print("RMSE: ", score) if score < max_error: max_error = score best_f = k scores2.append(score) matplotlib.pyplot.plot(space, scores2, 'k^:') matplotlib.pyplot.xlabel('Number of Features') matplotlib.pyplot.ylabel('Cross Validation Error') matplotlib.pyplot.title('Singular Value Decomposition') matplotlib.pyplot.savefig('../plots/singular_value_decomposition2.png') matplotlib.pyplot.gcf().clear() space = (numpy.linspace(0.01, 1, 10)) model.set_f(best_f) model.set_bias(True) for k in space: print("Epoch: %i", k) model.set_k_u(k) model.set_k_b(k) model.set_k_m(k) score = model.train(X, Y, X_val, Y_val) score = model.RMSE(X_test, Y_test) print("RMSE: ", score) scores3.append(score) matplotlib.pyplot.plot(space, scores3, 'rx-') matplotlib.pyplot.xlabel('Bias') matplotlib.pyplot.ylabel('RMSE') matplotlib.pyplot.title('Singular Value Decomposition')