def test_multivariate_gradient_descent(): gd_lr = TfLinearRegression(eta=0.005, epochs=250, random_seed=1, print_progress=0) gd_lr.fit(X2, y) assert_almost_equal(gd_lr.predict(X2), y, decimal=1) assert_almost_equal(gd_lr.b_, np.array([0.1]), decimal=2) assert_almost_equal(gd_lr.w_, np.array([-1.1, 1.2]), decimal=2)
def test_univariate_univariate_gradient_descent(): gd_lr = TfLinearRegression(eta=0.05, epochs=55, random_seed=1, print_progress=0) gd_lr.fit(X, y) assert_almost_equal(gd_lr.b_, np.array([0.11]), decimal=2) assert_almost_equal(gd_lr.w_, np.array([0.10]), decimal=2) assert_almost_equal(gd_lr.predict(X), y, decimal=1)
def test_continue_training(): gd_lr = TfLinearRegression(eta=0.005, epochs=120, random_seed=1, print_progress=0) gd_lr.fit(X2, y) gd_lr.fit(X2, y, init_params=False) assert_almost_equal(gd_lr.predict(X2), y, decimal=1) assert_almost_equal(gd_lr.b_, np.array([0.1]), decimal=2) assert_almost_equal(gd_lr.w_, np.array([-1.1, 1.2]), decimal=2)