mat_c = np.array(Matrix_C)
mat_r = np.array(Matrix_R)
print(mat_c.shape, " ", U_mat.shape, " ", mat_r.shape)
Cur_mat = np.matmul(Matrix_C, U_mat)
Cur_mat = np.matmul(Cur_mat, Matrix_R)
print("Final Matrix Shape")
print(Cur_mat.shape)
a = Cur_mat[0, 1]
Cur_mat = np.add(Cur_mat, means_mat)
return Cur_mat
train2 = recsys_utils.read_train()
test = recsys_utils.read_test_table()
truth = test['rating'].as_matrix()
user_map = recsys_utils.read_user_map()
movie_map = recsys_utils.read_movie_map()
print("Train Data Shape")
print(train2.shape)
means_mat = Usr_Mean(train2)
print("Done till here")
train = Subtract_Mean_value(train2)
'''Calcuating Frobnieus Norm rowwise and column wise'''
start_time_user = time()
if c >= n:
break
elif temp_rating_dist[i][1][0] != 0:
rating += temp_rating_dist[i][1][0] * temp_rating_dist[i][
0][0]
den += temp_rating_dist[i][0][0]
c += 1
if den == 0:
den = 1
rating = rating / den
pred.append(rating)
return np.array(pred)
if __name__ == '__main__':
training = recsys_utils.read_train(sparse=True)
testing = recsys_utils.read_test_table().head(10000)
truth = testing['rating'].head(10000).as_matrix()
user_mappings = recsys_utils.read_user_map()
movie_mappings = recsys_utils.read_movie_map()
user_means = np.squeeze(np.sum(np.array(training.todense()), axis=1))
user_means = np.divide(user_means,
(np.array(training.todense()) != 0).sum(1))
print('collaborative filtering for User-User:'******'float32')), metric='cosine')
print('distance calculation time:', time() - start_time_user)
predictions = predict(training, user_dist, testing, user_mappings,
movie_mappings, 10)
np.dot(matrix.T, np.reshape(U[:, i], [U.shape[0], 1])),
[matrix.shape[1]])
V_t = V.T
if save_factorized:
np.save('temp_data/U', U)
np.save('temp_data/V_t', V_t)
np.save('temp_data/sigma', sigma)
print('matrices saved!')
return U, V_t, sigma
if __name__ == '__main__':
# Read data
train = np.array(recsys_utils.read_train())
test = recsys_utils.read_test_table()
truth = test['rating'].as_matrix()
user_map = recsys_utils.read_user_map()
movie_map = recsys_utils.read_movie_map()
start_time = time()
# Subtracting mean of data from train set
user_means = np.squeeze(np.sum(train, axis=1))
user_means = np.divide(user_means, (train != 0).sum(1))
for i in range(train.shape[0]):
train[i, :][train[i, :] != 0] -= user_means[i]
# Decomposition and Reconstruction of SVD
U, V_t, sigma = SVD(train, retain_energy=90, save_factorized=True)