Python numpy.triu() Examples
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Example #1
Source File: test_blas.py From Computable with MIT License | 6 votes |
def test_syrk(self): for f in _get_func('syrk'): c = f(a=self.a, alpha=1.) assert_array_almost_equal(np.triu(c), np.triu(self.t)) c = f(a=self.a, alpha=1., lower=1) assert_array_almost_equal(np.tril(c), np.tril(self.t)) c0 = np.ones(self.t.shape) c = f(a=self.a, alpha=1., beta=1., c=c0) assert_array_almost_equal(np.triu(c), np.triu(self.t+c0)) c = f(a=self.a, alpha=1., trans=1) assert_array_almost_equal(np.triu(c), np.triu(self.tt)) #prints '0-th dimension must be fixed to 3 but got 5', FIXME: suppress? # FIXME: how to catch the _fblas.error?
Example #2
Source File: statesp_array_test.py From python-control with BSD 3-Clause "New" or "Revised" License | 6 votes |
def test_dc_gain_integrator(self): """DC gain when eigenvalue at DC returns appropriately sized array of nan.""" # the SISO case is also tested in test_dc_gain_{cont,discr} import itertools # iterate over input and output sizes, and continuous (dt=None) and discrete (dt=True) time for inputs, outputs, dt in itertools.product(range(1, 6), range(1, 6), [None, True]): states = max(inputs, outputs) # a matrix that is singular at DC, and has no "useless" states as in # _remove_useless_states a = np.triu(np.tile(2, (states, states))) # eigenvalues all +2, except for ... a[0, 0] = 0 if dt is None else 1 b = np.eye(max(inputs, states))[:states, :inputs] c = np.eye(max(outputs, states))[:outputs, :states] d = np.zeros((outputs, inputs)) sys = StateSpace(a, b, c, d, dt) dc = np.squeeze(np.tile(np.nan, (outputs, inputs))) np.testing.assert_array_equal(dc, sys.dcgain())
Example #3
Source File: statesp_test.py From python-control with BSD 3-Clause "New" or "Revised" License | 6 votes |
def test_dc_gain_integrator(self): """DC gain when eigenvalue at DC returns appropriately sized array of nan.""" # the SISO case is also tested in test_dc_gain_{cont,discr} import itertools # iterate over input and output sizes, and continuous (dt=None) and discrete (dt=True) time for inputs, outputs, dt in itertools.product(range(1, 6), range(1, 6), [None, True]): states = max(inputs, outputs) # a matrix that is singular at DC, and has no "useless" states as in # _remove_useless_states a = np.triu(np.tile(2, (states, states))) # eigenvalues all +2, except for ... a[0, 0] = 0 if dt is None else 1 b = np.eye(max(inputs, states))[:states, :inputs] c = np.eye(max(outputs, states))[:outputs, :states] d = np.zeros((outputs, inputs)) sys = StateSpace(a, b, c, d, dt) dc = np.squeeze(np.tile(np.nan, (outputs, inputs))) np.testing.assert_array_equal(dc, sys.dcgain())
Example #4
Source File: blow.py From blow with Apache License 2.0 | 6 votes |
def __init__(self,in_channel): super(InvConv,self).__init__() weight=np.random.randn(in_channel,in_channel) q,_=linalg.qr(weight) w_p,w_l,w_u=linalg.lu(q.astype(np.float32)) w_s=np.diag(w_u) w_u=np.triu(w_u,1) u_mask=np.triu(np.ones_like(w_u),1) l_mask=u_mask.T self.register_buffer('w_p',torch.from_numpy(w_p)) self.register_buffer('u_mask',torch.from_numpy(u_mask)) self.register_buffer('l_mask',torch.from_numpy(l_mask)) self.register_buffer('l_eye',torch.eye(l_mask.shape[0])) self.register_buffer('s_sign',torch.sign(torch.from_numpy(w_s))) self.w_l=torch.nn.Parameter(torch.from_numpy(w_l)) self.w_s=torch.nn.Parameter(torch.log(1e-7+torch.abs(torch.from_numpy(w_s)))) self.w_u=torch.nn.Parameter(torch.from_numpy(w_u)) self.weight=None self.invweight=None return
Example #5
Source File: test_twodim_base.py From recruit with Apache License 2.0 | 6 votes |
def test_tril_triu_ndim3(): for dtype in np.typecodes['AllFloat'] + np.typecodes['AllInteger']: a = np.array([ [[1, 1], [1, 1]], [[1, 1], [1, 0]], [[1, 1], [0, 0]], ], dtype=dtype) a_tril_desired = np.array([ [[1, 0], [1, 1]], [[1, 0], [1, 0]], [[1, 0], [0, 0]], ], dtype=dtype) a_triu_desired = np.array([ [[1, 1], [0, 1]], [[1, 1], [0, 0]], [[1, 1], [0, 0]], ], dtype=dtype) a_triu_observed = np.triu(a) a_tril_observed = np.tril(a) assert_array_equal(a_triu_observed, a_triu_desired) assert_array_equal(a_tril_observed, a_tril_desired) assert_equal(a_triu_observed.dtype, a.dtype) assert_equal(a_tril_observed.dtype, a.dtype)
Example #6
Source File: test_twodim_base.py From recruit with Apache License 2.0 | 6 votes |
def test_tril_triu_dtype(): # Issue 4916 # tril and triu should return the same dtype as input for c in np.typecodes['All']: if c == 'V': continue arr = np.zeros((3, 3), dtype=c) assert_equal(np.triu(arr).dtype, arr.dtype) assert_equal(np.tril(arr).dtype, arr.dtype) # check special cases arr = np.array([['2001-01-01T12:00', '2002-02-03T13:56'], ['2004-01-01T12:00', '2003-01-03T13:45']], dtype='datetime64') assert_equal(np.triu(arr).dtype, arr.dtype) assert_equal(np.tril(arr).dtype, arr.dtype) arr = np.zeros((3,3), dtype='f4,f4') assert_equal(np.triu(arr).dtype, arr.dtype) assert_equal(np.tril(arr).dtype, arr.dtype)
Example #7
Source File: test_tools.py From tenpy with GNU General Public License v3.0 | 6 votes |
def test_qr_li(): cutoff = 1.e-10 for shape in [(5, 4), (4, 5)]: print('shape =', shape) A = np.arange(20).reshape(shape) # linearly dependent: only two rows/columns independent A[3, :] = np.random.random() * (cutoff / 100) # nearly linear dependent q, r = tools.math.qr_li(A) assert np.linalg.norm(r - np.triu(r)) == 0. qdq = q.T.conj().dot(q) assert np.linalg.norm(qdq - np.eye(len(qdq))) < 1.e-13 assert np.linalg.norm(q.dot(r) - A) < cutoff * 20 r, q = tools.math.rq_li(A) assert np.linalg.norm(r - np.triu(r, r.shape[1] - r.shape[0])) == 0. qqd = q.dot(q.T.conj()) assert np.linalg.norm(qqd - np.eye(len(qqd))) < 1.e-13 assert np.linalg.norm(r.dot(q) - A) < cutoff * 20
Example #8
Source File: test_twodim_base.py From lambda-packs with MIT License | 6 votes |
def test_tril_triu_ndim3(): for dtype in np.typecodes['AllFloat'] + np.typecodes['AllInteger']: a = np.array([ [[1, 1], [1, 1]], [[1, 1], [1, 0]], [[1, 1], [0, 0]], ], dtype=dtype) a_tril_desired = np.array([ [[1, 0], [1, 1]], [[1, 0], [1, 0]], [[1, 0], [0, 0]], ], dtype=dtype) a_triu_desired = np.array([ [[1, 1], [0, 1]], [[1, 1], [0, 0]], [[1, 1], [0, 0]], ], dtype=dtype) a_triu_observed = np.triu(a) a_tril_observed = np.tril(a) yield assert_array_equal, a_triu_observed, a_triu_desired yield assert_array_equal, a_tril_observed, a_tril_desired yield assert_equal, a_triu_observed.dtype, a.dtype yield assert_equal, a_tril_observed.dtype, a.dtype
Example #9
Source File: test_twodim_base.py From lambda-packs with MIT License | 6 votes |
def test_tril_triu_dtype(): # Issue 4916 # tril and triu should return the same dtype as input for c in np.typecodes['All']: if c == 'V': continue arr = np.zeros((3, 3), dtype=c) assert_equal(np.triu(arr).dtype, arr.dtype) assert_equal(np.tril(arr).dtype, arr.dtype) # check special cases arr = np.array([['2001-01-01T12:00', '2002-02-03T13:56'], ['2004-01-01T12:00', '2003-01-03T13:45']], dtype='datetime64') assert_equal(np.triu(arr).dtype, arr.dtype) assert_equal(np.tril(arr).dtype, arr.dtype) arr = np.zeros((3,3), dtype='f4,f4') assert_equal(np.triu(arr).dtype, arr.dtype) assert_equal(np.tril(arr).dtype, arr.dtype)
Example #10
Source File: transformer.py From ITDD with MIT License | 6 votes |
def _get_attn_subsequent_mask(self, size): """ Get an attention mask to avoid using the subsequent info. Args: size: int Returns: (`LongTensor`): * subsequent_mask `[1 x size x size]` """ attn_shape = (1, size, size) subsequent_mask = np.triu(np.ones(attn_shape), k=1).astype('uint8') subsequent_mask = torch.from_numpy(subsequent_mask) return subsequent_mask
Example #11
Source File: ktransformer.py From ITDD with MIT License | 6 votes |
def _get_attn_subsequent_mask(self, size): """ Get an attention mask to avoid using the subsequent info. Args: size: int Returns: (`LongTensor`): * subsequent_mask `[1 x size x size]` """ attn_shape = (1, size, size) subsequent_mask = np.triu(np.ones(attn_shape), k=1).astype('uint8') subsequent_mask = torch.from_numpy(subsequent_mask) return subsequent_mask
Example #12
Source File: mtransformer.py From ITDD with MIT License | 6 votes |
def _get_attn_subsequent_mask(self, size): """ Get an attention mask to avoid using the subsequent info. Args: size: int Returns: (`LongTensor`): * subsequent_mask `[1 x size x size]` """ attn_shape = (1, size, size) subsequent_mask = np.triu(np.ones(attn_shape), k=1).astype('uint8') subsequent_mask = torch.from_numpy(subsequent_mask) return subsequent_mask
Example #13
Source File: test_twodim_base.py From vnpy_crypto with MIT License | 6 votes |
def test_tril_triu_ndim3(): for dtype in np.typecodes['AllFloat'] + np.typecodes['AllInteger']: a = np.array([ [[1, 1], [1, 1]], [[1, 1], [1, 0]], [[1, 1], [0, 0]], ], dtype=dtype) a_tril_desired = np.array([ [[1, 0], [1, 1]], [[1, 0], [1, 0]], [[1, 0], [0, 0]], ], dtype=dtype) a_triu_desired = np.array([ [[1, 1], [0, 1]], [[1, 1], [0, 0]], [[1, 1], [0, 0]], ], dtype=dtype) a_triu_observed = np.triu(a) a_tril_observed = np.tril(a) yield assert_array_equal, a_triu_observed, a_triu_desired yield assert_array_equal, a_tril_observed, a_tril_desired yield assert_equal, a_triu_observed.dtype, a.dtype yield assert_equal, a_tril_observed.dtype, a.dtype
Example #14
Source File: test_twodim_base.py From vnpy_crypto with MIT License | 6 votes |
def test_tril_triu_dtype(): # Issue 4916 # tril and triu should return the same dtype as input for c in np.typecodes['All']: if c == 'V': continue arr = np.zeros((3, 3), dtype=c) assert_equal(np.triu(arr).dtype, arr.dtype) assert_equal(np.tril(arr).dtype, arr.dtype) # check special cases arr = np.array([['2001-01-01T12:00', '2002-02-03T13:56'], ['2004-01-01T12:00', '2003-01-03T13:45']], dtype='datetime64') assert_equal(np.triu(arr).dtype, arr.dtype) assert_equal(np.tril(arr).dtype, arr.dtype) arr = np.zeros((3,3), dtype='f4,f4') assert_equal(np.triu(arr).dtype, arr.dtype) assert_equal(np.tril(arr).dtype, arr.dtype)
Example #15
Source File: _bksf_test.py From OpenFermion with Apache License 2.0 | 6 votes |
def test_bravyi_kitaev_fast_edgeoperator_Bi(self): # checking the edge operators edge_matrix = numpy.triu(numpy.ones((4, 4))) edge_matrix_indices = numpy.array( numpy.nonzero(numpy.triu(edge_matrix) - numpy.diag(numpy.diag(edge_matrix)))) correct_operators_b0 = ((0, 'Z'), (1, 'Z'), (2, 'Z')) correct_operators_b1 = ((0, 'Z'), (3, 'Z'), (4, 'Z')) correct_operators_b2 = ((1, 'Z'), (3, 'Z'), (5, 'Z')) correct_operators_b3 = ((2, 'Z'), (4, 'Z'), (5, 'Z')) qterm_b0 = QubitOperator(correct_operators_b0, 1) qterm_b1 = QubitOperator(correct_operators_b1, 1) qterm_b2 = QubitOperator(correct_operators_b2, 1) qterm_b3 = QubitOperator(correct_operators_b3, 1) self.assertTrue(qterm_b0 == _bksf.edge_operator_b(edge_matrix_indices, 0)) self.assertTrue(qterm_b1 == _bksf.edge_operator_b(edge_matrix_indices, 1)) self.assertTrue(qterm_b2 == _bksf.edge_operator_b(edge_matrix_indices, 2)) self.assertTrue(qterm_b3 == _bksf.edge_operator_b(edge_matrix_indices, 3))
Example #16
Source File: models.py From fragile with MIT License | 6 votes |
def _cov_matrix_diagonalization(self): # Decomposition of cov_matrix into coords_matrix*diag(scaling_diag.^2)*coords_matrix' # (diagonalization) if ( self._count_eval - self.n_eigen_eval > self.pop_size / (self.lr_covrank1_const + self.lr_mu_const) / self.n_dims / 10 ): self.n_eigen_eval = self._count_eval self.cov_matrix = numpy.triu(self.cov_matrix) + numpy.triu(self.cov_matrix, 1).T eigvals, eigvects = numpy.linalg.eig(self.cov_matrix) self.scaling_diag = numpy.diag(eigvals) # [::-1]) self.coords_matrix = eigvects # [:, ::-1] assert numpy.abs(numpy.imag(self.coords_matrix).sum()) == 0, self.coords_matrix assert numpy.abs(numpy.imag(self.scaling_diag).sum()) == 0, self.scaling_diag self.scaling_diag = numpy.sqrt(numpy.diag(self.scaling_diag)).reshape(-1, 1) self.invsqrtC = numpy.matmul( numpy.matmul(self.coords_matrix, numpy.diag(self.scaling_diag.flatten() ** -1)), self.coords_matrix.T, )
Example #17
Source File: test_twodim_base.py From auto-alt-text-lambda-api with MIT License | 6 votes |
def test_tril_triu_ndim3(): for dtype in np.typecodes['AllFloat'] + np.typecodes['AllInteger']: a = np.array([ [[1, 1], [1, 1]], [[1, 1], [1, 0]], [[1, 1], [0, 0]], ], dtype=dtype) a_tril_desired = np.array([ [[1, 0], [1, 1]], [[1, 0], [1, 0]], [[1, 0], [0, 0]], ], dtype=dtype) a_triu_desired = np.array([ [[1, 1], [0, 1]], [[1, 1], [0, 0]], [[1, 1], [0, 0]], ], dtype=dtype) a_triu_observed = np.triu(a) a_tril_observed = np.tril(a) yield assert_array_equal, a_triu_observed, a_triu_desired yield assert_array_equal, a_tril_observed, a_tril_desired yield assert_equal, a_triu_observed.dtype, a.dtype yield assert_equal, a_tril_observed.dtype, a.dtype
Example #18
Source File: test_twodim_base.py From auto-alt-text-lambda-api with MIT License | 6 votes |
def test_tril_triu_dtype(): # Issue 4916 # tril and triu should return the same dtype as input for c in np.typecodes['All']: if c == 'V': continue arr = np.zeros((3, 3), dtype=c) assert_equal(np.triu(arr).dtype, arr.dtype) assert_equal(np.tril(arr).dtype, arr.dtype) # check special cases arr = np.array([['2001-01-01T12:00', '2002-02-03T13:56'], ['2004-01-01T12:00', '2003-01-03T13:45']], dtype='datetime64') assert_equal(np.triu(arr).dtype, arr.dtype) assert_equal(np.tril(arr).dtype, arr.dtype) arr = np.zeros((3,3), dtype='f4,f4') assert_equal(np.triu(arr).dtype, arr.dtype) assert_equal(np.tril(arr).dtype, arr.dtype)
Example #19
Source File: slinalg.py From D-VAE with MIT License | 5 votes |
def perform(self, node, inputs, outputs): """ Implements the "reverse-mode" gradient [1]_ for the Cholesky factorization of a positive-definite matrix. References ---------- .. [1] S. P. Smith. "Differentiation of the Cholesky Algorithm". Journal of Computational and Graphical Statistics, Vol. 4, No. 2 (Jun.,1995), pp. 134-147 http://www.jstor.org/stable/1390762 """ x = inputs[0] L = inputs[1] dz = inputs[2] dx = outputs[0] N = x.shape[0] if self.lower: F = numpy.tril(dz) for k in xrange(N - 1, -1, -1): for j in xrange(k + 1, N): for i in xrange(j, N): F[i, k] -= F[i, j] * L[j, k] F[j, k] -= F[i, j] * L[i, k] for j in xrange(k + 1, N): F[j, k] /= L[k, k] F[k, k] -= L[j, k] * F[j, k] F[k, k] /= (2 * L[k, k]) else: F = numpy.triu(dz) for k in xrange(N - 1, -1, -1): for j in xrange(k + 1, N): for i in xrange(j, N): F[k, i] -= F[j, i] * L[k, j] F[k, j] -= F[j, i] * L[k, i] for j in xrange(k + 1, N): F[k, j] /= L[k, k] F[k, k] -= L[k, j] * F[k, j] F[k, k] /= (2 * L[k, k]) dx[0] = F
Example #20
Source File: data.py From controllable-text-attribute-transfer with Apache License 2.0 | 5 votes |
def subsequent_mask(size): "Mask out subsequent positions." attn_shape = (1, size, size) subsequent_mask = np.triu(np.ones(attn_shape), k=1).astype('uint8') return torch.from_numpy(subsequent_mask) == 0
Example #21
Source File: graphTools.py From graph-neural-networks with GNU General Public License v3.0 | 5 votes |
def edgeFailSampling(W, p): """ edgeFailSampling: randomly delete the edges of a given graph Input: W (np.array): adjacency matrix p (float): probability of deleting an edge Output: W (np.array): adjacency matrix with some edges randomly deleted Obs.: The resulting graph need not be connected (even if the input graph is) """ assert 0 <= p <= 1 N = W.shape[0] assert W.shape[1] == N undirected = np.allclose(W, W.T, atol = zeroTolerance) maskEdges = np.random.rand(N, N) maskEdges = (maskEdges > p).astype(W.dtype) # Put a 1 with probability 1-p W = maskEdges * W if undirected: W = np.triu(W) W = W + W.T return W
Example #22
Source File: slinalg.py From D-VAE with MIT License | 5 votes |
def __init__(self, lower=True): assert lower in [True, False] self.lower = lower if lower: self.tri0 = numpy.tril self.tri1 = lambda a: numpy.triu(a, 1) else: self.tri0 = numpy.triu self.tri1 = lambda a: numpy.tril(a, -1)
Example #23
Source File: model.py From controllable-text-attribute-transfer with Apache License 2.0 | 5 votes |
def subsequent_mask(size): "Mask out subsequent positions." attn_shape = (1, size, size) subsequent_mask = np.triu(np.ones(attn_shape), k=1).astype('uint8') return torch.from_numpy(subsequent_mask) == 0
Example #24
Source File: nlinalg.py From D-VAE with MIT License | 5 votes |
def perform(self, node, inputs, outputs): """ Implements the "reverse-mode" gradient for the eigensystem of a square matrix. """ x, w, v, W, V = inputs N = x.shape[0] outer = numpy.outer def G(n): return sum(v[:, m] * V.T[n].dot(v[:, m]) / (w[n] - w[m]) for m in xrange(N) if m != n) g = sum(outer(v[:, n], v[:, n] * W[n] + G(n)) for n in xrange(N)) # Numpy's eigh(a, 'L') (eigh(a, 'U')) is a function of tril(a) # (triu(a)) only. This means that partial derivative of # eigh(a, 'L') (eigh(a, 'U')) with respect to a[i,j] is zero # for i < j (i > j). At the same time, non-zero components of # the gradient must account for the fact that variation of the # opposite triangle contributes to variation of two elements # of Hermitian (symmetric) matrix. The following line # implements the necessary logic. out = self.tri0(g) + self.tri1(g).T # The call to self.tri0 in perform upcast from float32 to # float64 or from int* to int64 in numpy 1.6.1 but not in # 1.6.2. We do not want version dependent dtype in Theano. # We think it should be the same as the output. outputs[0][0] = numpy.asarray(out, dtype=node.outputs[0].dtype)
Example #25
Source File: model.py From controllable-text-attribute-transfer with Apache License 2.0 | 5 votes |
def subsequent_mask(size): "Mask out subsequent positions." attn_shape = (1, size, size) subsequent_mask = np.triu(np.ones(attn_shape), k=1).astype('uint8') return torch.from_numpy(subsequent_mask) == 0
Example #26
Source File: test_twodim_base.py From vnpy_crypto with MIT License | 5 votes |
def test_mask_indices(): # simple test without offset iu = mask_indices(3, np.triu) a = np.arange(9).reshape(3, 3) assert_array_equal(a[iu], array([0, 1, 2, 4, 5, 8])) # Now with an offset iu1 = mask_indices(3, np.triu, 1) assert_array_equal(a[iu1], array([1, 2, 5]))
Example #27
Source File: test_linalg.py From vnpy_crypto with MIT License | 5 votes |
def test_dynamic_programming_logic(self): # Test for the dynamic programming part # This test is directly taken from Cormen page 376. arrays = [np.random.random((30, 35)), np.random.random((35, 15)), np.random.random((15, 5)), np.random.random((5, 10)), np.random.random((10, 20)), np.random.random((20, 25))] m_expected = np.array([[0., 15750., 7875., 9375., 11875., 15125.], [0., 0., 2625., 4375., 7125., 10500.], [0., 0., 0., 750., 2500., 5375.], [0., 0., 0., 0., 1000., 3500.], [0., 0., 0., 0., 0., 5000.], [0., 0., 0., 0., 0., 0.]]) s_expected = np.array([[0, 1, 1, 3, 3, 3], [0, 0, 2, 3, 3, 3], [0, 0, 0, 3, 3, 3], [0, 0, 0, 0, 4, 5], [0, 0, 0, 0, 0, 5], [0, 0, 0, 0, 0, 0]], dtype=int) s_expected -= 1 # Cormen uses 1-based index, python does not. s, m = _multi_dot_matrix_chain_order(arrays, return_costs=True) # Only the upper triangular part (without the diagonal) is interesting. assert_almost_equal(np.triu(s[:-1, 1:]), np.triu(s_expected[:-1, 1:])) assert_almost_equal(np.triu(m), np.triu(m_expected))
Example #28
Source File: bierman.py From vnpy_crypto with MIT License | 5 votes |
def udu(M): """Construct the UDU' decomposition of a positive, semidefinite matrix M Parameters ---------- M : [n, n] array Matrix to factorize Returns ------- UDU : UDU_decomposition of size n UDU' representation of M """ assert np.allclose(M, M.T), 'M must be symmetric, positive semidefinite' n = M.shape[0] # perform Bierman's COV2UD subroutine (fucking inclusive indices) M = np.triu(M) U = np.eye(n) d = np.zeros(n) for j in reversed(range(2, n + 1)): d[j - 1] = M[j - 1, j - 1] if d[j - 1] > 0: alpha = 1.0 / d[j - 1] else: alpha = 0.0 for k in range(1, j): beta = M[k - 1, j - 1] U[k - 1, j - 1] = alpha * beta M[0:k, k - 1] = M[0:k, k - 1] - beta * U[0:k, j - 1] d[0] = M[0, 0] return UDU_decomposition(U, d)
Example #29
Source File: test_sample.py From strawberryfields with Apache License 2.0 | 5 votes |
def test_invalid_adjacency(self, dim): """Test if function raises a ``ValueError`` for a matrix that is not symmetric""" with pytest.raises(ValueError, match="Input must be a NumPy array"): adj_asym = np.triu(np.ones((dim, dim))) sample.sample(A=adj_asym, n_mean=1.0)
Example #30
Source File: test_twodim_base.py From vnpy_crypto with MIT License | 5 votes |
def test_tril_triu_with_inf(): # Issue 4859 arr = np.array([[1, 1, np.inf], [1, 1, 1], [np.inf, 1, 1]]) out_tril = np.array([[1, 0, 0], [1, 1, 0], [np.inf, 1, 1]]) out_triu = out_tril.T assert_array_equal(np.triu(arr), out_triu) assert_array_equal(np.tril(arr), out_tril)