def test_cost_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]])
expected_value = 0
output_1 = RadialBasisFunctions.cost_function(theta, x_vector, y)
self.assertEqual(output_1, expected_value)
def test_cost_function_02(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([[4.5], [3], [3], [1], [1], [0]])
expected_value = 90.625 # Calculated externally as sum(dy^2) / 2m
output_1 = RadialBasisFunctions.cost_function(theta, x_vector, y)
self.assertEqual(output_1, expected_value)
