Python Adaline.predict Examples

Example #1

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)

Example #2

File: test_adaline.py Project: rasbt/mlxtend

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)

Example #3

File: test_adaline.py Project: beingzy/mlxtend

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())

Example #4

File: test_adaline.py Project: Afey/mlxtend

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())

Example #5

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())

Example #6

File: test_adaline.py Project: Afey/mlxtend

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())

Example #7

File: test_adaline.py Project: pengyuange/mlxtend

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())

Example #8

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())

Example #9

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())

Example #10

File: test_adaline.py Project: Afey/mlxtend

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())

Example #11

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())

Example #12

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())

Example #13

File: test_adaline.py Project: datasci-co/mlxtend

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())

Example #14

File: test_adaline.py Project: rasbt/mlxtend

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())

Example #15

File: test_adaline.py Project: pengyuange/mlxtend

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())

Example #16

File: test_adaline.py Project: beingzy/mlxtend

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())

Example #17

File: test_adaline.py Project: datasci-co/mlxtend

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())

Example #18

File: test_adaline.py Project: rasbt/mlxtend

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())

Example #19

File: test_adaline.py Project: rasbt/mlxtend

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())

Example #20

File: test_adaline.py Project: pengyuange/mlxtend

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())

Example #21

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())