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)
