PythonLinearNonlinearControl/tests/models/test_model.py at master · privvyledge/PythonLinearNonlinearControl · GitHub

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import pytest

import numpy as np

from PythonLinearNonlinearControl.models.model import LinearModel

class TestLinearModel():

"""

def test_predict(self):

A = np.array([[1., 0.1],

[0.1, 1.5]])

B = np.array([[0.2], [0.5]])

curr_x = np.ones(2) * 0.5

u = np.ones((1, 1))

linear_model = LinearModel(A, B)

pred_xs = linear_model.predict_traj(curr_x, u)

assert pred_xs == pytest.approx(np.array([[0.5, 0.5], [0.75, 1.3]]))

def test_alltogether(self):

A = np.array([[1., 0.1],

[0.1, 1.5]])

B = np.array([[0.2], [0.5]])

curr_x = np.ones(2) * 0.5

u = np.ones((1, 1))

linear_model = LinearModel(A, B)

pred_xs = linear_model.predict_traj(curr_x, u)

u = np.tile(u, (1, 1, 1))

pred_xs_alltogether = linear_model.predict_traj(curr_x, u)[0]

assert pred_xs_alltogether == pytest.approx(pred_xs)

def test_alltogether_val(self):

A = np.array([[1., 0.1],

[0.1, 1.5]])

B = np.array([[0.2], [0.5]])

curr_x = np.ones(2) * 0.5

u = np.stack((np.ones((1, 1)), np.ones((1, 1))*0.5), axis=0)

linear_model = LinearModel(A, B)

pred_xs_alltogether = linear_model.predict_traj(curr_x, u)

expected_val = np.array([[[0.5, 0.5], [0.75, 1.3]],

[[0.5, 0.5], [0.65, 1.05]]])

assert pred_xs_alltogether == pytest.approx(expected_val)

PythonLinearNonlinearControl/tests/models/test_model.py at master · privvyledge/PythonLinearNonlinearControl · GitHub

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