def classify(self,X):
transformed = self.first_pipeline.transform(X)
transformed = np.concatenate((transformed,[[0]*10]*len(transformed)),axis=1)
# The inverse of rbm_3 to go from hidden layer to visible layer
rbm_aux = BernoulliRBM()
rbm_aux.intercept_hidden_ = self.rbm_3.intercept_visible_
rbm_aux.intercept_visible_ = self.rbm_3.intercept_hidden_
rbm_aux.components_ = np.transpose(self.rbm_3.components_)
results = rbm_aux.transform(self.rbm_3.transform(transformed))
results = results[:,-10:]
return np.argmax(results,axis=1)
def classify(self, X):
transformed = self.first_pipeline.transform(X)
transformed = np.concatenate(
(transformed, [[0] * 10] * len(transformed)), axis=1)
# The inverse of rbm_3 to go from hidden layer to visible layer
rbm_aux = BernoulliRBM()
rbm_aux.intercept_hidden_ = self.rbm_3.intercept_visible_
rbm_aux.intercept_visible_ = self.rbm_3.intercept_hidden_
rbm_aux.components_ = np.transpose(self.rbm_3.components_)
results = rbm_aux.transform(self.rbm_3.transform(transformed))
results = results[:, -10:]
return np.argmax(results, axis=1)
def get_likelihood(data, W, vb, hb):
rbm = BernoulliRBM(n_components=W.shape[1])
rbm.components_ = W.T
rbm.intercept_hidden_ = hb
rbm.intercept_visible_ = vb
return rbm.score_samples(data).mean()
