from sklearn.metrics import mean_squared_error | |
prediction = similarity_user.dot(train_matrix) / np.array([np.abs(similarity_user).sum(axis=1)]).T | |
prediction = prediction[test_matrix.nonzero()].flatten() | |
test_vector = test_matrix[test_matrix.nonzero()].flatten() | |
mse = mean_squared_error(prediction, test_vector) | |
print 'MSE = ' + str(mse) |
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