def train(self):
#wb is a dictionary that stores the average Weight matrices and Bias matrices. The keys are where the
#files are stored
self.wb = {}
"""
For each user an RBM will be created.
"""
for i in range(self.dataset.training_X.shape[1]):
user = self.dataset.training_X[:,i]
rbm = RBM(hidden_layer_n=HIDDEN_LAYER_N,iterations=ITERATIONS,dataset=user)
rbm.run()
#After the an RBM is run the weights and biases are re-added to complete set.
self.all_weights.append(rbm.full_weights)
self.all_bv.append(rbm.full_bv)
self.all_bh.append(rbm.full_bh)
print("RBM number: " + str(i))
#Average all the weights and all the biases from all the RBM's (With each RBM corresponding to a user)
self.wb[WEIGHTS_FILE] = self.average_matrices(self.all_weights)
self.wb[VISIBLE_BIAS_FILE] = self.average_matrices(self.all_bv)
self.wb[HIDDEN_BIAS_FILE] = self.average_matrices(self.all_bh)
#Training can take a long time so we can save the weights and biases
self.save_matrix(WEIGHTS_FILE)
self.save_matrix(VISIBLE_BIAS_FILE)
self.save_matrix(HIDDEN_BIAS_FILE)
