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@@ -4,10 +4,13 @@ import matplotlib.pyplot as plt
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from scipy import optimize
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from scipy.special import betaln, gammaln
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import scipy.io as io
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+import os
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-data_path = "../../data/"
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-suv = io.loadmat(data_path + "suv_percentilesSLOthenUWM.mat")['lung_SUVperc_COMBINED'][0:58, :, :]
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-flags = io.loadmat(data_path + "flags_combined.mat")['flags'][0:58, 3] # 0=NC, 1=AE
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+# determine git root
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+root = os.popen("git rev-parse --show-toplevel").read().strip()
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+
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+suv = io.loadmat(os.path.join(root, "data", "suv_percentilesSLOthenUWM.mat"))['lung_SUVperc_COMBINED'][0:58, :, :]
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+flags = io.loadmat(os.path.join(root, "data", "flags_combined.mat"))['flags'][0:58, 3] # 0=NC, 1=AE
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# Feature X = max SUV_94 per subject; label y = flags
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X = np.nanmax(suv[:, :, 94], axis=1).astype(float).ravel()
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@@ -69,8 +72,8 @@ TAU = 25.0 # shrink toward empirical AE rate
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alpha = max(TAU * p_emp, 1e-6)
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beta = max(TAU * (1.0 - p_emp), 1e-6)
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-#prior_r = None
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-prior_r = (1.01, 1.01)
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+prior_r = None
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+#prior_r = (1.01, 1.01)
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#prior_r = (3, 3)
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# Objective: negative log-posterior (likelihood + Beta prior on p)
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