def test_normal_equation():
t1 = np.array([[-0.08], [1.02]])
b1 = np.array([0.00])
ada = Adaline(epochs=30, eta=0.01, minibatches=None, random_seed=None)
ada.fit(X_std, y1)
np.testing.assert_almost_equal(ada.w_, t1, decimal=2)
np.testing.assert_almost_equal(ada.b_, b1, decimal=2)
assert (y1 == ada.predict(X_std)).all(), ada.predict(X_std)
def test_normal_equation():
t1 = np.array([[-0.08], [1.02]])
b1 = np.array([0.00])
ada = Adaline(epochs=30,
eta=0.01,
minibatches=None,
random_seed=None)
ada.fit(X_std, y1)
np.testing.assert_almost_equal(ada.w_, t1, decimal=2)
np.testing.assert_almost_equal(ada.b_, b1, decimal=2)
assert (y1 == ada.predict(X_std)).all(), ada.predict(X_std)
def test_refit_weights():
t1 = np.array([-5.21e-16, -7.86e-02, 1.02e+00])
ada = Adaline(epochs=15, eta=0.01, solver='gd', random_seed=1)
ada.fit(X_std, y1, init_weights=True)
ada.fit(X_std, y1, init_weights=False)
np.testing.assert_almost_equal(ada.w_, t1, 2)
assert((y1 == ada.predict(X_std)).all())
def test_0_1_class():
t1 = np.array([0.51, -0.04, 0.51])
ada = Adaline(epochs=30, eta=0.01, learning='sgd', random_seed=1)
ada.fit(X_std, y0)
np.testing.assert_almost_equal(ada.w_, t1, 2)
assert((y0 == ada.predict(X_std)).all())
def test_stochastic_gradient_descent():
t1 = np.array([0.03, -0.09, 1.02])
ada = Adaline(epochs=30, eta=0.01, learning='sgd', random_seed=1)
ada.fit(X_std, y1)
np.testing.assert_almost_equal(ada.w_, t1, 2)
assert ((y1 == ada.predict(X_std)).all())
def test_gradient_descent():
t1 = np.array([-5.21e-16, -7.86e-02, 1.02e+00])
ada = Adaline(epochs=30, eta=0.01, learning='gd', random_seed=1)
ada.fit(X_std, y1)
np.testing.assert_almost_equal(ada.w_, t1, 2)
assert((y1 == ada.predict(X_std)).all())
def test_refit_weights():
t1 = np.array([-5.21e-16, -7.86e-02, 1.02e+00])
ada = Adaline(epochs=15, eta=0.01, solver='gd', random_seed=1)
ada.fit(X_std, y1, init_weights=True)
ada.fit(X_std, y1, init_weights=False)
np.testing.assert_almost_equal(ada.w_, t1, 2)
assert ((y1 == ada.predict(X_std)).all())
def test_0_1_class():
t1 = np.array([0.51, -0.04, 0.51])
ada = Adaline(epochs=30, eta=0.01, learning='sgd', random_seed=1)
ada.fit(X_std, y0)
np.testing.assert_almost_equal(ada.w_, t1, 2)
assert ((y0 == ada.predict(X_std)).all())
def test_refit_weights():
t1 = np.array([[-0.08], [1.02]])
ada = Adaline(epochs=15, eta=0.01, minibatches=1, random_seed=1)
ada.fit(X_std, y1, init_params=True)
ada.fit(X_std, y1, init_params=False)
np.testing.assert_almost_equal(ada.w_, t1, 2)
assert ((y1 == ada.predict(X_std)).all())
def test_stochastic_gradient_descent():
t1 = np.array([0.03, -0.09, 1.02])
ada = Adaline(epochs=30, eta=0.01, learning='sgd', random_seed=1)
ada.fit(X_std, y1)
np.testing.assert_almost_equal(ada.w_, t1, 2)
assert((y1 == ada.predict(X_std)).all())
def test_gradient_descent():
t1 = np.array([-5.21e-16, -7.86e-02, 1.02e+00])
ada = Adaline(epochs=30, eta=0.01, learning='gd', random_seed=1)
ada.fit(X_std, y1)
np.testing.assert_almost_equal(ada.w_, t1, 2)
assert ((y1 == ada.predict(X_std)).all())
def test_gradient_descent():
t1 = np.array([[-0.08], [1.02]])
b1 = np.array([0.00])
ada = Adaline(epochs=30, eta=0.01, minibatches=1, random_seed=1)
ada.fit(X_std, y1)
np.testing.assert_almost_equal(ada.w_, t1, decimal=2)
np.testing.assert_almost_equal(ada.b_, b1, decimal=2)
assert ((y1 == ada.predict(X_std)).all())
def test_normal_equation():
t1 = np.array([-5.21e-16, -7.86e-02, 1.02e+00])
ada = Adaline(epochs=30,
eta=0.01,
minibatches=None,
random_seed=1)
ada.fit(X_std, y1)
np.testing.assert_almost_equal(ada.w_, t1, 2)
assert((y1 == ada.predict(X_std)).all())
def test_stochastic_gradient_descent():
t1 = np.array([[-0.08], [1.02]])
ada = Adaline(epochs=30,
eta=0.01,
minibatches=len(y),
random_seed=1)
ada.fit(X_std, y1)
np.testing.assert_almost_equal(ada.w_, t1, 2)
assert((y1 == ada.predict(X_std)).all())
def test_standardized_iris_data_with_zero_weights():
t1 = np.array([-5.21e-16, -7.86e-02, 1.02e+00])
ada = Adaline(epochs=30,
eta=0.01,
solver='gd',
random_seed=1,
zero_init_weight=True)
ada.fit(X_std, y1)
np.testing.assert_almost_equal(ada.w_, t1, 2)
assert ((y1 == ada.predict(X_std)).all())
def test_standardized_iris_data_with_shuffle():
t1 = np.array([-5.21e-16, -7.86e-02, 1.02e+00])
ada = Adaline(epochs=30,
eta=0.01,
solver='gd',
random_seed=1,
shuffle=True)
ada.fit(X_std, y1)
np.testing.assert_almost_equal(ada.w_, t1, 2)
assert((y1 == ada.predict(X_std)).all())
def test_standardized_iris_data_with_zero_weights():
t1 = np.array([-5.21e-16, -7.86e-02, 1.02e+00])
ada = Adaline(epochs=30,
eta=0.01,
minibatches=1,
random_seed=1,
zero_init_weight=True)
ada.fit(X_std, y1)
np.testing.assert_almost_equal(ada.w_, t1, 2)
assert((y1 == ada.predict(X_std)).all())
def test_refit_weights():
t1 = np.array([[-0.08], [1.02]])
ada = Adaline(epochs=15,
eta=0.01,
minibatches=1,
random_seed=1)
ada.fit(X_std, y1, init_params=True)
ada.fit(X_std, y1, init_params=False)
np.testing.assert_almost_equal(ada.w_, t1, 2)
assert((y1 == ada.predict(X_std)).all())
def test_gradient_descent():
t1 = np.array([[-0.08], [1.02]])
b1 = np.array([0.00])
ada = Adaline(epochs=30,
eta=0.01,
minibatches=1,
random_seed=1)
ada.fit(X_std, y1)
np.testing.assert_almost_equal(ada.w_, t1, decimal=2)
np.testing.assert_almost_equal(ada.b_, b1, decimal=2)
assert((y1 == ada.predict(X_std)).all())
def test_normal_equation():
t1 = np.array([-5.21e-16, -7.86e-02, 1.02e+00])
ada = Adaline(epochs=30, eta=0.01, solver='normal equation', random_seed=1)
ada.fit(X_std, y1)
np.testing.assert_almost_equal(ada.w_, t1, 2)
assert ((y1 == ada.predict(X_std)).all())
def test_stochastic_gradient_descent():
t1 = np.array([[-0.08], [1.02]])
ada = Adaline(epochs=30, eta=0.01, minibatches=len(y), random_seed=1)
ada.fit(X_std, y1)
np.testing.assert_almost_equal(ada.w_, t1, 2)
assert ((y1 == ada.predict(X_std)).all())