def vdot(a, b): # pylint: disable=missing-docstring
a, b = np_array_ops._promote_dtype(a, b)
a = np_array_ops.reshape(a, [-1])
b = np_array_ops.reshape(b, [-1])
if a.dtype == np_dtypes.complex128 or a.dtype == np_dtypes.complex64:
a = conj(a)
return dot(a, b)
def kron(a, b): # pylint: disable=missing-function-docstring
# pylint: disable=protected-access,g-complex-comprehension
a, b = np_array_ops._promote_dtype(a, b)
t_a = np_utils.cond(
a.ndim < b.ndim,
lambda: np_array_ops.reshape( # pylint: disable=g-long-lambda
a, np_array_ops._pad_left_to(b.ndim, a.shape)),
lambda: a)
t_b = np_utils.cond(
b.ndim < a.ndim,
lambda: np_array_ops.reshape( # pylint: disable=g-long-lambda
b, np_array_ops._pad_left_to(a.ndim, b.shape)),
lambda: b)
def _make_shape(shape, prepend):
ones = array_ops.ones_like(shape)
if prepend:
shapes = [ones, shape]
else:
shapes = [shape, ones]
return array_ops.reshape(array_ops.stack(shapes, axis=1), [-1])
a_shape = array_ops.shape(t_a)
b_shape = array_ops.shape(t_b)
a_reshaped = np_array_ops.reshape(t_a, _make_shape(a_shape, False))
b_reshaped = np_array_ops.reshape(t_b, _make_shape(b_shape, True))
out_shape = a_shape * b_shape
return np_array_ops.reshape(a_reshaped * b_reshaped, out_shape)
def _bin_op(tf_fun, a, b, promote=True):
if promote:
a, b = np_array_ops._promote_dtype(a, b) # pylint: disable=protected-access
else:
a = np_array_ops.array(a)
b = np_array_ops.array(b)
return np_utils.tensor_to_ndarray(tf_fun(a.data, b.data))
def einsum(subscripts, *operands, **kwargs): # pylint: disable=missing-docstring
casting = kwargs.get('casting', 'safe')
optimize = kwargs.get('optimize', False)
if casting == 'safe':
operands = np_array_ops._promote_dtype(*operands) # pylint: disable=protected-access
elif casting == 'no':
operands = [np_array_ops.asarray(x) for x in operands]
else:
raise ValueError('casting policy not supported: %s' % casting)
if not optimize:
# TF doesn't have a "no optimization" option.
# TODO(wangpeng): Print a warning that np and tf use different
# optimizations.
tf_optimize = 'greedy'
elif optimize == True: # pylint: disable=singleton-comparison,g-explicit-bool-comparison
tf_optimize = 'greedy'
elif optimize == 'greedy':
tf_optimize = 'greedy'
elif optimize == 'optimal':
tf_optimize = 'optimal'
else:
raise ValueError('`optimize` method not supported: %s' % optimize)
operands = [x.data for x in operands]
res = special_math_ops.einsum(subscripts, *operands, optimize=tf_optimize)
res = np_utils.tensor_to_ndarray(res)
return res
def einsum(subscripts, *operands, **kwargs): # pylint: disable=missing-docstring
casting = kwargs.get('casting', 'safe')
optimize = kwargs.get('optimize', False)
if casting == 'safe':
operands = np_array_ops._promote_dtype(*operands) # pylint: disable=protected-access
elif casting == 'no':
operands = [np_array_ops.asarray(x) for x in operands]
else:
raise ValueError(
'Invalid value for argument `casting`. '
f'Expected casting="safe" or casting="no". Received: casting={casting}')
if not optimize:
# TF doesn't have a "no optimization" option.
# TODO(wangpeng): Print a warning that np and tf use different
# optimizations.
tf_optimize = 'greedy'
elif optimize == True: # pylint: disable=singleton-comparison,g-explicit-bool-comparison
tf_optimize = 'greedy'
elif optimize == 'greedy':
tf_optimize = 'greedy'
elif optimize == 'optimal':
tf_optimize = 'optimal'
else:
raise ValueError(
'Invalid value for argument `optimize`. '
'Expected one of {True, "greedy", "optimal"}. '
f'Received: optimize={optimize}')
res = special_math_ops.einsum(subscripts, *operands, optimize=tf_optimize)
return res
def clip(a, a_min, a_max): # pylint: disable=missing-docstring
if a_min is None and a_max is None:
raise ValueError('Not more than one of `a_min` and `a_max` may be `None`.')
if a_min is None:
return minimum(a, a_max)
elif a_max is None:
return maximum(a, a_min)
else:
a, a_min, a_max = np_array_ops._promote_dtype(a, a_min, a_max) # pylint: disable=protected-access
return clip_ops.clip_by_value(*np_utils.tf_broadcast(a, a_min, a_max))
def kron(a, b): # pylint: disable=missing-function-docstring
# pylint: disable=protected-access,g-complex-comprehension
a, b = np_array_ops._promote_dtype(a, b)
ndim = max(a.ndim, b.ndim)
if a.ndim < ndim:
a = np_array_ops.reshape(a, np_array_ops._pad_left_to(ndim, a.shape))
if b.ndim < ndim:
b = np_array_ops.reshape(b, np_array_ops._pad_left_to(ndim, b.shape))
a_reshaped = np_array_ops.reshape(a, [i for d in a.shape for i in (d, 1)])
b_reshaped = np_array_ops.reshape(b, [i for d in b.shape for i in (1, d)])
out_shape = tuple(np.multiply(a.shape, b.shape))
return np_array_ops.reshape(a_reshaped * b_reshaped, out_shape)
