def recommend(self, u, q):
self.average_weights = self._load_matrix(WEIGHTS_FILE)
self.average_bv = self._load_matrix(VISIBLE_BIAS_FILE)
self.average_bh = self._load_matrix(HIDDEN_BIAS_FILE)
#x is the input (the user we are predicting for), w is the weights of just the items rated by user x and bv is
# just the visible biases of the corresponding to the items rated by the user
x, w, bv = self._ratings_to_softmax_units(self.dataset.training_X[:,u], q)
#bh is the same size for every user because they correspond to the hidden units
bh = self.average_bh
#Returns the new index for the query q. The index changes because we reomved all unrated items.
#The units for the query are changed to 5 -1s as a placeholder
x, new_q = self._new_index(x)
results = []
#For each rating 1-5 we sample the RBM with the corresponding input.
for r in range(RATING_MAX):
x = self._set_rating(x, new_q, (r+1))
rbm = RBM(hidden_layer_n=HIDDEN_LAYER_N,iterations=ITERATIONS,dataset=x, training=False)
sample = rbm.run_prediction(x, w, bh, bv)
results.append(sample)
#Get the expected output from each of the probablitlies of each input
probs = self._get_probabilities(results, new_q)
prediction = self._expected_value(self.softmax(probs))
print("Prediction: user " + str(u) + " will give movie: " + str(q) + " rating: " + str(prediction))
