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@rubik
Last active October 27, 2017 15:23
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import numpy as np
import pymc3 as pm
import theano as t
train_new = np.load('train_new.npy')
targets = np.load('y.npy')
X = t.shared(train_new)
features = list(map(str, range(train_new.shape[1])))
with pm.Model() as logistic_model:
# we won't estimate the intercept because the data is centered
glm = pm.glm.GLM(X, targets, labels=features,
intercept=False, family='binomial')
trace = pm.sample(3000, tune=3000, jobs=-1)
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