def geomspace(start, stop, num=50, endpoint=True, dtype=None, axis=0): # pylint: disable=missing-docstring
dtype = dtypes.as_dtype(dtype) if dtype else np_utils.result_type(
start, stop, float(num), np_array_ops.zeros((), dtype))
computation_dtype = np.promote_types(dtype.as_numpy_dtype, np.float32)
start = np_array_ops.asarray(start, dtype=computation_dtype)
stop = np_array_ops.asarray(stop, dtype=computation_dtype)
# follow the numpy geomspace convention for negative and complex endpoints
start_sign = 1 - np_array_ops.sign(np_array_ops.real(start))
stop_sign = 1 - np_array_ops.sign(np_array_ops.real(stop))
signflip = 1 - start_sign * stop_sign // 2
res = signflip * logspace(log10(signflip * start),
log10(signflip * stop),
num,
endpoint=endpoint,
base=10.0,
dtype=computation_dtype,
axis=0)
if axis != 0:
res = np_array_ops.moveaxis(res, 0, axis)
return math_ops.cast(res, dtype)
def testSign(self):
state = np.random.RandomState(0)
test_types = [np.float16, np.float32, np.float64, np.int32, np.int64,
np.complex64, np.complex128]
test_shapes = [(), (1,), (2, 3, 4), (2, 3, 0, 4)]
for dtype in test_types:
for shape in test_shapes:
if np.issubdtype(dtype, np.complex):
arr = (np.asarray(state.randn(*shape) * 100, dtype=dtype) +
1j * np.asarray(state.randn(*shape) * 100, dtype=dtype))
else:
arr = np.asarray(state.randn(*shape) * 100, dtype=dtype)
self.match(np_array_ops.sign(arr), np.sign(arr))
