def test_gradient_function_01(self):
x = self.training_data[:, :-1]
y = self.training_data[:, -1]
x_data_nr = x.shape[0]
x_data_nc = 6
x_vector = np.zeros((x_data_nr, x_data_nc))
x_vector[:, 0] = 1
x_vector[:, 1] = x[:, 0]
x_vector[:, 2] = x[:, 1]
x_vector[:, 3] = x[:, 0]**2
x_vector[:, 4] = x[:, 1]**2
x_vector[:, 5] = x[:, 0] * x[:, 1]
theta = np.zeros((x_data_nc, ))
expected_value = np.array([[-97], [-635], [-635], [-5246.875],
[-5246.875], [-3925]])
expected_value = expected_value.reshape(expected_value.shape[0], )
output_1 = RadialBasisFunctions.gradient_function(theta, x_vector, y)
np.testing.assert_equal(output_1, expected_value)
def test_gradient_function_03(self):
x = self.training_data[:, :-1]
y = self.training_data[:, -1]
x_data_nr = x.shape[0]
x_data_nc = 6
x_vector = np.zeros((x_data_nr, x_data_nc))
x_vector[:, 0] = 1
x_vector[:, 1] = x[:, 0]
x_vector[:, 2] = x[:, 1]
x_vector[:, 3] = x[:, 0]**2
x_vector[:, 4] = x[:, 1]**2
x_vector[:, 5] = x[:, 0] * x[:, 1]
theta = np.array(
[[2], [2], [2], [1], [1],
[0]]) # Actual coefficients in (x1 + 1)^2 + (x2 + 1) ^ 2
theta = theta.reshape(theta.shape[0], )
expected_value = np.array([[0], [0], [0], [0], [0], [0]
]) # Calculated externally: see Excel sheet
expected_value = expected_value.reshape(expected_value.shape[0], )
output_1 = RadialBasisFunctions.gradient_function(theta, x_vector, y)
np.testing.assert_equal(output_1, expected_value)