def test_array():
# array's function is very similar to asarray, so we mainly test the
# `copy` argument.
test_case = Cases()
for array in test_case.array_sets:
arr1 = mnp.asarray(array)
arr2 = mnp.array(arr1, copy=False)
arr3 = mnp.array(arr1)
arr4 = mnp.asarray(array, dtype='int32')
arr5 = mnp.asarray(arr4, dtype=mnp.int32)
assert arr1 is arr2
assert arr1 is not arr3
assert arr4 is arr5
def test_isscalar():
assert mnp.isscalar(1) == onp.isscalar(1)
assert mnp.isscalar(2.3) == onp.isscalar(2.3)
assert mnp.isscalar([4.5]) == onp.isscalar([4.5])
assert mnp.isscalar(False) == onp.isscalar(False)
assert mnp.isscalar(mnp.array(True)) == onp.isscalar(onp.array(True))
assert mnp.isscalar('numpy') == onp.isscalar('numpy')
def test_full_like():
test_case = Cases()
for mnp_proto, onp_proto in zip(test_case.mnp_prototypes, test_case.onp_prototypes):
shape = onp.zeros_like(onp_proto).shape
fill_value = rand_int()
actual = mnp.full_like(mnp_proto, mnp.array(fill_value)).asnumpy()
expected = onp.full_like(onp_proto, fill_value)
match_array(actual, expected)
for i in range(len(shape) - 1, 0, -1):
fill_value = rand_int(*shape[i:])
actual = mnp.full_like(mnp_proto, mnp.array(fill_value)).asnumpy()
expected = onp.full_like(onp_proto, fill_value)
match_array(actual, expected)
fill_value = rand_int(1, *shape[i + 1:])
actual = mnp.full_like(mnp_proto, mnp.array(fill_value)).asnumpy()
expected = onp.full_like(onp_proto, fill_value)
match_array(actual, expected)
def test_array():
# array's function is very similar to asarray, so we mainly test the
# `copy` argument.
test_case = Cases()
for array in test_case.array_sets:
arr1 = mnp.asarray(array)
arr2 = mnp.array(arr1, copy=False)
arr3 = mnp.array(arr1)
arr4 = mnp.asarray(array, dtype='int32')
arr5 = mnp.asarray(arr4, dtype=mnp.int32)
assert arr1 is arr2
assert arr1 is not arr3
assert arr4 is arr5
# Additional tests for nested tensor/numpy_array mixture
mnp_input = [(mnp.ones(3, ), mnp.ones(3)), [[1, 1, 1], (1, 1, 1)]]
onp_input = [(onp.ones(3, ), onp.ones(3)), [[1, 1, 1], (1, 1, 1)]]
actual = onp.array(onp_input)
expected = mnp.array(mnp_input).asnumpy()
match_array(actual, expected, error=7)
