuPg1FrVDo8IiIi+pWYdFNQ83fz9kreQDfuxl26XT75GmqH1wGX1wWv5A3c/IOTQQAqlQpatTZwM2gMMOlM0Kq1TKiJOoFP8uFg5UHkH8pH/qF8bDm0BfWO+hbLqlVq5KbnYsLgCRg/aDwGJw8OqZ4iQgjYPXY0uBvg9rkRpgtDgikBKREpiAmLQYQ+tEZiJyIi6u2YdFO3EkLAJ3yBpNl/8yfWXskLp8cpJ9NeeSAyr0/u3u2/XwgRSKbVKjU0ak0gmdZr9AjXhQeWiahr1NvrsaN4B3Yc2YEdxTuwq2RXqy3ZABBjisHo/qMxYfAEjB04NiQHQ3N4HLC6rZCEhDB9GJIikpBkSmKiTURE1MsxK6EO8yfQjVuWGyfS/pt/qiyn1ykn0ZIXXiGX998Cjs1H7W+B9o/0HaYOg0YlL4dSixhRMKiz12Ff+T78UvYLfin7BXvK9uBw9eGTPiYyLBJ5GXkYlTkKozJHYUDigJB67woh4PA6YHVYAQCVtkpEGCOQGZOJuPA4RBujOc0XERFRiGDSTRBCBAYLa5w8n7js8XmOzzl97K9P8sEnjifP/gHHhBCBL9hqlRoalSaQRGtUGug1+kASze7dRMHBJ/lQVleGgooCOcEul28V9RWnfGyCOQHD+gzD8D7DMSpzFAYlDQq597bT64TdY4fdY4cQAmG6MEQaI1GNapyZfibiTHHQaXRKh0lERETdjEl3LyAJqWny28Jff/LsEz64vceSZskNt9cdGEzMK7yQpGPbgk/uxi3PghVIoDUqTZMu3f4EWq1SB5ZDqTWLqCdyeVw4cvQICqsK5Vt1IQ5VHUJRTRFcXtcpH6/VaJGVnIXhfYbLiXb6cCRFJYXUe18SUiDJdnqdkIQEo9YIk96EPlF9EGWIQqQhEjro8NX2rxAXxoSbiIgoVDHpDmJVtiq5NVn44PHIc9juq9kHn0q+Jtrj88Atya3NkpDgE75AAu5fhkDg+meojo/Q7U+SG/+vV+kDLdL+9UTUM1mdVhTXFqP4aDFKaktQUlsS+L+sriwwav+pmAwmDE4ejKzkLGSlZGFw8mD0T+gPnTZ0EkghBNw+eQYEp9cJj88DlUoFo84Ik86EjOgMRBmiEKGPgElvavLZ6f/sJiIiotDFpDtIuX1u7KzciTpnHVRQAZKcMBfUFECrO54s+1ue/Ulz43Wh1rWTKFS4vW5UNVSh0lKJivoKVFgqUGmpDCyX1Ja0OnJ4azRqDfrG9UW/+H7ITMjE4OTByE7JRmp0akhN4SUJqck4FB5JTpr1Gj2MWiNSzamICYuBSWeCSW9CmDYspFr4iYiIqP2YdAcxr+RFbFgsIvQRkHwSSlGK1MhUqDWh8wWYKFRIkgSLw4KjtqPHb1b5b62tFjXWmkCiXWOt6fDzmAymJsl1ZkImMuMzkR6bDq0mdE4JPskHj+QJJNhuyQ0hyWNRGLSGQIIdbYxGmC4MJp0J4bpw/phJRERE7RY637B6EEkC9u0DfikEoiOBrEylI+oekgCOHAGsViAiAujTB1AHUQNSR+LzeIAvvgTKy4HkZOCiCwHdCb1ym2w3HGjtK317nj9Yj2Vb4mpcxnRscGebveXy/rINFqChATCbAXPk8XKSAIoOAwUH5PIDBwDpfYCSkpZjaGl7ERHy+gMFQE0NEB8PDBwI9M1o+Zj6JIF9BxzYtc8Ch7cBsUkWGMwWHC6x4HBZAxweC/QmCySNBZUVFbBvsuOo7Sjq7HVNR/LvILVKjaSoJKTFpCEtJg3pMelIi5X/T41OR311JGw2VYv7frio+XHxH5P6euDgQbmskIBwE6DRyMe08bE48Zj37y/fZ7XKx9QUAdisx48tcPy+xusa1znQ8uumpdcTcPz5fZIXujAPwk1uGCM8iE+SL8cpLwMcDjWizQb0S9fLo4kbohGuD4dRa0SYNgxhurBefYmNJAEFBXK9RkXJr+kQ6tBAQYCvQSIKJUGRdL/00kt4+umnUVZWhqFDh+K5557DxIkTWy2/Zs0a3Hvvvdi1axdSU1MxZ84c3H777d0YcdfZuhV4+21g116g3AgYtcDADGDqRUBvnsV17y/A558DhwoBlxsw6IF+mcDUqUB2ltLRdSy+N98E3n9fTggkSf4y8c8XgeuvB26+ueXtmk3AvdOBffuB7OyOPX+wHsu2xNW4TF2dfAOA6Gj51ri8v+zOnUBFBeB2ATq9/ONGbq58+/EHYPt2wHps+miDHjCZgMgo+ccPfwwXXOiF2+fCF185sWufHVW1Nnh8DkBrg6S2wyPZIantEBo7VDobdEYHYhNtSO1rh0bvgM1lg9VlRa21AQ0OC4TK2zUHUWgQro5HWlwSMpISkRiZiKTIJCRFJQX+j4uIa7HFeu8vwOtLWj7+QMt1k5srH9/Nm4DiEsDjlhNdIQCVCtDrgJgY4LTTgJk3yNt5953jx1w6NqyEXgeo1IDXA3i9gFYLaHWAv9OOT5ITeZVaXqfTHa/zqCi5TH398dgyMgVyhvqwY5cXhw574fR6oNd7ERHlgaVBwpESAZsV8Lq1UEk6hBt0SE+OQt/kCGiFCfU1BngcRoTrjcjJMuDmGzUYPKJrqiwY+c8ze/YATidgNAJDhgA33giMCKHjQMrha5CIQo3iSffSpUsxe/ZsvPTSSxg/fjxeffVVXHDBBdi9ezf69u3brHxhYSEuvPBC3HbbbXjvvffw008/4Y477kBCQgKuvPJKBfag82zdCjz+OFBdDaSkA9pYwG0H9u4BjlYBd/Ts3WvV3l+Afy+SE6zERCAsDHA45P0uLwNuvU35ZLG98b35JvDaIjnBMOjlhFuSgHqLvB4Axo5rvl3vsTGX3nkLuOHm48llW58/WI/liXEZjQJ2pxe79npxpMyD6TM88EoefLDYi/oGL3RGD6o8Hrh0HkDlhcvngdB7UV7owf/e8CBnmBc7dnpQW++BTXghJbqh0bngEi4c0LhwqNCJZQUuSHBBlemCWueCpHLBKjlRo3ZBpXNBq3NBUrnxc70TS5Y0amFOPXZrhQDgAlAGoKy4hQLt7FGgUWth0sbCVhsL4YyFToqF1x4L4YiF5IwF3LGI0MVC64uFVkQjNlqL1FTgpiltr8uTvS7275PL+HxN79uSD3yzSk6Qjx6V7/dJCMxoIAC4PfJrev16uZUcAEpL5Ne70Sj3GPB45O1p1IBaIyfXXh8A5/HkXagkmCJ8sDp8gMoHY5gPkt0HmHzYud0LqHwYOEggLlXA5QI27AZW/KRFbJQWfdN1SDToUFNhRv63Jris4dBr9IBHD7VHD+E1wO7To+SwBkckeX/OOAMYlAnYbMDWfODIYeD//i80vuw3Ps+kp8s/QtlsQH4+cDiEjgMph69BIgpFiifdCxcuxKxZs3DrrbcCAJ577jmsWLECL7/8MhYsWNCs/CuvvIK+ffviueeeAwAMGTIEmzdvxjPPPNOjk25Jkn/1ra4GBmQ3wKoqhB1FUIXrENcvHOXlEo40VMJR6YRGo4bwf/OFPLLuiRqva61s4/VN/xVt3kaHn/PYH0kAS/4jUGwHkjMAh0rAAQDhQESGQHEZ8OanwFVXCbQ0VpF/2x3dx9bKB7YrgE8+AY44gKQMwAIBCwCEAYYMgcMVwGv/AS65RBx7tIDHI+HNrwU88QIGvYBQS/ABgEqCTgi4PQJvrBLYUCqhwi0Q11egUiUASFDrJXx5qA6/uCOxcDkwaZLA6tUSKrwCMX0FSiAAIUEYAdFXwt6jAn9fJjB2rIAkJKxfD1T7JET1ETikkpskhVGC6CNQUi/w148FRuRJUAHHR7k/Nsd6a/+3tVyr/0sSbHYfvGE+qEw+SPBC+LyADoHk9qdPjlVA+LEbAEQ1revaRv/v3g/AACCx+WuiLQSAzh5TWgsTJFckVF4zdCISaskMjy0KHocZ8ERC5TFDr45CVJgZGikSPkcEFt5rx5c/D8LmfA2Ki4GEBOBoLQDXsdZfn/wadOmA2GS514TPB9TWAl98AQwefOrLBiQht2LX1cmt1/73kSkC6BcOrP1JXp4wXm5pBuSu417v8dZqSTr+OPlHBQlQSRAqAY+QoNcJHCySoFJLCAuXEGGWUFsvIMJ8UBkE4PPBp5YgqSUYjYDL1fQ9qFGrYRcaqI0aqIQaUGngdupQejACehgAnwH2Q0YMidFCrdGhulIH72EdDGk6DMjQQeXVomifClI5oPICPrUcb5gBgA5wSfKx0+nkHwNKSoC+fYHISLl1bc8e4J135Bb73ty9tfF5ZsiQ43UaaseBlMPXIBGFKkWTbrfbjfz8fDz44INN1k+ZMgXr1q1r8THr16/HlClTmqw7//zz8frrr8Pj8UB34gWzAFwuF1yu43PPWiwWAPJULsEynUtBgXzr1w+o0H6Nr73TAHejAnHAn9YAWKNQgF0tAShoqUduAlBgA759u9sjaioeKGwpvjig0A78sOSE9ce6hrc247EDwGY3gBjgyAmX8W7ZCcAMHHYCW1YcWxkFlLZ0uW8UUO4CdqxutC4SqGhpNigzUOUGfvm5laC6WjB8gZK0UEkGQDJALQww6g3QwACHzQCVpIdKMkKnCofKZ4LLHgbJEw54w6HyhUEthQO+cOjV8rLkDodBF46MlDDMnBGGD95To6QUCDMCGq2A1wPUHAU8XkClEhAC0GoFItRy12qtSUKEXo0du504Wq9CQhKg1gho9ECYXu7KrVLLj5OEgKQGouIAl1cgNRY4XCqwu0AgLbXpD0iNfzgSECgtAw6VCcT3EfDpRaNkV8DuENBGyct1PgGTQQUBAYdDBZdOIDJFvtbaoFPB5xPQCQBQyb9aCBVUKjWEpII+Qg2fSgUIDSIiVNCq1dALLfQaI9xeHYSkg3DpAKFFmFsLtVMD4dMAQgOV0ECv1cDl0iBMr4VapYXkVcNs0qKuDohLkJNldw2gbpB/hPBagNQ4wGMHnHYA8KKhQW6lB+SkPixMbtUG5Mc7HMe7rTudgN1+vOt6v37A/v3AL7/I15V2Jv85JhjONY3PMy2cKrv0OISCYKrrYNUbXoOs59DBug4NJ9ZzV9W3SrTWbNkNSktLkZaWhp9++gnjxo0LrJ8/fz7efvtt/PLLL80eM3jwYNx00014+OGHA+vWrVuH8ePHo7S0FCkpKc0eM3fuXMybN6/Z+g8++ADh4eHN1ivt57Kf8WT+k0qHQXRKjaeoazJdXaN1Jy5rVVpo1Y1uqub/a9Qa6FS6JuU0Kg10al2TZf//OrUOerUeBo0BOs3x//UaPfRqvfxXI89DT0RERETUErvdjunTp6O+vh6RkZGdtl3Fu5cDaDbHqRDipPOetlS+pfV+Dz30EO69997AssViQZ8+fTBlypROPZi/RkEBcP/98qBEzohkZKtL4YPrWKuUCj6fhP59j8JgioPZLO9n4/1VNbqQtFPWNzqUXfk89fXy9aAGgzwIlqrJBbEqeNxyq9X48SpERbdtm609f9N/2xZvbS2wejUQZlRBr2++IY9bBYcT+M05QFycGiqVCjt2qLBqhRpqtQoajRoQqmOPUQFCDcmrgs+nRlycCilJKhiMKqigBqCCRg2MyCnChq39YLVqcfYkFdasVsMUroLRqDoWoTrw1+VUwWZTY/p0OQF+/301zCb1sRY/NVSB8mo4HSpYrSrMmqVCaqpKTlo12sCc7oGbSgON5tjfRuu1ai3UanUg2W3r3M2FhcC8eUB01PGRqRuzWlUoK5P/T0qRWzLz8wG9Xm4Rlo+zfG1wXh5gdwDbtgKDBskjV+t0qiYtJk4HUFUl/6/RAAnHuqBXV6qg0cjV4PUAEWZgzBj5vg0/A14J0KhVgdbSqkp54C4hyfXtH5sszCC/BrxewBgmt8bMvkuF516Qr0cMC5NbcDxuFSorALdHJV+3LACdRoWkZHnf1Gof5j2yFvOfmojDRVqYIwCNVoXqKgBCBZdbfg0KIXfxTkmW99PjUSEnB/B5VXjqKRUGDpRfDa19/jX+bDnx466+HvjpWPfycePkVmD/+g0b5PdeTY38/vR4mnYzF0Lu/imEvO1jHYiQlCS3MPvrwOWSy/h8cvmwMLml2f9zr0olHw9/67R//IOICLlLfIK/pdsNnHmm/LiNG4+XO/NMeTtr18qt8v7nbNzS7fUeb+lOTDz+OH9Lt8Uid9l/+umuaeletWoVzjvvvBZ7YnWnk70WgK49DqEgmOo6WPWG1yDrOXSwrkPDifXs7xHd2RRNuuPj46HRaFBeXt5kfWVlJZKSklp8THJycovltVot4uLiWnyMwWCAwWBotl6n0wXNmygrSz7B5OcDQ4acibNUZwbuEwI4UOjBny7+Er/97YUwGIIj5s4gScC9Bf79RpPrtoUA9uwHRo0Cnr1dmeu7JAm4d/fJ45s8Cnh21vH43BcBaS/Kg09FRDSNW5IAuxWIiwOuGA1s29Z0u1q1B+ekf4kdyy/EmOE6PH0bcN8v8vMPaun5C4CzRwF/vkRed+R7uWzfVsqOHwXcNqV7j2XycCAno/VjWHYAGDlS/n/LFnnUdpMKqC6Tk0AhgPo6OVmKMQDlB4Eks9zF2KQCqo6V82/XWgto3Mev4UXEscG6HIDVeTxZTIsDog3Huitb5b+RsUB1+bHk0wm4LHKyqFLJN60W0ITJCSAAhKmBvBzgkt8Ca1YBB3YBdfXyF0oVALgBp+X46PU6s7xOHlxM7r40brQZbrsOBw4AqamA5JaTUp9PThaFkJNSjQqorTl23Xe1PBjYkOxT12XTz5amxz+s0b6Eh8vP5//fYADKyuTYrVZ5vbeFSyz0ejlej0fetsUiHz+f7/h+uN3yfWq1/ENI46Tbv10h5OdRqeTnttvlsv5zX0KCHBcgX5d9+LB8XbZ/ndkMVFbK21Kr5W35P/ZdLnm9JB0fTM6/v0IAhw7JnzNZWV333giG883JXgvddRxCQTDUdbDqTa9B1nPoYF2HBn89d1VdK/qRptfrMXLkSKxatarJ+lWrVjXpbt7Y2LFjm5VfuXIlRo0a1aPfEGq1PFVGfLw8kIjFIn8htFjkZf/vCcF+EmqvU+13fDxwww3K7XdH4tPrgXvvlRM+q1VOOPyJhz8RvOce4JZbWt4uINf3DTfISV5bnz9Yj2Vb4rrxRuCmm+T/9+4F0tLk41hdfbylNTVVvi8hAZgzR06ctFr5VlcnJ2m1tfJyQoLciuIfedvtlhNMfyJrMgEDBsgto3v3ygOSZWXJdePfnsHQtFXXvy/+RFKvlx93443yY266SV72eOS4/a2t/m2oVHKi53bL9/svGZoxQ96fiAigtFR+Xn8Lsv95IyLk1mf//iYktL0uT3b8/fs+eLD8v/++hgb5WERGysdZozkeS+O4VCq5nCQBOTnyl2iPR64zf48B/48WGo0cu8Nx/PXqT8T9LduSJJcH5MTafzw9Hvk14fPJsWm1cnmdTl72+Y6/ZgB5vUol15XDcXz7Op28f/5tKf3e6G7B+hlBoYOvQSIKVYpe0w3IU4bNnDkTr7zyCsaOHYvXXnsNixYtwq5du5CRkYGHHnoIJSUleOeddwDIU4bl5ubi97//PW677TasX78et99+OxYvXtzm0cstFguioqI6va9+Z2hp7sqcHGDGDA+Ki7/EhRde2KN/XGhNa/t9ww3BMXVIR+JbsABYuFBOlvwtndHRcsL90EMtb9ds9uC2275EevqFGDlS16HnD9Zj2Za4GpeprpZvgPxFLD6+aXl/2Q0b5G7m/m326SN3Gx89GvjsM7n7tP/HDKNRTiRjY+UErXEMQPPt+RNCu/14q6hGI7dk/+Y3wH33NT2mW7fK3SJPfE6DQU7CnU55XWQkcPbZHlxxxfH39OLFwFNPyc/tb5lVq4+1cmua7ltH6vJkx9+/7yfeN3q0fDy++w44eFCOS5KOT/VlMMg/AEyYIB8LoOn++46Nvu7/IcHtPj5Pt3+/ALmc/z2i0cj3+es8Pl7eRk1Ny7E1jjk+Xi63Z4/8A4y/hd1sln9k6d+/5W115XvD4/Hgyy+D67M7WD8jerpgrOtg1ZNfg6zn0MG6Dg0n1nNX5YmKJ90A8NJLL+Gpp55CWVkZcnNz8Y9//ANnnXUWAOCmm27CoUOHsHr16kD5NWvW4J577sGuXbuQmpqKBx54ALfffnubny+Yk25A/vJZUCAna1FRclcsn6/3v/Fb2u9g+rW7I/G53cC77wJFRXJX2Jkz0eja8ObbjYjwYN++luu5Pc8frMeyLXE1LmM2y+saGlou7y9bWyu3TkdHywmxv5wkAfv2ATt3yuVzc+X7Dh5sOYaWthcVJa/fuRMoLwdSUoBhw45N19XCMW3tOQsKmq7LzPTg66+b1rXXC3z7rfw8iYlARoa87y3tW2cf/9bu86+vqQF27z5e1myWk+fc3KbH4sT9z8mR76uvl/cjMvJ4F3T/NdX++xqva1znwMlja7weOP78Qsgt3LGxx49da9vqKsH6pS1YPyN6smCt62DVU1+DrOfQwboODSGVdHe3YE+6W8I3fmhgPYcO1nVoYD2HDtZ1aGA9hw7WdWjorqS7B/ymSERERERERNQzMekmIiIiIiIi6iJMuomIiIiIiIi6CJNuIiIiIiIioi7CpJuIiIiIiIioizDpJiIiIiIiIuoiTLqJiIiIiIiIugiTbiIiIiIiIqIuwqSbiIiIiIiIqIsw6SYiIiIiIiLqIky6iYiIiIiIiLoIk24iIiIiIiKiLsKkm4iIiIiIiKiLMOkmIiIiIiIi6iJMuomIiIiIiIi6iFbpAJQghAAAWCwWhSNpO4/HA7vdDovFAp1Op3Q41EVYz6GDdR0aWM+hg3UdGljPoYN1HRpOrGd/fujPFztLSCbdDQ0NAIA+ffooHAkREREREREFk4aGBkRFRXXa9lSis9P4HkCSJJSWlsJsNkOlUikdTptYLBb06dMHR44cQWRkpNLhUBdhPYcO1nVoYD2HDtZ1aGA9hw7WdWg4sZ6FEGhoaEBqairU6s67EjskW7rVajXS09OVDqNDIiMj+cYPAazn0MG6Dg2s59DBug4NrOfQwboODY3ruTNbuP04kBoRERERERFRF2HSTURERERERNRFmHT3EAaDAY899hgMBoPSoVAXYj2HDtZ1aGA9hw7WdWhgPYcO1nVo6K56DsmB1IiIiIiIiIi6A1u6iYiIiIiIiLoIk24iIiIiIiKiLsKkm4iIiIiIiKiLMOkOErW1tZg5cyaioqIQFRWFmTNnoq6urtXyHo8HDzzwAIYNGwaTyYTU1FTccMMNKC0tbVLu7LPPhkqlanK79tpru3hvqLGXXnoJmZmZMBqNGDlyJH788ceTll+zZg1GjhwJo9GI/v3745VXXmlWZtmyZcjJyYHBYEBOTg4++eSTrgqf2qg99bx8+XKcd955SEhIQGRkJMaOHYsVK1Y0KfPWW281e++qVCo4nc6u3hU6hfbU9erVq1usx7179zYpx/d08GlPPd90000t1vPQoUMDZfieDj4//PADLr74YqSmpkKlUuE///nPKR/Dc3TP1N665nm6Z2pvPXfnOZpJd5CYPn06tm3bhq+//hpff/01tm3bhpkzZ7Za3m63Y8uWLXj00UexZcsWLF++HPv27cMll1zSrOxtt92GsrKywO3VV1/tyl2hRpYuXYrZs2fjkUcewdatWzFx4kRccMEFKCoqarF8YWEhLrzwQkycOBFbt27Fww8/jLvuugvLli0LlFm/fj2uueYazJw5E9u3b8fMmTMxbdo0bNiwobt2i07Q3nr+4YcfcN555+HLL79Efn4+zjnnHFx88cXYunVrk3KRkZFN3rtlZWUwGo3dsUvUivbWtd8vv/zSpB4HDRoUuI/v6eDT3np+/vnnm9TvkSNHEBsbi6uvvrpJOb6ng4vNZsNpp52Gf/7zn20qz3N0z9XeuuZ5umdqbz37dcs5WpDidu/eLQCIn3/+ObBu/fr1AoDYu3dvm7ezceNGAUAcPnw4sG7SpEni7rvv7sxwqR1Gjx4tbr/99ibrsrOzxYMPPthi+Tlz5ojs7Owm637/+9+LMWPGBJanTZsmfvvb3zYpc/7554trr722k6Km9mpvPbckJydHzJs3L7D85ptviqioqM4KkTpJe+v6+++/FwBEbW1tq9vkezr4/Nr39CeffCJUKpU4dOhQYB3f08ENgPjkk09OWobn6N6hLXXdEp6ne5a21HN3nqPZ0h0E1q9fj6ioKJx55pmBdWPGjEFUVBTWrVvX5u3U19dDpVIhOjq6yfr3338f8fHxGDp0KO677z40NDR0Vuh0Em63G/n5+ZgyZUqT9VOmTGm1XtevX9+s/Pnnn4/NmzfD4/GctEx7XivUeTpSzyeSJAkNDQ2IjY1tst5qtSIjIwPp6emYOnVqs1/YqXv9mroeMWIEUlJScO655+L7779vch/f08GlM97Tr7/+OiZPnoyMjIwm6/me7tl4jg5dPE/3bt1xjmbSHQTKy8uRmJjYbH1iYiLKy8vbtA2n04kHH3wQ06dPR2RkZGD99ddfj8WLF2P16tV49NFHsWzZMlxxxRWdFju1rrq6Gj6fD0lJSU3WJyUltVqv5eXlLZb3er2orq4+aZm2vlaoc3Wknk/07LPPwmazYdq0aYF12dnZeOutt/Df//4XixcvhtFoxPjx47F///5OjZ/ariN1nZKSgtdeew3Lli3D8uXLkZWVhXPPPRc//PBDoAzf08Hl176ny8rK8NVXX+HWW29tsp7v6Z6P5+jQxfN079Sd52htp0RMLZo7dy7mzZt30jKbNm0CAKhUqmb3CSFaXH8ij8eDa6+9FpIk4aWXXmpy32233Rb4Pzc3F4MGDcKoUaOwZcsW5OXltWU36Fc6sQ5PVa8tlT9xfXu3SV2vo3WyePFizJ07F59++mmTH9/GjBmDMWPGBJbHjx+PvLw8vPjii3jhhRc6L3Bqt/bUdVZWFrKysgLLY8eOxZEjR/DMM8/grLPO6tA2qXt0tE7eeustREdH47LLLmuynu/p3oHn6NDD83Tv1Z3naCbdXeiPf/zjKUcK79evH/73v/+hoqKi2X1VVVXNflk5kcfjwbRp01BYWIjvvvuuSSt3S/Ly8qDT6bB//34m3V0sPj4eGo2m2S9hlZWVrdZrcnJyi+W1Wi3i4uJOWuZUrxXqGh2pZ7+lS5di1qxZ+OijjzB58uSTllWr1TjjjDP4C7qCfk1dNzZmzBi89957gWW+p4PLr6lnIQTeeOMNzJw5E3q9/qRl+Z7ueXiODj08T4eerjpHs3t5F4qPj0d2dvZJb0ajEWPHjkV9fT02btwYeOyGDRtQX1+PcePGtbp9f8K9f/9+fPPNN4EP/JPZtWsXPB4PUlJSOmUfqXV6vR4jR47EqlWrmqxftWpVq/U6duzYZuVXrlyJUaNGQafTnbTMyV4r1HU6Us+A/Mv5TTfdhA8++AAXXXTRKZ9HCIFt27bxvaugjtb1ibZu3dqkHvmeDi6/pp7XrFmDgoICzJo165TPw/d0z8NzdGjheTo0ddk5ul3DrlGX+e1vfyuGDx8u1q9fL9avXy+GDRsmpk6d2qRMVlaWWL58uRBCCI/HIy655BKRnp4utm3bJsrKygI3l8slhBCioKBAzJs3T2zatEkUFhaKL774QmRnZ4sRI0YIr9fb7fsYipYsWSJ0Op14/fXXxe7du8Xs2bOFyWQKjGj74IMPipkzZwbKHzx4UISHh4t77rlH7N69W7z++utCp9OJjz/+OFDmp59+EhqNRjz55JNiz5494sknnxRarbbJ6PfUvdpbzx988IHQarXiX//6V5P3bl1dXaDM3Llzxddffy0OHDggtm7dKm6++Wah1WrFhg0bun3/6Lj21vU//vEP8cknn4h9+/aJnTt3igcffFAAEMuWLQuU4Xs6+LS3nv1mzJghzjzzzBa3yfd08GloaBBbt24VW7duFQDEwoULxdatWwOzwPAc3Xu0t655nu6Z2lvP3XmOZtIdJGpqasT1118vzGazMJvN4vrrr282fD0A8eabbwohhCgsLBQAWrx9//33QgghioqKxFlnnSViY2OFXq8XAwYMEHfddZeoqanp3p0Lcf/6179ERkaG0Ov1Ii8vT6xZsyZw34033igmTZrUpPzq1avFiBEjhF6vF/369RMvv/xys21+9NFHIisrS+h0OpGdnd3kw4GU0Z56njRpUovv3RtvvDFQZvbs2aJv375Cr9eLhIQEMWXKFLFu3bpu3CNqTXvq+u9//7sYMGCAMBqNIiYmRkyYMEF88cUXzbbJ93Twae9nd11dnQgLCxOvvfZai9vjezr4+KcLau2zmOfo3qO9dc3zdM/U3nruznO0SohjI0AQERERERERUafiNd1EREREREREXYRJNxEREREREVEXYdJNRERERERE1EWYdBMRERERERF1ESbdRERERERERF2ESTcRERERERFRF2HSTURERERERNRFmHQTERERERERdREm3URERERERERdhEk3ERERERERURdh0k1ERL3K2WefjdmzZ3fLc0mShG+//bZbnivY1NTUIDExEYcOHWrX437/+99j+vTpnV62p7jqqquwcOHCZuvXr1+vQDRERNQdmHQTERF10DPPPIPTTz9d6TAAAMXFxfjDH/6AgQMHwmg0IikpCVOmTMGOHTsghEB0dDRefPHFZo/705/+hLy8PADA8OHD8dhjj7W4/fnz5yMmJgZVVVUAgAULFuDiiy9Gv3792hXnggULsGjRog6VnT17Ni677LJ2PV9namhowOzZs5GRkYGwsDCMGzcOmzZtarHsggULoFKpmv0A9H//93944oknYLFYmqyPj4/HO++801WhExGRgph0ExERdcC3336LxMRExMXFKR0KDh06hBEjRqC6uhrvvvsu9u7di48//hg5OTkwGAw4cOAA6uvrMWrUqGaP3bx5M0aOHAlATrp37NjRrEx5eTkWLFiAefPmISEhAQ6HA6+//jpuvfXWdscaGxsLk8nUobKbNm3C6NGj2/2cneXWW2/FqlWr8O6772LHjh2YMmUKJk+ejJKSkiblNm3ahNdeew3Dhw9vto3hw4ejX79+eP/995usHzRoEKqqqrBr164u3QciIup+TLqJiKjbLF68GEajsUmScuutt2L48OGor6/vkud0uVy46667kJiYCKPRiAkTJjRpnWxoaMD1118Pk8mElJQU/OMf/2hTF/WFCxdixowZgeWzzz4bf/rTnzB79mzExMQgKSkJr732Gmw2G26++WaYzWYMGDAAX331VZPtfP3115gwYQKio6MRFxeHqVOn4sCBAwCAqqoqJCcnY/78+YHyGzZsgF6vx8qVKwPrXnzxRZhMJixduhRjx45Fv379MHHiRDz33HMYPHgw8vPzodVqm7XKe71ebN++PZB0Dxs2DDt37my2rw8//DAyMjJwxx13AAC++uoraLVajB07NlAmMTER//73v5s8btOmTYGkH5B/HFCpVDh8+DAkScL8+fMxaNCgQMv8zJkzA49tXNbj8UCv12PdunV45JFHoFKpcOaZZ560fioqKqBSqfD8889jxIgRMBqNGDp0KNauXXvSx7XG4XBg2bJleOqpp3DWWWdh4MCBmDt3LjIzM/Hyyy8HylmtVlx//fVYtGgRYmJiWtzWJZdcgsWLFzdbf9ttt+GJJ57oUHxERBS8mHQTEVG3ufbaa5GVlYUFCxYAAObNm4cVK1bgq6++QlRUVJOy8+fPR0RExElvP/744ymfc86cOVi2bBnefvttbNmyBQMHDsT555+Po0ePAgDuvfde/PTTT/jvf/+LVatW4ccff8SWLVtOus1vvvkGubm50Gq1Tda//fbbiI+Px8aNG/GnP/0Jf/jDH3D11Vdj3Lhx2LJlC84//3zMnDkTdrs98BibzYZ7770XmzZtwrfffgu1Wo3LL78ckiQhISEBb7zxBubOnYvNmzfDarVixowZuOOOOzBlypTANmpra+F0OlFUVNRivPn5+cjJyUFYWFiT9bt27YLD4WiSdB84cAAOhyNQZuvWrXj77bfxwgsvBPb3hx9+aNZqnpub26yV9qGHHsLvfvc7DBgwAACwbds2REdHIyMjAwsWLMAHH3yA1157Db/88guWL1+Os88+O/DYxmU1Gk0gWd62bRvKysqwYsWK1ivoWNwA8NJLL+Ef//gHtm/fjn79+uH666+HJEkA2vca83q98Pl8MBqNTZ4nLCysSSJ/55134qKLLsLkyZNbjW306NHYuHEjXC5Xk/WRkZEwmUz4+eefT7pvRETUwwgiIqJu9NlnnwmDwSCeeOIJERMTI3bu3NliuZqaGrF///6T3ux2e7PHTZo0Sdx9991CCCGsVqvQ6XTi/fffD9zvdrtFamqqeOqpp4TFYhE6nU589NFHgfvr6upEeHh4YBstufHGG8WaNWuaPe+ECRMCy16vV5hMJjFz5szAurKyMgFArF+/vtVtV1ZWCgBix44dgXV33HGHGDx4sLj++utFbm6ucDgcTR6Tn58v+vbtK1QqlRg5cqR44IEHxK5duwL3/+Y3vxG33HJLs+f697//LXQ6nXA6nUIIIYqKigQAkZ+fHyhz1llniSuvvLLJ4y699NJm27vrrrvElClTAssrVqwQERERoqKiIrBu7ty5YtKkSUIIISZOnCjmzJnT6nFoXFYIIT755BMRFxfXavkTPfnkk0Kn04mDBw8G1m3evFkAEEVFRUKI9r/Gxo4dKyZNmiRKSkqE1+sV7777rlCpVGLw4MFCCCEWL17cpH4avxYb2759uwAgDh061Oy+//73v2LWrFlt3k8iIgp+2pNm5ERERJ1s6tSpyMnJwbx587By5UoMHTq0xXKxsbGIjY39Vc914MABeDwejB8/PrBOp9Nh9OjR2LNnDw4ePAiPx9PkOuGoqChkZWWddLsrV65s0qXYr/E1vBqNBnFxcRg2bFhgXVJSEgCgsrKySYyPPvoofv75Z1RXVwdaYYuKipCbmwtAHrAtNzcXH374ITZv3tystTUvLw8HDx7E2rVrsWrVKnz44Yd49tln8eGHH+Lyyy/Hli1bcNVVVzWLd/PmzRg6dCgMBgMAoE+fPoiOjsaOHTuQl5eHjz/+GJs2bcKePXuaPM7hcDSLITc3F8uWLQMACCHw8MMP4/7770diYmKgzLZt23DaaacBkLtYP/DAA9i6dSuuuOIKTJs2rUl9Ny4LyC3XjZdPZdu2bbjiiiuQmZkZWOffT7/2vsbeffdd3HLLLUhLS4NGo0FeXh6mT5+OLVu24MiRI7j77ruxcuXKZsfmRP4eB417PPideeaZHbpWnoiIghe7lxMRUbdasWIF9u7dC5/PF0hCW9IZ3cuFEAAAlUrVbL1KpTrp/a0pLCxEeHh4s67agJzQN6ZSqZqs8z+PP7EGgIsvvhg1NTVYtGgRNmzYgA0bNgAA3G53oMzBgwdRWloKSZJw+PDhFuPSaDSYNGkS/va3v2HXrl1ITEzEBx98gCNHjqCurg5Dhgxp9phvvvkG48aNa7LOf123y+XCnDlzMGfOHGRkZDQpEx8fj9ra2maPKykpgcViwYcffoji4mLce++9Tcps3749cF35fffdhz179mDy5Ml48cUXMXDgQBQWFrZYFmiehJ/Ktm3bml3DvmXLFsTHxyMtLQ1A+19jAwYMwJo1a2C1WnHkyBFs3LgRHo8HmZmZyM/PR2VlJUaOHAmtVgutVos1a9YEuuX7fL7AdvyXNiQkJDSL2z/2QEFBQZv3lYiIghtbuomIqNts2bIFV199NV599VUsWbIEjz76KD766KMWy95+++2YNm3aSbfnT55aM3DgQOj1eqxduzYw37PH48HmzZsxe/ZsDBgwADqdDhs3bkSfPn0AABaLBfv378ekSZNa3Oa+fftO+bxtVVNTgz179uDVV1/FxIkTAaDZQF9utxvXX389rrnmGmRnZ2PWrFnYsWPHSX+wkCQJLpcLCQkJ8Hg8AJq3qq5atQoFBQV4/fXXm6z3J90LFy6Ez+fDAw880Gz7I0aMwHvvvddkXW5uLlQqFbZv345HH30Ujz32GCIiIgL3WywWHDp0qEniPHjwYMyZMwd33303oqKisHv3bmRmZrZYdseOHbj88stb3efGHA4H9u/f3yTRlSQJzz//PG688Uao1XKbQ0dfYyaTCSaTCbW1tVixYgWeeuopnHvuuc1Gfr/55puRnZ2NBx54ABqNJrB+586dSE9PR3x8fIvPmZKSgn379mHgwIFt2l8iIgpuTLqJiKhbHDp0CBdddBEefPBBzJw5Ezk5OTjjjDOQn58fGMirsc7oXm4ymfCHP/wB999/P2JjY9G3b1889dRTsNvtmDVrFsxmM2688cbA/YmJiXjsscegVqubtX77lZeXIzo6+lfF5RcTE4O4uDi89tprSElJQVFRER588MEmZR555BHU19fjhRdeQEREBL766ivMmjULn3/+OQAEjuVvfvMbJCUl4eDBg5g/fz6EELj33nuRmZmJ7OxsPPTQQ9Dr9UhISMCGDRvwyCOPYMaMGTjrrLOaPN+wYcPw4Ycf4qeffsIbb7zRYov++eefj4ceegi1tbWBEbojIiKQkZGBOXPmAJBH4m5s+/bt0Gg0GDp0KJ566ikkJSXhjDPOgEajwb///W/ExMQEWt0bl/WTJAn/+9//UFpaCpPJ1GzgvcZ27NgBlUqF9957D7/5zW8QHR2N//u//0NdXR3+8pe/BMq19zW2YsUKCCGQlZWFgoIC3H///cjKysLNN98MnU4XuBzAz2QyIS4urtn6H3/8sclAeCeKjo5GeXl5m+MiIqLgxu7lRETU5Y4ePYoLLrgAl1xyCR5++GEAwMiRI3HxxRfjkUce6dLnfvLJJ3HllVdi5syZyMvLQ0FBAVasWBFIFhcuXIixY8di6tSpmDx5MsaPH48hQ4a0el2u3W5vdm1wR6nVaixZsgT5+fnIzc3FPffcg6effjpw/+rVq/Hcc8/h3XffRWRkJNRqNd59912sXbs2cE15Xl4ePv/8c0ydOhVDhgzB7bffjuzsbGzfvh0DBw6ESqXCF198gQEDBuDaa6/FWWedhVdffRV/+9vf8NZbbzWLadiwYaiursaoUaNavA7cX2bUqFH48MMPm63/+eef8cQTTzQb2X379u3Izs6GwWCA0+nE/PnzMXLkSEyYMAH79+/Hd999F6iTxmX9/va3v2Hp0qVIS0vD448/DgB46623WvxxZNu2bcjOzsZf/vIXXHXVVRg1ahTUajXWr1//q34wqa+vx5133ons7GzccMMNmDBhAlauXNnssoKTcTqd+OSTT5r9KNFYVFRUi9d7ExFRz6QSJ7twjYiIKMTYbDakpaXh2WefxaxZs5rd/+9//xuffPIJvvjiCwWiCx5ffvkl7rvvPuzcuTPQXbu7zZ07F6tXr8bq1aubrL/zzjtRW1uLDz74QJG4TuZf//oXPv300ybzrJ/okksuwdSpU/G73/2uGyMjIqKuwu7lREQU0rZu3Yq9e/di9OjRqK+vD7SiXnrppS2Wj4mJQV1dXTdGGJwuvPBC7N+/HyUlJYHr4bvbihUr8Pzzzzdbv23bNlx88cUKRHRqOp0OL7744knLNO62T0REPR+TbiIiCnnPPPMMfvnlF+j1eowcORI//vhjq4Nc9e/fPzD6dKi7++67FX3+9evXN1snhMCOHTu6/LKFjmpL63VtbW2Tqc6IiKhnY/dyIiKidnC73YiLi0NlZWWLg4y1xTfffIMdO3bgnnvuOen9l156KXbt2hW0rbbU+bxeLxITE1FSUtLh1xcREQUXDqRGRETUDnq9HmPGjEF+fn6HtzF58uRWE+7G93/11VfYu3dvh5+Hep4tW7Zg+PDhTLiJiHoRJt1ERETtdP311+P7778/aZmff/65SQv1Z599hltuuQUAcMEFF2DPnj0AgFdffRV5eXnIzc0NzCV+wQUXYM2aNfjLX/6CRYsWYcSIEXA4HF20NxRM1q5di6uvvlrpMIiIqBOxezkREVE7eTweXHDBBVi5cmWrI3dbLBaMGjUK+/btAwCMHTsW77//Pvr374+BAwdi7969aGhowOTJk7Fx40ZoNBrU1dUhOjo6cP8555yDDz74QLGByqj7XXjhhViyZAkiIyOVDoWIiDoJW7qJiIjaSafTYdasWfjqq69aLRMZGQmXywWPx4PPPvsMQ4cORf/+/VFfX4+IiAhotVpotVrU1NTggQcewK5duxAdHd3k/uLiYibcIWTLli0YN24cE24iol6GSTcREVEHXHvttVizZs1Ju30PGjQIBQUFWLBgAf7yl78AAHbu3ImhQ4cCAMxmM3bs2IHTTjsNV111FT7//PPA/cXFxUhLS+uWfaHg8MEHH+D+++9XOgwiIupkTLqJiIg6QKVS4bHHHsPnn3/eapmcnBw888wzGDZsGPr16wdATrpzc3MBAPv374fZbMbMmTMxceJEuFyuwP2FhYVITU3tjl2hIPDpp5/ij3/8IwwGg9KhEBFRJ2PSTURE1EEmkwlXXHFFq/cPGTIE7733XpM5o3ft2hVIuv/6178iKysLI0aMgNFoxOWXXx64Pzc3F/v378ewYcM4gnkIuOiiiwI/zBARUe/CgdSIiIiIiIiIughbuomIiIiIiIi6CJNuIiIiIiIioi7CpJuIiIiIiIioizDpJiIiIiIiIuoiTLqJiIiIiIiIugiTbiIiIiIiIqIuwqSbiIiIiIiIqIsw6SYiIiIiIiLqIky6iYiIiIiIiLoIk24iIiIiIiKiLsKkm4iIiIiIiKiL/D+PjhHxy8OPiwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Selection Criteria:\n",
      "Log-Likelihood: -6.7954\n",
      "AIC: 17.5908\n",
      "BIC: 21.7116\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 0.9655\n",
      "\n",
      "Confusion Matrix:\n",
      "[[52  1]\n",
      " [ 1  4]]\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.9811    0.9811    0.9811        53\n",
      "           1     0.8000    0.8000    0.8000         5\n",
      "\n",
      "    accuracy                         0.9655        58\n",
      "   macro avg     0.8906    0.8906    0.8906        58\n",
      "weighted avg     0.9655    0.9655    0.9655        58\n",
      "\n",
      "ROC AUC Score: 0.9811\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from scipy import io\n",
    "from scipy.stats import norm\n",
    "from scipy.special import expit  # Sigmoid\n",
    "\n",
    "# --- Load Data ---\n",
    "data_path = \"../data/\"\n",
    "suv_file = data_path + \"suv_percentilesSLOthenUWM.mat\"\n",
    "flags_file = data_path + \"flags_combined.mat\"\n",
    "\n",
    "suv_dict = io.loadmat(suv_file)\n",
    "flags_dict = io.loadmat(flags_file)\n",
    "\n",
    "suv = suv_dict['lung_SUVperc_COMBINED'][0:58, :, :]\n",
    "flags = flags_dict['flags'][0:58, 3]\n",
    "\n",
    "p = 94  # Percentile index\n",
    "X_raw = np.nanmax(suv[:, :, p], axis=1).reshape(-1, 1)\n",
    "\n",
    "# --- Log-transform SUVmax ---\n",
    "X = np.log(X_raw)  # Log-transformed SUV\n",
    "\n",
    "# --- Logistic Regression ---\n",
    "logr = LogisticRegression(penalty=None, solver='lbfgs')\n",
    "logr.fit(X, flags)\n",
    "\n",
    "# --- Parameters and Delta Method Confidence Intervals ---\n",
    "params = np.insert(logr.coef_[0], 0, logr.intercept_[0])  # [intercept, coef]\n",
    "X_design = np.hstack([np.ones((X.shape[0], 1)), X])  # Design matrix\n",
    "pred_probs = logr.predict_proba(X)[:, 1]\n",
    "V = np.diagflat(pred_probs * (1 - pred_probs))\n",
    "cov_matrix = np.linalg.inv(X_design.T @ V @ X_design)\n",
    "\n",
    "se_params = np.sqrt(np.diag(cov_matrix))\n",
    "z = norm.ppf(1 - 0.05 / 2)\n",
    "ci_lower = params - z * se_params\n",
    "ci_upper = params + z * se_params\n",
    "\n",
    "# --- Print Estimated Parameters and CIs ---\n",
    "print(\"\\nFitted Parameters (using log(SUV)):\")\n",
    "print(f\"Intercept (w0): {params[0]:.4f} [{ci_lower[0]:.4f}, {ci_upper[0]:.4f}]\")\n",
    "print(f\"Coefficient (w1): {params[1]:.4f} [{ci_lower[1]:.4f}, {ci_upper[1]:.4f}]\")\n",
    "\n",
    "# --- Function: Predict with CI ---\n",
    "def predict_prob_ci(x, beta, cov, alpha=0.05):\n",
    "    x_vec = np.array([1, x])\n",
    "    f_hat = x_vec @ beta\n",
    "    var_f = x_vec @ cov @ x_vec.T\n",
    "    se_f = np.sqrt(var_f)\n",
    "    z = norm.ppf(1 - alpha / 2)\n",
    "    ci_logit = [f_hat - z * se_f, f_hat + z * se_f]\n",
    "    return expit(f_hat), expit(ci_logit[0]), expit(ci_logit[1])\n",
    "\n",
    "# --- Evaluate Model Predictions ---\n",
    "x_vals = np.linspace(X.min(), X.max(), 500)\n",
    "results = np.array([predict_prob_ci(x, params, cov_matrix) for x in x_vals])\n",
    "mean_probs, lower_ci, upper_ci = results.T\n",
    "\n",
    "# --- Plot: AE Probability vs log(SUV) ---\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.title(\"AE Probability from Logistic Regression (log(SUV) predictor)\")\n",
    "plt.xlabel(r\"$x = \\log\\left(\\max_{visit} SUV(visit, p=94)\\right)$\")\n",
    "plt.ylabel(\"P(AE | X = log(SUV))\")\n",
    "\n",
    "plt.scatter(X[flags == 0], flags[flags == 0], label=\"NC\", color='blue', alpha=0.6)\n",
    "plt.scatter(X[flags == 1], flags[flags == 1], label=\"AE\", color='red', alpha=0.6)\n",
    "\n",
    "plt.plot(x_vals, mean_probs, color='darkgreen', linewidth=2, label=\"Predicted Probability\")\n",
    "plt.fill_between(x_vals, lower_ci, upper_ci, color='green', alpha=0.2, label=\"95% CI (Delta Method)\")\n",
    "\n",
    "plt.legend(loc='center right')\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# --- Model Selection Criteria ---\n",
    "y_true = flags\n",
    "eps = 1e-15  # prevent log(0)\n",
    "log_likelihood = np.sum(\n",
    "    y_true * np.log(pred_probs + eps) + (1 - y_true) * np.log(1 - pred_probs + eps)\n",
    ")\n",
    "\n",
    "k = len(params)\n",
    "n = len(y_true)\n",
    "\n",
    "AIC = 2 * k - 2 * log_likelihood\n",
    "BIC = k * np.log(n) - 2 * log_likelihood\n",
    "\n",
    "print(\"\\nModel Selection Criteria:\")\n",
    "print(f\"Log-Likelihood: {log_likelihood:.4f}\")\n",
    "print(f\"AIC: {AIC:.4f}\")\n",
    "print(f\"BIC: {BIC:.4f}\")\n",
    "\n",
    "# --- Residuals ---\n",
    "residuals = y_true - pred_probs\n",
    "\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.title(\"Residuals of Logistic Regression (log(SUV) model)\")\n",
    "plt.xlabel(\"Predicted Probability\")\n",
    "plt.ylabel(\"Residual (Observed - Predicted)\")\n",
    "plt.scatter(pred_probs, residuals, alpha=0.7)\n",
    "plt.axhline(0, linestyle='--', color='gray')\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "# Accuracy\n",
    "accuracy = accuracy_score(flags, y_pred_binary)\n",
    "print(f\"\\nAccuracy: {accuracy:.4f}\")\n",
    "\n",
    "# Confusion matrix\n",
    "cm = confusion_matrix(flags, y_pred_binary)\n",
    "print(\"\\nConfusion Matrix:\")\n",
    "print(cm)\n",
    "\n",
    "# Precision, Recall, F1-score\n",
    "print(\"\\nClassification Report:\")\n",
    "print(classification_report(flags, y_pred_binary, digits=4))\n",
    "\n",
    "# ROC AUC Score\n",
    "roc_auc = roc_auc_score(flags, pred_probs)\n",
    "print(f\"ROC AUC Score: {roc_auc:.4f}\")\n",
    "\n",
    "\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "fpr, tpr, _ = roc_curve(flags, pred_probs)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC curve (AUC = {roc_auc:.2f})')\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "190935c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:2385: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:2443: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q * linpred)))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parametric Bootstrap Results:\n",
      "Intercept: mean = -11.6949, 95% CI = [-18.5592, -4.8498]\n",
      "Slope:     mean = 5.6943, 95% CI = [1.8860, 9.5262]\n",
      "\n",
      "Delta Method Results:\n",
      "Intercept: -11.6839 ± 6.8804 (95% CI: [-18.5643, -4.8035])\n",
      "Slope:     5.6870 ± 3.8302 (95% CI: [1.8568, 9.5171])\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import io\n",
    "from scipy.ndimage import gaussian_filter1d\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import statsmodels.api as sm\n",
    "from scipy.special import expit  # for sigmoid\n",
    "\n",
    "# --- Load Data ---\n",
    "data_path = \"../data/\"\n",
    "suv = io.loadmat(data_path + \"suv_percentilesSLOthenUWM.mat\")['lung_SUVperc_COMBINED'][0:58, :, :]\n",
    "flags = io.loadmat(data_path + \"flags_combined.mat\")['flags'][0:58, 3]\n",
    "\n",
    "# --- Preprocessing ---\n",
    "p = 94\n",
    "X = np.nanmax(suv[:, :, p], axis=1).reshape(-1, 1)\n",
    "valid = ~np.isnan(X).flatten()\n",
    "X = X[valid]\n",
    "flags = flags[valid]\n",
    "\n",
    "x_vals = np.linspace(X.min(), X.max(), 500)\n",
    "\n",
    "# --- Parametric Bootstrap ---\n",
    "logr = LogisticRegression(penalty=None, solver='lbfgs')\n",
    "logr.fit(X, flags)\n",
    "\n",
    "X_design = np.hstack([np.ones((X.shape[0], 1)), X])\n",
    "pred_probs = logr.predict_proba(X)[:, 1]\n",
    "V = np.diagflat(pred_probs * (1 - pred_probs))\n",
    "cov_matrix = np.linalg.inv(X_design.T @ V @ X_design)\n",
    "params = np.insert(logr.coef_[0], 0, logr.intercept_[0])\n",
    "\n",
    "n_bootstraps = 200000\n",
    "bootstrap_params = np.random.multivariate_normal(params, cov_matrix, size=n_bootstraps)\n",
    "predicted_probs_boot = 1 / (1 + np.exp(-(bootstrap_params[:, 0:1] + bootstrap_params[:, 1:2] * x_vals)))\n",
    "\n",
    "# Smooth and filter bootstrap curves\n",
    "sigma_curve = 0.5\n",
    "smoothed = np.array([gaussian_filter1d(c, sigma=sigma_curve) for c in predicted_probs_boot])\n",
    "\n",
    "def is_reasonable(c): return (0.05 < c.mean() < 0.95) and (c.max() - c.min() > 0.2)\n",
    "filtered = np.array([c for c in smoothed if is_reasonable(c)])\n",
    "mean_probs_boot = filtered.mean(axis=0)\n",
    "\n",
    "lower_boot = gaussian_filter1d(np.percentile(filtered, 5, axis=0), sigma=0.2)\n",
    "upper_boot = gaussian_filter1d(np.percentile(filtered, 95, axis=0), sigma=0.2)\n",
    "\n",
    "# --- Delta Method (Corrected) ---\n",
    "X_with_intercept = sm.add_constant(X)\n",
    "model = sm.Logit(flags, X_with_intercept)\n",
    "result = model.fit(disp=0)\n",
    "\n",
    "x_vals_stats = np.linspace(X.min(), X.max(), 500)\n",
    "X_pred = sm.add_constant(x_vals_stats)\n",
    "linear_preds = X_pred @ result.params\n",
    "prob_preds = expit(linear_preds)\n",
    "\n",
    "cov_params = result.cov_params()\n",
    "\n",
    "# Gradient of logistic function\n",
    "gradients = X_pred * (prob_preds * (1 - prob_preds))[:, None]  # shape: (500, 2)\n",
    "\n",
    "# Delta method variance propagation\n",
    "variances = np.einsum('ij,jk,ik->i', gradients, cov_params, gradients)\n",
    "standard_errors = np.sqrt(variances)\n",
    "\n",
    "z = 1.96\n",
    "lower_delta = np.clip(prob_preds - z * standard_errors, 0, 1)\n",
    "upper_delta = np.clip(prob_preds + z * standard_errors, 0, 1)\n",
    "\n",
    "# --- Plotting ---\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.title(\"Comparison of Parametric Bootstrap vs Delta Method\")\n",
    "plt.xlabel(r\"$x = \\max_{visit} SUV(visit, p)$\")\n",
    "plt.ylabel(r\"$P(\\mathrm{AE} \\mid X = x)$\")\n",
    "\n",
    "plt.scatter(X[flags == 0], flags[flags == 0], label=\"NC\", color='blue', alpha=0.5)\n",
    "plt.scatter(X[flags == 1], flags[flags == 1], label=\"AE\", color='red', alpha=0.5)\n",
    "\n",
    "# Delta method curve\n",
    "plt.plot(x_vals_stats, prob_preds, color='green', label=\"MLE (Delta Method)\")\n",
    "plt.fill_between(x_vals_stats, lower_delta, upper_delta, color='green', alpha=0.2, label=\"95% CI (Delta)\")\n",
    "\n",
    "# Bootstrap curve\n",
    "plt.plot(x_vals, mean_probs_boot, color='orange', label=\"MLE (Parametric Bootstrap)\", linewidth=2)\n",
    "plt.fill_between(x_vals, lower_boot, upper_boot, color='orange', alpha=0.2, label=\"95% CI (Bootstrap)\")\n",
    "\n",
    "plt.grid(True)\n",
    "plt.legend(loc='center right')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# --- Summary Outputs ---\n",
    "boot_intercepts = bootstrap_params[:, 0]\n",
    "boot_slopes = bootstrap_params[:, 1]\n",
    "\n",
    "print(\"Parametric Bootstrap Results:\")\n",
    "print(f\"Intercept: mean = {np.mean(boot_intercepts):.4f}, 95% CI = [{np.percentile(boot_intercepts, 2.5):.4f}, {np.percentile(boot_intercepts, 97.5):.4f}]\")\n",
    "print(f\"Slope:     mean = {np.mean(boot_slopes):.4f}, 95% CI = [{np.percentile(boot_slopes, 2.5):.4f}, {np.percentile(boot_slopes, 97.5):.4f}]\")\n",
    "\n",
    "params_delta = result.params\n",
    "se_delta = np.sqrt(np.diag(cov_params))\n",
    "print(\"\\nDelta Method Results:\")\n",
    "print(f\"Intercept: {params_delta[0]:.4f} ± {z*se_delta[0]:.4f} (95% CI: [{params_delta[0] - z*se_delta[0]:.4f}, {params_delta[0] + z*se_delta[0]:.4f}])\")\n",
    "print(f\"Slope:     {params_delta[1]:.4f} ± {z*se_delta[1]:.4f} (95% CI: [{params_delta[1] - z*se_delta[1]:.4f}, {params_delta[1] + z*se_delta[1]:.4f}])\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0b190ff2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:2385: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:2443: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q * linpred)))\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified\n",
      "  warnings.warn(msg, category=PerfectSeparationWarning)\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Parametric Bootstrap Results:\n",
      "Intercept: mean = -4.5122, 95% CI = [-15.3118, -2.3987]\n",
      "Slope:     mean = 3.4465, 95% CI = [1.4146, 10.6469]\n",
      "\n",
      "Delta Method Results:\n",
      "Intercept: -3.6869 ± 1.9550 (95% CI: [-5.6419, -1.7319])\n",
      "Slope:     2.7240 ± 1.8346 (95% CI: [0.8894, 4.5586])\n",
      "\n",
      "Model Summary Statistics:\n",
      "AIC: inf\n",
      "BIC: inf\n",
      "Log-Likelihood (Model): -inf\n",
      "Log-Likelihood (Null):  0.00\n",
      "Chi2 Statistic: -inf, df = 1.0, p-value = 1.0000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:595: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  warnings.warn('Inverting hessian failed, no bse or cov_params '\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:595: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  warnings.warn('Inverting hessian failed, no bse or cov_params '\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import io\n",
    "from scipy.ndimage import gaussian_filter1d\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from scipy.stats import chi2\n",
    "import statsmodels.api as sm\n",
    "\n",
    "# ---------------- Load Data ---------------- #\n",
    "data_path = \"../data/\"\n",
    "suv = io.loadmat(data_path + \"suv_percentilesSLOthenUWM.mat\")['lung_SUVperc_COMBINED'][0:58, :, :]\n",
    "flags = io.loadmat(data_path + \"flags_combined.mat\")['flags'][0:58, 3]\n",
    "\n",
    "# ---------------- Preprocessing ---------------- #\n",
    "p = 94\n",
    "X_raw = np.nanmax(suv[:, :, p], axis=1).reshape(-1, 1)\n",
    "valid = ~np.isnan(X_raw).flatten()\n",
    "X_raw = X_raw[valid]\n",
    "flags = flags[valid]\n",
    "\n",
    "# Standardize X\n",
    "scaler = StandardScaler()\n",
    "X = scaler.fit_transform(X_raw)\n",
    "\n",
    "# Design matrices\n",
    "X_design = sm.add_constant(X)\n",
    "x_vals_std = np.linspace(X.min(), X.max(), 500).reshape(-1, 1)\n",
    "x_vals_intercept = sm.add_constant(x_vals_std)\n",
    "x_vals_original = scaler.inverse_transform(x_vals_std)\n",
    "\n",
    "# ---------------- MLE Fit ---------------- #\n",
    "model = sm.Logit(flags, X_design)\n",
    "result = model.fit(disp=0)\n",
    "params_mle = result.params\n",
    "probs = result.predict(X_design)\n",
    "\n",
    "# ---------------- Parametric Bootstrap ---------------- #\n",
    "n_bootstraps = 2000\n",
    "boot_intercepts = []\n",
    "boot_slopes = []\n",
    "predicted_probs_boot = []\n",
    "attempts = 0\n",
    "max_attempts = 5000\n",
    "success = 0\n",
    "\n",
    "while success < n_bootstraps and attempts < max_attempts:\n",
    "    attempts += 1\n",
    "    y_sim = np.random.binomial(n=1, p=probs)\n",
    "\n",
    "    try:\n",
    "        model_sim = sm.Logit(y_sim, X_design)\n",
    "        result_sim = model_sim.fit(disp=0)\n",
    "\n",
    "        if (not result_sim.mle_retvals['converged'] or \n",
    "            np.abs(result_sim.params[1]) > 20 or\n",
    "            np.any(np.isnan(result_sim.params))):\n",
    "            continue\n",
    "\n",
    "        boot_intercepts.append(result_sim.params[0])\n",
    "        boot_slopes.append(result_sim.params[1])\n",
    "\n",
    "        probs_sim = result_sim.predict(x_vals_intercept)\n",
    "        probs_smooth = gaussian_filter1d(probs_sim, sigma=0.5)\n",
    "\n",
    "        if not (0.05 < probs_smooth.mean() < 0.95 and probs_smooth.max() - probs_smooth.min() > 0.2):\n",
    "            continue\n",
    "\n",
    "        predicted_probs_boot.append(probs_smooth)\n",
    "        success += 1\n",
    "\n",
    "    except Exception:\n",
    "        continue\n",
    "\n",
    "predicted_probs_boot = np.array(predicted_probs_boot)\n",
    "lower_boot = gaussian_filter1d(np.percentile(predicted_probs_boot, 5, axis=0), sigma=0.5)\n",
    "upper_boot = gaussian_filter1d(np.percentile(predicted_probs_boot, 95, axis=0), sigma=0.5)\n",
    "mean_probs_boot = predicted_probs_boot.mean(axis=0)\n",
    "\n",
    "# ---------------- Delta Method CI ---------------- #\n",
    "cov_params = result.cov_params()\n",
    "linear_pred = x_vals_intercept @ params_mle\n",
    "\n",
    "def logistic(z): return 1 / (1 + np.exp(-z))\n",
    "\n",
    "g = x_vals_intercept * (logistic(linear_pred) * (1 - logistic(linear_pred)))[:, None]\n",
    "se = np.sqrt(np.sum(g @ cov_params * g, axis=1))\n",
    "z = 1.96\n",
    "predicted_probs_delta = result.predict(x_vals_intercept)\n",
    "lower_delta = np.clip(predicted_probs_delta - z * se, 0, 1)\n",
    "upper_delta = np.clip(predicted_probs_delta + z * se, 0, 1)\n",
    "\n",
    "# ---------------- Plot ---------------- #\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.title(\"Parametric Bootstrap vs Delta Method\")\n",
    "plt.xlabel(r\"$x = \\max_{visit} SUV(visit, p)$\")\n",
    "plt.ylabel(r\"$P(\\mathrm{AE} \\mid X = x)$\")\n",
    "\n",
    "plt.scatter(X_raw[flags == 0], flags[flags == 0], label=\"NC\", color='blue', alpha=0.5)\n",
    "plt.scatter(X_raw[flags == 1], flags[flags == 1], label=\"AE\", color='red', alpha=0.5)\n",
    "\n",
    "plt.plot(x_vals_original, predicted_probs_delta, color='green', label=\"MLE (Delta Method)\")\n",
    "plt.fill_between(x_vals_original.flatten(), lower_delta, upper_delta, color='green', alpha=0.2, label=\"95% CI (Delta)\")\n",
    "\n",
    "plt.plot(x_vals_original, mean_probs_boot, color='orange', label=\"MLE (Parametric Bootstrap)\", linewidth=2)\n",
    "plt.fill_between(x_vals_original.flatten(), lower_boot, upper_boot, color='orange', alpha=0.2, label=\"95% CI (Bootstrap)\")\n",
    "\n",
    "plt.grid(True)\n",
    "plt.legend(loc='center right')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# ---------------- Summary Stats ---------------- #\n",
    "boot_intercepts = np.array(boot_intercepts)\n",
    "boot_slopes = np.array(boot_slopes)\n",
    "\n",
    "print(\"\\nParametric Bootstrap Results:\")\n",
    "print(f\"Intercept: mean = {np.mean(boot_intercepts):.4f}, 95% CI = [{np.percentile(boot_intercepts, 2.5):.4f}, {np.percentile(boot_intercepts, 97.5):.4f}]\")\n",
    "print(f\"Slope:     mean = {np.mean(boot_slopes):.4f}, 95% CI = [{np.percentile(boot_slopes, 2.5):.4f}, {np.percentile(boot_slopes, 97.5):.4f}]\")\n",
    "\n",
    "print(\"\\nDelta Method Results:\")\n",
    "se_delta = np.sqrt(np.diag(cov_params))\n",
    "print(f\"Intercept: {params_mle[0]:.4f} ± {z*se_delta[0]:.4f} (95% CI: [{params_mle[0] - z*se_delta[0]:.4f}, {params_mle[0] + z*se_delta[0]:.4f}])\")\n",
    "print(f\"Slope:     {params_mle[1]:.4f} ± {z*se_delta[1]:.4f} (95% CI: [{params_mle[1] - z*se_delta[1]:.4f}, {params_mle[1] + z*se_delta[1]:.4f}])\")\n",
    "\n",
    "# ---------------- Model Summary ---------------- #\n",
    "ll_model = result.llf\n",
    "ll_null = result.llnull\n",
    "df_model = result.df_model\n",
    "aic = result.aic\n",
    "bic = result.bic\n",
    "lr_stat = 2 * (ll_model - ll_null)\n",
    "lr_df = df_model\n",
    "lr_pvalue = chi2.sf(lr_stat, lr_df)\n",
    "\n",
    "print(\"\\nModel Summary Statistics:\")\n",
    "print(f\"AIC: {aic:.2f}\")\n",
    "print(f\"BIC: {bic:.2f}\")\n",
    "print(f\"Log-Likelihood (Model): {ll_model:.2f}\")\n",
    "print(f\"Log-Likelihood (Null):  {ll_null:.2f}\")\n",
    "print(f\"Chi2 Statistic: {lr_stat:.2f}, df = {lr_df}, p-value = {lr_pvalue:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "278ae10e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model converged successfully.\n",
      "Log-Likelihood (model): -40.2026\n",
      "AIC (manual): 84.4052\n",
      "BIC (manual): 88.5260\n",
      "\n",
      "Model Fit Summary:\n",
      " Log-Likelihood (Model)  AIC (manual)  BIC (manual)  Converged\n",
      "             -40.202575     84.405151     88.526037       True\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:2385: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:2443: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q * linpred)))\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:2385: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:2443: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q * linpred)))\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import io\n",
    "from scipy.ndimage import gaussian_filter1d\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import statsmodels.api as sm\n",
    "import pandas as pd\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
    "\n",
    "# ---------------- Load Data ---------------- #\n",
    "data_path = \"../data/\"\n",
    "suv = io.loadmat(data_path + \"suv_percentilesSLOthenUWM.mat\")['lung_SUVperc_COMBINED'][0:58, :, :]\n",
    "flags = io.loadmat(data_path + \"flags_combined.mat\")['flags'][0:58, 3]\n",
    "\n",
    "# ---------------- Preprocessing ---------------- #\n",
    "p = 94\n",
    "X_raw = np.nanmax(suv[:, :, p], axis=1).reshape(-1, 1)\n",
    "valid = ~np.isnan(X_raw).flatten()\n",
    "X_raw = X_raw[valid]\n",
    "flags = flags[valid]\n",
    "\n",
    "# Standardize and clip\n",
    "scaler = StandardScaler()\n",
    "X = scaler.fit_transform(X_raw)\n",
    "X = np.clip(X, -10, 10)\n",
    "\n",
    "# Extended grid for plotting (x from 0.5 to 6.0)\n",
    "x_plot_original = np.linspace(0.5, 6.0, 500).reshape(-1, 1)\n",
    "x_vals_std = scaler.transform(x_plot_original)\n",
    "x_vals_original = x_plot_original\n",
    "\n",
    "# ---------------- L2-Regularized Logistic Regression ---------------- #\n",
    "clf = LogisticRegression(penalty='l2', C=1.0, solver='lbfgs')\n",
    "clf.fit(X, flags)\n",
    "pred_probs = clf.predict_proba(x_vals_std)[:, 1]\n",
    "\n",
    "# ---------------- Delta Method ---------------- #\n",
    "intercept = clf.intercept_[0]\n",
    "coef = clf.coef_[0][0]\n",
    "linear_pred = intercept + coef * x_vals_std.flatten()\n",
    "p = 1 / (1 + np.exp(-linear_pred))\n",
    "g = np.vstack([p * (1 - p), p * (1 - p) * x_vals_std.flatten()]).T\n",
    "Hessian_approx = np.dot(g.T, g)\n",
    "cov = np.linalg.inv(Hessian_approx)\n",
    "z = 1.96\n",
    "se = np.sqrt(np.sum(g @ cov * g, axis=1))\n",
    "lower_delta = np.clip(p - z * se, 0, 1)\n",
    "upper_delta = np.clip(p + z * se, 0, 1)\n",
    "\n",
    "# ---------------- Parametric Bootstrap ---------------- #\n",
    "n_boot = 2000\n",
    "boot_probs = []\n",
    "\n",
    "for _ in range(n_boot):\n",
    "    y_sim = np.random.binomial(n=1, p=clf.predict_proba(X)[:, 1])\n",
    "    clf_sim = LogisticRegression(penalty='l2', C=1.0, solver='lbfgs')\n",
    "    clf_sim.fit(X, y_sim)\n",
    "    sim_probs = clf_sim.predict_proba(x_vals_std)[:, 1]\n",
    "    sim_probs = gaussian_filter1d(sim_probs, sigma=0.5, truncate=2.0)\n",
    "    boot_probs.append(sim_probs)\n",
    "\n",
    "boot_probs = np.array(boot_probs)\n",
    "lower_boot = np.percentile(boot_probs, 5, axis=0)\n",
    "upper_boot = np.percentile(boot_probs, 95, axis=0)\n",
    "mean_probs_boot = boot_probs.mean(axis=0)\n",
    "\n",
    "# ---------------- Plot ---------------- #\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.title(\"Logistic Regression with L2 Regularization (Bootstrap vs Delta)\")\n",
    "plt.xlabel(r\"$x = \\max_{visit} SUV(visit, p)$\")\n",
    "plt.ylabel(r\"$P(\\mathrm{AE} \\mid X = x)$\")\n",
    "\n",
    "plt.scatter(X_raw[flags == 0], flags[flags == 0], label=\"NC\", color='blue', alpha=0.5)\n",
    "plt.scatter(X_raw[flags == 1], flags[flags == 1], label=\"AE\", color='red', alpha=0.5)\n",
    "\n",
    "plt.plot(x_vals_original, p, color='green', label=\"MLE (Delta Method)\")\n",
    "plt.fill_between(x_vals_original.flatten(), lower_delta, upper_delta, color='green', alpha=0.2, label=\"95% CI (Delta)\")\n",
    "\n",
    "plt.plot(x_vals_original, mean_probs_boot, color='orange', label=\"MLE (Bootstrap)\", linewidth=2)\n",
    "plt.fill_between(x_vals_original.flatten(), lower_boot, upper_boot, color='orange', alpha=0.2, label=\"95% CI (Bootstrap)\")\n",
    "\n",
    "plt.grid(True)\n",
    "plt.legend(loc='center right')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# ---------------- Model Fit Summary (statsmodels) ---------------- #\n",
    "# Clip extreme values in X to avoid overflow in statsmodels\n",
    "X_clipped = np.clip(X, -5, 5)  # or even [-4, 4] if still problematic\n",
    "X_design = sm.add_constant(X_clipped)\n",
    "\n",
    "logit_model = sm.Logit(flags, X_design)\n",
    "\n",
    "try:\n",
    "    result = logit_model.fit(disp=False)\n",
    "except Exception as e:\n",
    "    print(\"Model failed to converge:\", e)\n",
    "    result = None\n",
    "\n",
    "\n",
    "llf = result.llf\n",
    "ll_null = result.llnull\n",
    "aic = result.aic\n",
    "bic = result.bic\n",
    "lr_stat = result.llr\n",
    "lr_df = result.df_model\n",
    "lr_pvalue = result.llr_pvalue\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# ---------------- Model Fit Summary (statsmodels with regularization) ---------------- #\n",
    "# Tighter clipping to avoid overflow\n",
    "X_clipped = np.clip(X, -4, 4)\n",
    "X_design = sm.add_constant(X_clipped)\n",
    "\n",
    "logit_model = sm.Logit(flags, X_design)\n",
    "\n",
    "try:\n",
    "    # Use penalized logistic regression fit for stability\n",
    "    result = logit_model.fit_regularized(alpha=1e-4, disp=False)\n",
    "except Exception as e:\n",
    "    print(\"Model failed to converge:\", e)\n",
    "    result = None\n",
    "\n",
    "if result is not None:\n",
    "    converged = result.mle_retvals.get('converged', True)  # default True if not available\n",
    "\n",
    "    if converged:\n",
    "        llf = result.llf\n",
    "        k = X_design.shape[1]  # number of parameters including intercept\n",
    "        n = X_design.shape[0]\n",
    "\n",
    "        # Manual calculation of AIC and BIC\n",
    "        aic_manual = 2 * k - 2 * llf\n",
    "        bic_manual = np.log(n) * k - 2 * llf\n",
    "\n",
    "        # You can still access result.aic and result.bic but they may not be reliable\n",
    "        print(f\"Model converged successfully.\")\n",
    "        print(f\"Log-Likelihood (model): {llf:.4f}\")\n",
    "        print(f\"AIC (manual): {aic_manual:.4f}\")\n",
    "        print(f\"BIC (manual): {bic_manual:.4f}\")\n",
    "\n",
    "        # Optionally, prepare a summary table\n",
    "        summary_stats = {\n",
    "            \"Log-Likelihood (Model)\": [llf],\n",
    "            \"AIC (manual)\": [aic_manual],\n",
    "            \"BIC (manual)\": [bic_manual],\n",
    "            \"Converged\": [converged]\n",
    "        }\n",
    "        summary_table = pd.DataFrame(summary_stats)\n",
    "        print(\"\\nModel Fit Summary:\")\n",
    "        print(summary_table.to_string(index=False))\n",
    "\n",
    "    else:\n",
    "        print(\"Model did not converge; AIC/BIC undefined.\")\n",
    "else:\n",
    "    print(\"No valid model result; cannot compute AIC/BIC.\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "b9041357",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Summary (Log SUV):\n",
      "Log-Likelihood: -40.2026\n",
      "AIC: 84.4052\n",
      "BIC: 88.5260\n",
      "Confidence Intervals:\n",
      "                 2.5%     97.5%\n",
      "Intercept   -0.514718  0.514718\n",
      "Coefficient -0.525720  0.525720\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:2385: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:2443: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q * linpred)))\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import io\n",
    "from scipy.ndimage import gaussian_filter1d\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import statsmodels.api as sm\n",
    "import pandas as pd\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
    "\n",
    "# ---------------- Load Data ---------------- #\n",
    "data_path = \"../data/\"\n",
    "suv = io.loadmat(data_path + \"suv_percentilesSLOthenUWM.mat\")[\"lung_SUVperc_COMBINED\"][0:58, :, :]\n",
    "flags = io.loadmat(data_path + \"flags_combined.mat\")[\"flags\"][0:58, 3]\n",
    "\n",
    "# ---------------- Preprocessing ---------------- #\n",
    "p = 94\n",
    "X_raw = np.nanmax(suv[:, :, p], axis=1).reshape(-1, 1)\n",
    "valid = ~np.isnan(X_raw).flatten()\n",
    "X_raw = X_raw[valid]\n",
    "flags = flags[valid]\n",
    "\n",
    "# Log transform\n",
    "X_log_raw = np.log(X_raw)\n",
    "\n",
    "# Standardize\n",
    "scaler_log = StandardScaler()\n",
    "X_log = scaler_log.fit_transform(X_log_raw)\n",
    "X_log = np.clip(X_log, -10, 10)\n",
    "\n",
    "# Extended grid for plotting\n",
    "x_plot_original = np.linspace(X_raw.min() * 0.8, X_raw.max() * 1.2, 500).reshape(-1, 1)\n",
    "x_plot_log = np.log(x_plot_original)\n",
    "x_vals_std_log = scaler_log.transform(x_plot_log)\n",
    "\n",
    "# ---------------- Logistic Regression (Log SUV) ---------------- #\n",
    "clf_log = LogisticRegression(penalty=\"l2\", C=1.0, solver=\"lbfgs\")\n",
    "clf_log.fit(X_log, flags)\n",
    "p_log = clf_log.predict_proba(x_vals_std_log)[:, 1]\n",
    "\n",
    "# ---------------- Delta Method (Log SUV) ---------------- #\n",
    "intercept_log = clf_log.intercept_[0]\n",
    "coef_log = clf_log.coef_[0][0]\n",
    "linear_pred_log = intercept_log + coef_log * x_vals_std_log.flatten()\n",
    "pred_log = 1 / (1 + np.exp(-linear_pred_log))\n",
    "g_log = np.vstack([pred_log * (1 - pred_log), pred_log * (1 - pred_log) * x_vals_std_log.flatten()]).T\n",
    "Hessian_log = np.dot(g_log.T, g_log)\n",
    "cov_log = np.linalg.inv(Hessian_log)\n",
    "z = 1.96\n",
    "se_log = np.sqrt(np.sum(g_log @ cov_log * g_log, axis=1))\n",
    "lower_delta_log = np.clip(pred_log - z * se_log, 0, 1)\n",
    "upper_delta_log = np.clip(pred_log + z * se_log, 0, 1)\n",
    "\n",
    "# ---------------- Bootstrap ---------------- #\n",
    "n_boot = 2000\n",
    "boot_probs_log = []\n",
    "for _ in range(n_boot):\n",
    "    y_sim = np.random.binomial(n=1, p=clf_log.predict_proba(X_log)[:, 1])\n",
    "    clf_sim = LogisticRegression(penalty=\"l2\", C=1.0, solver=\"lbfgs\")\n",
    "    clf_sim.fit(X_log, y_sim)\n",
    "    sim_probs = clf_sim.predict_proba(x_vals_std_log)[:, 1]\n",
    "    sim_probs = gaussian_filter1d(sim_probs, sigma=0.5, truncate=2.0)\n",
    "    boot_probs_log.append(sim_probs)\n",
    "\n",
    "boot_probs_log = np.array(boot_probs_log)\n",
    "lower_boot_log = np.percentile(boot_probs_log, 5, axis=0)\n",
    "upper_boot_log = np.percentile(boot_probs_log, 95, axis=0)\n",
    "mean_probs_boot_log = boot_probs_log.mean(axis=0)\n",
    "\n",
    "# ---------------- Plot (Log SUV) ---------------- #\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.title(\"Logistic Regression with Log(SUV)\")\n",
    "plt.xlabel(r\"$\\log(SUV)$\")\n",
    "plt.ylabel(r\"$P(\\mathrm{AE} \\mid \\log(SUV))$\")\n",
    "\n",
    "plt.scatter(X_log_raw[flags == 0], flags[flags == 0], label=\"NC\", color='blue', alpha=0.5)\n",
    "plt.scatter(X_log_raw[flags == 1], flags[flags == 1], label=\"AE\", color='red', alpha=0.5)\n",
    "\n",
    "plt.plot(x_plot_log, pred_log, color='green', label=\"MLE (Delta Method)\")\n",
    "plt.fill_between(x_plot_log.flatten(), lower_delta_log, upper_delta_log, color='green', alpha=0.2, label=\"95% CI (Delta)\")\n",
    "\n",
    "plt.plot(x_plot_log, mean_probs_boot_log, color='orange', label=\"MLE (Bootstrap)\", linewidth=2)\n",
    "plt.fill_between(x_plot_log.flatten(), lower_boot_log, upper_boot_log, color='orange', alpha=0.2, label=\"95% CI (Bootstrap)\")\n",
    "\n",
    "plt.grid(True)\n",
    "plt.legend(loc='center right')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# ---------------- statsmodels Summary (Log SUV) ---------------- #\n",
    "X_clipped_log = np.clip(X_log, -4, 4)\n",
    "X_design_log = sm.add_constant(X_clipped_log)\n",
    "logit_model_log = sm.Logit(flags, X_design_log)\n",
    "try:\n",
    "    res_log = logit_model_log.fit_regularized(alpha=1e-4, disp=False)\n",
    "except Exception as e:\n",
    "    print(\"Model failed to converge:\", e)\n",
    "    res_log = None\n",
    "\n",
    "# ---------------- Model Summary Function ---------------- #\n",
    "def summarize_results(res, X_design):\n",
    "    if res is None:\n",
    "        return None\n",
    "    converged = res.mle_retvals.get(\"converged\", True)\n",
    "    if not converged:\n",
    "        print(\"Model did not converge.\")\n",
    "        return None\n",
    "    llf = res.llf\n",
    "    k = X_design.shape[1]\n",
    "    n = X_design.shape[0]\n",
    "    aic = 2 * k - 2 * llf\n",
    "    bic = np.log(n) * k - 2 * llf\n",
    "    conf_array = res.conf_int()\n",
    "    conf = pd.DataFrame(conf_array, columns=[\"2.5%\", \"97.5%\"], index=[\"Intercept\", \"Coefficient\"])\n",
    "    return {\n",
    "        \"Log-Likelihood\": llf,\n",
    "        \"AIC\": aic,\n",
    "        \"BIC\": bic,\n",
    "        \"Conf_Int\": conf\n",
    "    }\n",
    "\n",
    "# ---------------- Print Summary ---------------- #\n",
    "summary_log = summarize_results(res_log, X_design_log)\n",
    "\n",
    "if summary_log is not None:\n",
    "    print(\"Model Summary (Log SUV):\")\n",
    "    print(f\"Log-Likelihood: {summary_log['Log-Likelihood']:.4f}\")\n",
    "    print(f\"AIC: {summary_log['AIC']:.4f}\")\n",
    "    print(f\"BIC: {summary_log['BIC']:.4f}\")\n",
    "    print(\"Confidence Intervals:\")\n",
    "    print(summary_log['Conf_Int']) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f51322a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Raw SUV Model:\n",
      "Log-Likelihood: -40.2026\n",
      "AIC: 84.4052\n",
      "BIC: 88.5260\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:2385: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:2443: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q * linpred)))\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c6209f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model converged successfully.\n",
      "Log-Likelihood: -inf\n",
      "AIC: inf\n",
      "BIC: inf\n",
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   No. Observations:                   58\n",
      "Model:                          Logit   Df Residuals:                       56\n",
      "Method:                           MLE   Df Model:                            1\n",
      "Date:                Sat, 28 Jun 2025   Pseudo R-squ.:                     inf\n",
      "Time:                        11:16:17   Log-Likelihood:                   -inf\n",
      "converged:                       True   LL-Null:                        0.0000\n",
      "Covariance Type:            nonrobust   LLR p-value:                     1.000\n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         -4.7619      1.470     -3.239      0.001      -7.643      -1.881\n",
      "x1             3.3873      1.110      3.051      0.002       1.211       5.563\n",
      "==============================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:2385: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "c:\\Users\\zahra\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:2443: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q * linpred)))\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "01db1ab7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- DEBUG: Predictor Stats ---\n",
      "Raw SUV Mean: 1.4062, Std: 0.4790\n",
      "Log SUV Mean: 0.3014, Std: 0.2616\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n--- DEBUG: Predictor Stats ---\")\n",
    "print(f\"Raw SUV Mean: {np.mean(X_raw):.4f}, Std: {np.std(X_raw):.4f}\")\n",
    "print(f\"Log SUV Mean: {np.mean(X_log_transformed):.4f}, Std: {np.std(X_log_transformed):.4f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "91a184fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Raw Design Matrix Head:\n",
      "[[ 1.          1.45953197]\n",
      " [ 1.          0.2717923 ]\n",
      " [ 1.         -0.27514214]\n",
      " [ 1.         -0.45566563]\n",
      " [ 1.          0.56042544]]\n",
      "Log Design Matrix Head:\n",
      "[[ 1.          1.69372042]\n",
      " [ 1.          0.48942419]\n",
      " [ 1.         -0.22525798]\n",
      " [ 1.         -0.49385899]\n",
      " [ 1.          0.81881789]]\n"
     ]
    }
   ],
   "source": [
    "print(\"Raw Design Matrix Head:\")\n",
    "print(X_raw_design[:5])\n",
    "\n",
    "print(\"Log Design Matrix Head:\")\n",
    "print(X_log_design[:5])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "4d2695ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.ndimage import gaussian_filter1d\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import statsmodels.api as sm\n",
    "import pandas as pd\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
    "\n",
    "# ... [your data loading and preprocessing code remains unchanged] ...\n",
    "\n",
    "# Extended x-axis in log scale from -1 to 3\n",
    "x_plot_log = np.linspace(-1, 3, 500).reshape(-1, 1)\n",
    "\n",
    "# Standardize these extended values using your fitted scaler\n",
    "x_vals_std_log = scaler_log.transform(x_plot_log)\n",
    "x_vals_std_log = np.clip(x_vals_std_log, -10, 10)\n",
    "\n",
    "# Logistic regression prediction on extended grid\n",
    "p_log = clf_log.predict_proba(x_vals_std_log)[:, 1]\n",
    "\n",
    "# Delta Method CI on extended grid\n",
    "intercept_log = clf_log.intercept_[0]\n",
    "coef_log = clf_log.coef_[0][0]\n",
    "linear_pred_log = intercept_log + coef_log * x_vals_std_log.flatten()\n",
    "pred_log = 1 / (1 + np.exp(-linear_pred_log))\n",
    "\n",
    "g_log = np.vstack([pred_log * (1 - pred_log),\n",
    "                   pred_log * (1 - pred_log) * x_vals_std_log.flatten()]).T\n",
    "Hessian_log = np.dot(g_log.T, g_log)\n",
    "cov_log = np.linalg.inv(Hessian_log)\n",
    "z = 1.96\n",
    "se_log = np.sqrt(np.sum(g_log @ cov_log * g_log, axis=1))\n",
    "lower_delta_log = np.clip(pred_log - z * se_log, 0, 1)\n",
    "upper_delta_log = np.clip(pred_log + z * se_log, 0, 1)\n",
    "\n",
    "# Bootstrap on extended grid\n",
    "n_boot = 2000\n",
    "boot_probs_log = []\n",
    "for _ in range(n_boot):\n",
    "    y_sim = np.random.binomial(n=1, p=clf_log.predict_proba(X_log)[:, 1])\n",
    "    clf_sim = LogisticRegression(penalty=\"l2\", C=1.0, solver=\"lbfgs\")\n",
    "    clf_sim.fit(X_log, y_sim)\n",
    "    sim_probs = clf_sim.predict_proba(x_vals_std_log)[:, 1]\n",
    "    sim_probs = gaussian_filter1d(sim_probs, sigma=0.5, truncate=2.0)\n",
    "    boot_probs_log.append(sim_probs)\n",
    "\n",
    "boot_probs_log = np.array(boot_probs_log)\n",
    "lower_boot_log = np.percentile(boot_probs_log, 5, axis=0)\n",
    "upper_boot_log = np.percentile(boot_probs_log, 95, axis=0)\n",
    "mean_probs_boot_log = boot_probs_log.mean(axis=0)\n",
    "\n",
    "# Plot all\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.title(\"Parametric Bootstrapp vs DElta Method\")\n",
    "plt.xlabel(r\"$\\log(SUV)$\")\n",
    "plt.ylabel(r\"$P(\\mathrm{AE} \\mid \\log(SUV))$\")\n",
    "\n",
    "# Plot original data points\n",
    "plt.scatter(X_log_raw[flags == 0], flags[flags == 0], label=\"NC\", color='blue', alpha=0.5)\n",
    "plt.scatter(X_log_raw[flags == 1], flags[flags == 1], label=\"AE\", color='red', alpha=0.5)\n",
    "\n",
    "# Plot MLE prediction (Delta method)\n",
    "plt.plot(x_plot_log, pred_log, color='green', label=\"MLE (Delta Method)\")\n",
    "plt.fill_between(x_plot_log.flatten(), lower_delta_log, upper_delta_log,\n",
    "                 color='green', alpha=0.2, label=\"95% CI (Delta)\")\n",
    "\n",
    "# Plot Bootstrap mean and CI\n",
    "plt.plot(x_plot_log, mean_probs_boot_log, color='orange', label=\"MLE (Bootstrap)\", linewidth=2)\n",
    "plt.fill_between(x_plot_log.flatten(), lower_boot_log, upper_boot_log,\n",
    "                 color='orange', alpha=0.2, label=\"95% CI (Bootstrap)\")\n",
    "\n",
    "plt.grid(True)\n",
    "plt.legend(loc='center right')\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  }
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