def test_unknown_options_or_solver(): c = np.array([-3,-2]) A_ub = [[2,1], [1,1], [1,0]] b_ub = [10,8,4] _assert_warns(OptimizeWarning, linprog, c, A_ub=A_ub, b_ub=b_ub, options=dict(spam='42')) assert_raises(ValueError, linprog, c, A_ub=A_ub, b_ub=b_ub, method='ekki-ekki-ekki')
def test_logm_exactly_singular(self): A = np.array([[0, 0], [1j, 1j]]) B = np.asarray([[1, 1], [0, 0]]) for M in A, A.T, B, B.T: expected_warning = _matfuncs_inv_ssq.LogmExactlySingularWarning L, info = _assert_warns(expected_warning, logm, M, disp=False) E = expm(L) assert_allclose(E, M, atol=1e-14)
def test_unknown_options_or_solver(): c = np.array([-3, -2]) A_ub = [[2, 1], [1, 1], [1, 0]] b_ub = [10, 8, 4] _assert_warns(OptimizeWarning, linprog, c, A_ub=A_ub, b_ub=b_ub, options=dict(spam='42')) assert_raises(ValueError, linprog, c, A_ub=A_ub, b_ub=b_ub, method='ekki-ekki-ekki')
def test_logm_nearly_singular(self): M = np.array([[1e-100]]) expected_warning = _matfuncs_inv_ssq.LogmNearlySingularWarning L, info = _assert_warns(expected_warning, logm, M, disp=False) E = expm(L) assert_allclose(E, M, atol=1e-14)
def test_indeterminate_covariance(self): # Test that a warning is returned when pcov is indeterminate xdata = np.array([1, 2, 3, 4, 5, 6]) ydata = np.array([1, 2, 3, 4, 5.5, 6]) _assert_warns(OptimizeWarning, curve_fit, lambda x, a, b: a * x, xdata, ydata)
def test_indeterminate_covariance(self): # Test that a warning is returned when pcov is indeterminate xdata = np.array([1, 2, 3, 4, 5, 6]) ydata = np.array([1, 2, 3, 4, 5.5, 6]) _assert_warns(OptimizeWarning, curve_fit, lambda x, a, b: a*x, xdata, ydata)