def test_update_P_A_indP_indA(self):
import mat_emosqp
# Update matrices P and A
Pnew_triu = sparse.triu(self.P_new).tocsc()
Px = Pnew_triu.data
Px_idx = np.arange(Pnew_triu.nnz)
Ax = self.A_new.data
Ax_idx = np.arange(self.A_new.nnz)
mat_emosqp.update_P_A(Px, Px_idx, len(Px), Ax, Ax_idx, len(Ax))
# Solve problem
x, y, _, _, _ = mat_emosqp.solve()
# Assert close
nptest.assert_array_almost_equal(x, np.array([4.25, 3.25]), decimal=5)
nptest.assert_array_almost_equal(y,
np.array([0., 0., 3.625, 0., 0.]),
decimal=5)
# Update matrices P and A to the original values
P_triu = sparse.triu(self.P).tocsc()
Px = P_triu.data
Ax = self.A.data
mat_emosqp.update_P_A(Px, None, 0, Ax, None, 0)
def test_solve(self):
# Generate the code
self.model.codegen('code2', python_ext_name='mat_emosqp',
force_rewrite=True, parameters='matrices')
sh.rmtree('code2')
import mat_emosqp
# Solve problem
x, y, _, _, _ = mat_emosqp.solve()
# Assert close
nptest.assert_array_almost_equal(x, np.array([0., 5.]), decimal=5)
nptest.assert_array_almost_equal(
y, np.array([1.5, 0., 1.5, 0., 0.]), decimal=5)
def test_update_P_allind(self):
import mat_emosqp
# Update matrix P
Px = self.P_new.data
mat_emosqp.update_P(Px, None, 0)
x, y, _, _, _ = mat_emosqp.solve()
# Assert close
nptest.assert_array_almost_equal(x, np.array([0., 5.]), decimal=5)
nptest.assert_array_almost_equal(
y, np.array([0., 0., 3., 0., 0.]), decimal=5)
# Update matrix P to the original value
Px_idx = np.arange(self.P.nnz)
mat_emosqp.update_P(Px, Px_idx, len(Px))
def test_update_P_A_allind(self):
import mat_emosqp
# Update matrices P and A
Px = self.P_new.data
Ax = self.A_new.data
mat_emosqp.update_P_A(Px, None, 0, Ax, None, 0)
x, y, _, _, _ = mat_emosqp.solve()
# Assert close
nptest.assert_array_almost_equal(x, np.array([4.25, 3.25]), decimal=5)
nptest.assert_array_almost_equal(
y, np.array([0., 0., 3.625, 0., 0.]), decimal=5)
# Update matrices P and A to the original values
Px = self.P.data
Ax = self.A.data
mat_emosqp.update_P_A(Px, None, 0, Ax, None, 0)
def test_update_A_allind(self):
import mat_emosqp
# Update matrix A
Ax = self.A_new.data
mat_emosqp.update_A(Ax, None, 0)
x, y, _, _, _ = mat_emosqp.solve()
# Assert close
nptest.assert_array_almost_equal(x,
np.array([0.15765766, 7.34234234]), decimal=5)
nptest.assert_array_almost_equal(
y, np.array([0., 0., 2.36711712, 0., 0.]), decimal=5)
# Update matrix A to the original value
Ax = self.A.data
Ax_idx = np.arange(self.A.nnz)
mat_emosqp.update_A(Ax, Ax_idx, len(Ax))
def test_update_P_A_indA(self):
import mat_emosqp
# Update matrices P and A
Px = self.P_new.data
Ax = self.A_new.data
Ax_idx = np.arange(self.A_new.nnz)
mat_emosqp.update_P_A(Px, None, 0, Ax, Ax_idx, len(Ax))
x, y, _, _, _ = mat_emosqp.solve()
# Assert close
nptest.assert_array_almost_equal(x, np.array([4.25, 3.25]), decimal=5)
nptest.assert_array_almost_equal(
y, np.array([0., 0., 3.625, 0., 0.]), decimal=5)
# Update matrix P to the original value
Px = self.P.data
Px_idx = np.arange(self.P.nnz)
Ax = self.A.data
Ax_idx = np.arange(self.A.nnz)
mat_emosqp.update_P_A(Px, Px_idx, len(Px), Ax, Ax_idx, len(Ax))
def test_update_P(self):
import mat_emosqp
# Update matrix P
Pnew_triu = sparse.triu(self.P_new).tocsc()
Px = Pnew_triu.data
Px_idx = np.arange(Pnew_triu.nnz)
mat_emosqp.update_P(Px, Px_idx, len(Px))
# Solve problem
x, y, _, _, _ = mat_emosqp.solve()
# Assert close
nptest.assert_array_almost_equal(x, np.array([0., 5.]), decimal=5)
nptest.assert_array_almost_equal(y,
np.array([0., 0., 3., 0., 0.]),
decimal=5)
# Update matrix P to the original value
P_triu = sparse.triu(self.P).tocsc()
Px = P_triu.data
Px_idx = np.arange(P_triu.nnz)
mat_emosqp.update_P(Px, Px_idx, len(Px))
