def test_update_rank1_f_f(self):
"""Rank1 update on F layout matrix, explicit F array output"""
a = np.array([2, 3, 4])
b = np.array([1, 3, 4, 5])
c = np.asfortranarray(np.arange(1, 13).reshape(3, 4))
res = np.zeros((3, 4), order='F')
gulinalg.update_rank1(a, b, c, out=res)
ref = np.dot(a.reshape(3, 1), b.reshape(1, 4)) + c
assert_allclose(res, ref)
def test_output_non_contiguous(self):
"""Output not contiguous"""
a = np.random.randn(M)
b = np.random.randn(N)
c = np.ascontiguousarray(np.random.randn(M, N))
res = np.zeros((M, N, 2), order='C')[:, :, 0]
gulinalg.update_rank1(a, b, c, out=res)
ref = np.dot(a.reshape(M, 1), b.reshape(1, N)) + c
assert_allclose(res, ref)
def test_update_rank1_f_f(self):
"""Rank1 update on F layout matrix, explicit F array output"""
a = np.array([2, 3, 4])
b = np.array([1, 3, 4, 5])
c = np.asfortranarray(np.arange(1, 13).reshape(3, 4))
res = np.zeros((3, 4), order='F')
gulinalg.update_rank1(a, b, c, out=res)
ref = np.dot(a.reshape(3, 1), b.reshape(1, 4)) + c
assert_allclose(res, ref)
def test_output_non_contiguous(self):
"""Output not contiguous"""
a = np.random.randn(M)
b = np.random.randn(N)
c = np.ascontiguousarray(np.random.randn(M, N))
res = np.zeros((M, N, 2), order='C')[:, :, 0]
gulinalg.update_rank1(a, b, c, out=res)
ref = np.dot(a.reshape(M, 1), b.reshape(1, N)) + c
assert_allclose(res, ref)
def test_size_one_vector(self):
"""Test vector inputs of size one"""
a = np.random.randn(1)
b = np.random.randn(1)
c = np.ascontiguousarray(np.random.randn(1, 1))
res = gulinalg.update_rank1(a, b, c)
ref = np.dot(a.reshape(1, 1), b.reshape(1, 1)) + c
assert_allclose(res, ref)
def test_size_zero_matrix(self):
"""Test matrix input of size zero"""
a = np.zeros(0)
b = np.zeros(2)
c = np.full((0, 2), np.nan)
res = gulinalg.update_rank1(a, b, c)
ref = np.dot(np.zeros((0, 0)), np.zeros((0, 2))) + c
assert_allclose(res, ref)
def test_size_zero_vector(self):
"""Test vector input of size zero"""
a = np.zeros(1)
b = np.zeros(0)
c = np.ascontiguousarray(np.random.randn(1, 0))
res = gulinalg.update_rank1(a, b, c)
ref = np.dot(np.zeros((1, 0)), np.zeros((0, 0))) + c
assert_allclose(res, ref)
def test_update_rank1_for_complex_numbers_no_conjugate_transpose(self):
"""Test for complex numbers but no conjuage transpose"""
a = np.array([1 + 3j, 3 - 4j])
b = np.array([1 - 2j, 4 + 5j])
c = np.array([[1 + 2j, 3 + 4j], [5 + 6j, 7 + -8j]])
res = gulinalg.update_rank1(a, b, c, conjugate=False)
ref = np.dot(a.reshape(2, 1), b.reshape(1, 2)) + c
assert_allclose(res, ref)
def test_update_rank1_for_complex_numbers(self):
"""Test for complex numbers"""
a = np.array([1 + 3j, 3 - 4j])
b = np.array([1 - 2j, 4 + 5j])
c = np.array([[1 + 2j, 3 + 4j], [5 + 6j, 7 + -8j]])
res = gulinalg.update_rank1(a, b, c)
ref = np.dot(a.reshape(2, 1), b.conj().reshape(1, 2)) + c
assert_allclose(res, ref)
def test_update_rank1_f(self):
"""Rank update on F layout matrix"""
a = np.random.randn(M)
b = np.random.randn(N)
c = np.asfortranarray(np.random.randn(M, N))
res = gulinalg.update_rank1(a, b, c)
ref = np.dot(a.reshape(M, 1), b.reshape(1, N)) + c
assert_allclose(res, ref)
def test_size_one_vector(self):
"""Test vector inputs of size one"""
a = np.random.randn(1)
b = np.random.randn(1)
c = np.ascontiguousarray(np.random.randn(1, 1))
res = gulinalg.update_rank1(a, b, c)
ref = np.dot(a.reshape(1, 1), b.reshape(1, 1)) + c
assert_allclose(res, ref)
def test_update_rank1_for_complex_numbers_no_conjugate_transpose(self):
"""Test for complex numbers but no conjuage transpose"""
a = np.array([1 + 3j, 3 - 4j])
b = np.array([1 - 2j, 4 + 5j])
c = np.array([[1 + 2j, 3 + 4j], [5 + 6j, 7 + -8j]])
res = gulinalg.update_rank1(a, b, c, conjugate=False)
ref = np.dot(a.reshape(2, 1), b.reshape(1, 2)) + c
assert_allclose(res, ref)
def test_update_rank1_for_complex_numbers(self):
"""Test for complex numbers"""
a = np.array([1 + 3j, 3 - 4j])
b = np.array([1 - 2j, 4 + 5j])
c = np.array([[1 + 2j, 3 + 4j], [5 + 6j, 7 + -8j]])
res = gulinalg.update_rank1(a, b, c)
ref = np.dot(a.reshape(2, 1), b.conj().reshape(1, 2)) + c
assert_allclose(res, ref)
def test_update_rank1_f(self):
"""Rank update on F layout matrix"""
a = np.random.randn(M)
b = np.random.randn(N)
c = np.asfortranarray(np.random.randn(M, N))
res = gulinalg.update_rank1(a, b, c)
ref = np.dot(a.reshape(M, 1), b.reshape(1, N)) + c
assert_allclose(res, ref)
def test_infinity_handling(self):
"""Infinity in one output shouldn't contaminate remaining outputs"""
a = np.array([[1, 2], [1, np.inf]])
b = np.array([3, 4])
c = np.array([[1, 2], [3, 4]])
ref = np.array([[[4, 6], [9, 12]], [[4, 6], [np.inf, np.inf]]])
res = gulinalg.update_rank1(a, b, c)
assert_allclose(res, ref)
def test_size_zero_vector(self):
"""Test vector input of size zero"""
a = np.zeros(1)
b = np.zeros(0)
c = np.ascontiguousarray(np.random.randn(1, 0))
res = gulinalg.update_rank1(a, b, c)
ref = np.dot(np.zeros((1, 0)), np.zeros((0, 0))) + c
assert_allclose(res, ref)
def test_size_zero_matrix(self):
"""Test matrix input of size zero"""
a = np.zeros(0)
b = np.zeros(2)
c = np.full((0, 2), np.nan)
res = gulinalg.update_rank1(a, b, c)
ref = np.dot(np.zeros((0, 0)), np.zeros((0, 2))) + c
assert_allclose(res, ref)
def test_infinity_handling(self):
"""Infinity in one output shouldn't contaminate remaining outputs"""
a = np.array([[1, 2], [1, np.inf]])
b = np.array([3, 4])
c = np.array([[1, 2], [3, 4]])
ref = np.array([[[4, 6], [9, 12]],
[[4, 6], [np.inf, np.inf]]])
res = gulinalg.update_rank1(a, b, c)
assert_allclose(res, ref)
def test_input_non_contiguous_matrix(self):
"""Non contiguous matrix input"""
a = np.random.randn(M)
b = np.random.randn(N)
c = np.ascontiguousarray(np.random.randn(M, N, 2))[:, :, 0]
assert not c.flags.c_contiguous and not c.flags.f_contiguous
res = gulinalg.update_rank1(a, b, c)
ref = np.dot(a.reshape(M, 1), b.reshape(1, N)) + c
assert_allclose(res, ref)
def test_input_non_contiguous_matrix(self):
"""Non contiguous matrix input"""
a = np.random.randn(M)
b = np.random.randn(N)
c = np.ascontiguousarray(np.random.randn(M, N, 2))[:, :, 0]
assert not c.flags.c_contiguous and not c.flags.f_contiguous
res = gulinalg.update_rank1(a, b, c)
ref = np.dot(a.reshape(M, 1), b.reshape(1, N)) + c
assert_allclose(res, ref)
def test_size_one_vector(self):
"""Test broadcasting for vector inputs of size one"""
a = np.ascontiguousarray(np.random.randn(10, 1))
b = np.ascontiguousarray(np.random.randn(10, 1))
c = np.ascontiguousarray(np.random.randn(10, 1, 1))
res = gulinalg.update_rank1(a, b, c)
assert res.shape == (10, 1, 1)
ref = np.stack([np.dot(a[i].reshape(1, 1), b[i].reshape(1, 1)) + c[i]
for i in range(len(c))])
assert_allclose(res, ref)
def test_input_non_contiguous_vectors(self):
"""Not contiguous vector inputs"""
a = np.ascontiguousarray(np.random.randn(M, N, 2))[:, 0, 0]
b = np.ascontiguousarray(np.random.randn(M, N, 2))[0, :, 0]
c = np.ascontiguousarray(np.random.randn(M, N))
assert not a.flags.c_contiguous and not a.flags.f_contiguous
assert not b.flags.c_contiguous and not b.flags.f_contiguous
res = gulinalg.update_rank1(a, b, c)
ref = np.dot(a.reshape(M, 1), b.reshape(1, N)) + c
assert_allclose(res, ref)
def test_broadcast(self):
"""test broadcast rank1 update"""
a = np.ascontiguousarray(np.random.randn(10, M))
b = np.ascontiguousarray(np.random.randn(10, N))
c = np.ascontiguousarray(np.random.randn(M, N))
res = gulinalg.update_rank1(a, b, c)
assert res.shape == (10, M, N)
ref = np.stack([np.dot(a[i].reshape(M, 1), b[i].reshape(1, N)) + c
for i in range(len(b))])
assert_allclose(res, ref)
def test_input_non_contiguous_vectors(self):
"""Not contiguous vector inputs"""
a = np.ascontiguousarray(np.random.randn(M, N, 2))[:, 0, 0]
b = np.ascontiguousarray(np.random.randn(M, N, 2))[0, :, 0]
c = np.ascontiguousarray(np.random.randn(M, N))
assert not a.flags.c_contiguous and not a.flags.f_contiguous
assert not b.flags.c_contiguous and not b.flags.f_contiguous
res = gulinalg.update_rank1(a, b, c)
ref = np.dot(a.reshape(M, 1), b.reshape(1, N)) + c
assert_allclose(res, ref)
def test_size_zero_matrix(self):
"""Test broadcasting for matrix input of size zero"""
a = np.ascontiguousarray(np.random.randn(10, 0))
b = np.ascontiguousarray(np.random.randn(10, 2))
c = np.ascontiguousarray(np.random.randn(10, 0, 2))
res = gulinalg.update_rank1(a, b, c)
assert res.shape == (10, 0, 2)
ref = np.stack([np.dot(np.zeros((0, 0)), np.zeros((0, 2))) + c[i]
for i in range(len(c))])
assert_allclose(res, ref)
def test_size_one_vector(self):
"""Test broadcasting for vector inputs of size one"""
a = np.ascontiguousarray(np.random.randn(10, 1))
b = np.ascontiguousarray(np.random.randn(10, 1))
c = np.ascontiguousarray(np.random.randn(10, 1, 1))
res = gulinalg.update_rank1(a, b, c)
assert res.shape == (10, 1, 1)
ref = np.stack([np.dot(a[i].reshape(1, 1), b[i].reshape(1, 1)) + c[i]
for i in range(len(c))])
assert_allclose(res, ref)
def test_size_zero_matrix(self):
"""Test broadcasting for matrix input of size zero"""
a = np.ascontiguousarray(np.random.randn(10, 0))
b = np.ascontiguousarray(np.random.randn(10, 2))
c = np.ascontiguousarray(np.random.randn(10, 0, 2))
res = gulinalg.update_rank1(a, b, c)
assert res.shape == (10, 0, 2)
ref = np.stack([np.dot(np.zeros((0, 0)), np.zeros((0, 2))) + c[i]
for i in range(len(c))])
assert_allclose(res, ref)
def test_broadcast(self):
"""test broadcast rank1 update"""
a = np.ascontiguousarray(np.random.randn(10, M))
b = np.ascontiguousarray(np.random.randn(10, N))
c = np.ascontiguousarray(np.random.randn(M, N))
res = gulinalg.update_rank1(a, b, c)
assert res.shape == (10, M, N)
ref = np.stack([np.dot(a[i].reshape(M, 1), b[i].reshape(1, N)) + c
for i in range(len(b))])
assert_allclose(res, ref)
def test_stride_tricks(self):
"""test that matrices that are contiguous but have their dimension
overlapped *copy*, as BLAS does not support them"""
a = np.random.randn(M)
b = np.random.randn(N)
c = np.ascontiguousarray(np.random.randn(M + N))
c = np.lib.stride_tricks.as_strided(a,
shape=(M, N),
strides=(c.itemsize, c.itemsize))
res = gulinalg.update_rank1(a, b, c)
ref = np.dot(a.reshape(M, 1), b.reshape(1, N)) + c
assert_allclose(res, ref)
def test_stride_tricks(self):
"""test that matrices that are contiguous but have their dimension
overlapped *copy*, as BLAS does not support them"""
a = np.random.randn(M)
b = np.random.randn(N)
c = np.ascontiguousarray(np.random.randn(M + N))
c = np.lib.stride_tricks.as_strided(a,
shape=(M, N),
strides=(c.itemsize, c.itemsize))
res = gulinalg.update_rank1(a, b, c)
ref = np.dot(a.reshape(M, 1), b.reshape(1, N)) + c
assert_allclose(res, ref)
def test_vector(self):
"""test vectorized rank1 update"""
a = np.ascontiguousarray(np.random.randn(10, M))
b = np.ascontiguousarray(np.random.randn(10, N))
c = np.ascontiguousarray(np.random.randn(10, M, N))
for workers in [1, -1]:
res = gulinalg.update_rank1(a, b, c, workers=workers)
assert res.shape == (10, M, N)
ref = np.stack([
np.dot(a[i].reshape(M, 1), b[i].reshape(1, N)) + c[i]
for i in range(len(c))
])
assert_allclose(res, ref)
