def test_pyomo_optimization_02(self):
x = self.training_data[:, :-1]
y = self.training_data[:, -1]
x_vector = np.zeros((x.shape[0], 6))
x_vector[:, 0] = x[:, 0]**2
x_vector[:, 1] = x[:, 1]**2
x_vector[:, 2] = x[:, 0]
x_vector[:, 3] = x[:, 1]
x_vector[:, 4] = x[:, 1] * x[:, 0]
x_vector[:, 5] = 1
expected_value = np.array([[1.], [1.], [2.], [2.], [0.], [2.]])
output_1 = RadialBasisFunctions.pyomo_optimization(x_vector, y)
np.testing.assert_array_equal(expected_value, np.round(output_1, 4))
def test_pyomo_optimization_01():
# Create x vector for ax2 + bx + c: x data supplied in x_vector
input_array = np.array([[0, 1], [1, 4], [2, 9], [3, 16], [4, 25],
[5, 36], [6, 49], [7, 64], [8, 81], [9, 100]])
x = input_array[:, 0]
y = input_array[:, 1]
x_vector = np.zeros((x.shape[0], 3))
x_vector[:, 0] = x[:, ]**2
x_vector[:, 1] = x[:, ]
x_vector[:, 2] = 1
expected_value = np.array([[1.], [2.], [1.]])
output_1 = RadialBasisFunctions.pyomo_optimization(x_vector, y)
np.testing.assert_array_equal(expected_value, np.round(output_1, 4))
