Some update

python-paper/paper-bayesian.ipynb

@@ -282,7 +282,9 @@
     "        # helper function to sample with replacement of same size sample\n",
     "        R = lambda X: X[rng.integers(len(X), size=len(X))]\n",
     "\n",
-    "        ps = np.array([np.concatenate((myfitter(R(X_NC), fun, False), myfitter(R(X_AE), fun, False))) for _ in range(Nsamples)])\n",
+    "        ps = np.array([np.concatenate((myfitter(R(X_NC), fun, False), \n",
+    "                                       myfitter(R(X_AE), fun, False))) \n",
+    "                       for _ in range(Nsamples)])\n",
     "    elif bootstrapping == \"param\":\n",
     "        # approximationg parameters with multivatiate distribution\n",
     "        Z = np.zeros((len(popt_NC), len(popt_AE)))\n",

python-paper/paper-bayesian_constraints.ipynb

@@ -111,7 +111,7 @@
    "source": [
     "wd = os.getcwd()   # working directory\n",
     "\n",
-    "topic = \"bayes\"\n",
+    "topic = \"bayes_c\"\n",
     "\n",
     "if choice_setup == 0:\n",
     "    #data_path = \"/home/marija/maki/\"\n",
@@ -204,7 +204,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 1,
    "id": "8262d64c",
    "metadata": {
     "executionInfo": {
@@ -282,7 +282,9 @@
     "        # helper function to sample with replacement of same size sample\n",
     "        R = lambda X: X[rng.integers(len(X), size=len(X))]\n",
     "\n",
-    "        ps = np.array([np.concatenate((myfitter(R(X_NC), fun, False), myfitter(R(X_AE), fun, False))) for _ in range(Nsamples)])\n",
+    "        ps = np.array([np.concatenate((myfitter(R(X_NC), fun, False), \n",
+    "                                       myfitter(R(X_AE), fun, False))) \n",
+    "                       for _ in range(Nsamples)])\n",
     "    elif bootstrapping == \"param\":\n",
     "        # approximationg parameters with multivatiate distribution\n",
     "        Z = np.zeros((len(popt_NC), len(popt_AE)))\n",

python-paper/paper-logit_regression.ipynb

@@ -503,7 +503,7 @@
     "#   f(x|p*)    p ~ N(p*,C_p)\n",
     "#\n",
     "\n",
-    "f = lambda x,p: 1/(1 + np.exp(-p[0] - p[1]*x))\n",
+    "f = lambda x, p: 1/(1 + np.exp(-p[0] - p[1]*x))\n",
     "fopt = lambda x: 1/(1 + np.exp(-logitParams[0] - logitParams[1]*x))\n",
     "\n",
     "#generate parameters and points in x axis\n",