def test_error_calculation_01(self):
x = self.training_data[:, :-1]
y = self.training_data[:, -1].reshape(self.training_data.shape[0], 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, 1))
expected_value_1 = 2 * 6613.875 # Calculated externally as sum(y^2) / m
expected_value_2 = expected_value_1**0.5
output_1, output_2, _ = RadialBasisFunctions.error_calculation(
theta, x_vector, y)
self.assertEqual(output_1, expected_value_1)
self.assertEqual(output_2, expected_value_2)
def test_error_calculation_02(self):
x = self.training_data[:, :-1]
y = self.training_data[:, -1].reshape(self.training_data.shape[0], 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]
]) # coefficients in (x1 + 1.5)^2 + (x2 + 1.5) ^ 2
expected_value_1 = 2 * 90.625 # Calculated externally as sum(dy^2) / 2m
expected_value_2 = expected_value_1**0.5
output_1, output_2, _ = RadialBasisFunctions.error_calculation(
theta, x_vector, y)
self.assertEqual(output_1, expected_value_1)
self.assertEqual(output_2, expected_value_2)
def test_error_calculation_03(self):
x = self.training_data[:, :-1]
y = self.training_data[:, -1].reshape(self.training_data.shape[0], 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
expected_value_1 = 2 * 0 # Value should return zero for exact solution
expected_value_2 = expected_value_1**0.5
output_1, output_2, _ = RadialBasisFunctions.error_calculation(
theta, x_vector, y)
self.assertEqual(output_1, expected_value_1)
self.assertEqual(output_2, expected_value_2)
