def _comparison(tf_fun, x1, x2, cast_bool_to_int=False):
dtype = utils.result_type(x1, x2)
# Cast x1 and x2 to the result_type if needed.
x1 = array_ops.array(x1, dtype=dtype)
x2 = array_ops.array(x2, dtype=dtype)
x1 = x1.data
x2 = x2.data
if cast_bool_to_int and x1.dtype == tf.bool:
x1 = tf.cast(x1, tf.int32)
x2 = tf.cast(x2, tf.int32)
return utils.tensor_to_ndarray(tf_fun(x1, x2))
def arange(start, stop=None, step=1, dtype=None):
"""Returns `step`-separated values in the range [start, stop).
Args:
start: Start of the interval. Included in the range.
stop: End of the interval. If not specified, `start` is treated as 0 and
`start` value is used as `stop`. If specified, it is not included in the
range if `step` is integer. When `step` is floating point, it may or may
not be included.
step: The difference between 2 consecutive values in the output range. It is
recommended to use `linspace` instead of using non-integer values for
`step`.
dtype: Optional. Type of the resulting ndarray. Could be a python type, a
NumPy type or a TensorFlow `DType`. If not provided, the largest type of
`start`, `stop`, `step` is used.
Raises:
ValueError: If step is zero.
"""
if not step:
raise ValueError('step must be non-zero.')
if dtype:
dtype = utils.result_type(dtype)
else:
if stop is None:
dtype = utils.result_type(start, step)
else:
dtype = utils.result_type(start, step, stop)
if step > 0 and ((stop is not None and start > stop) or
(stop is None and start < 0)):
return array([], dtype=dtype)
if step < 0 and ((stop is not None and start < stop) or
(stop is None and start > 0)):
return array([], dtype=dtype)
# TODO(srbs): There are some bugs when start or stop is float type and dtype
# is integer type.
return arrays_lib.tensor_to_ndarray(
tf.cast(tf.range(start, limit=stop, delta=step), dtype=dtype))
def full_like(a, fill_value, dtype=None, order='K', subok=True, shape=None): # pylint: disable=missing-docstring,redefined-outer-name
"""order, subok and shape arguments mustn't be changed."""
if order != 'K':
raise ValueError('Non-standard orders are not supported.')
if not subok:
raise ValueError('subok being False is not supported.')
if shape:
raise ValueError('Overriding the shape is not supported.')
a = asarray(a).data
dtype = dtype or utils.result_type(a)
fill_value = asarray(fill_value, dtype=dtype)
return arrays_lib.tensor_to_ndarray(
tf.broadcast_to(fill_value.data, tf.shape(a)))
def trace(a, offset=0, axis1=0, axis2=1, dtype=None): # pylint: disable=missing-docstring
if dtype:
dtype = utils.result_type(dtype)
a = array_ops.asarray(a, dtype).data
if offset == 0:
a_shape = a.shape
if a_shape.rank is not None:
rank = len(a_shape)
if (axis1 == -2 or axis1 == rank - 2) and (axis2 == -1
or axis2 == rank - 1):
return utils.tensor_to_ndarray(tf.linalg.trace(a))
a = array_ops.diagonal(a, offset, axis1, axis2)
return array_ops.sum(a, -1, dtype)
def zeros_like(a, dtype=None):
"""Returns an array of zeros with the shape and type of the input array.
Args:
a: array_like. Could be an ndarray, a Tensor or any object that can be
converted to a Tensor using `tf.convert_to_tensor`.
dtype: Optional, defaults to dtype of the input array. The type of the
resulting ndarray. Could be a python type, a NumPy type or a TensorFlow
`DType`.
Returns:
An ndarray.
"""
if isinstance(a, arrays_lib.ndarray):
a = a.data
if dtype is None:
# We need to let utils.result_type decide the dtype, not tf.zeros_like
dtype = utils.result_type(a)
else:
# TF and numpy has different interpretations of Python types such as
# `float`, so we let `utils.result_type` decide.
dtype = utils.result_type(dtype)
dtype = tf.as_dtype(dtype) # Work around b/149877262
return arrays_lib.tensor_to_ndarray(tf.zeros_like(a, dtype))
def logspace(start,
stop,
num=50,
endpoint=True,
base=10.0,
dtype=None,
axis=0):
dtype = utils.result_type(start, stop, dtype)
result = linspace(start,
stop,
num=num,
endpoint=endpoint,
dtype=dtype,
axis=axis).data
result = tf.pow(tf.cast(base, result.dtype), result)
if dtype:
result = tf.cast(result, dtype)
return arrays.tensor_to_ndarray(result)
def zeros(shape, dtype=float): # pylint: disable=redefined-outer-name
"""Returns an ndarray with the given shape and type filled with zeros.
Args:
shape: A fully defined shape. Could be - NumPy array or a python scalar,
list or tuple of integers, - TensorFlow tensor/ndarray of integer type and
rank <=1.
dtype: Optional, defaults to float. The type of the resulting ndarray. Could
be a python type, a NumPy type or a TensorFlow `DType`.
Returns:
An ndarray.
"""
if dtype:
dtype = utils.result_type(dtype)
if isinstance(shape, arrays_lib.ndarray):
shape = shape.data
return arrays_lib.tensor_to_ndarray(tf.zeros(shape, dtype=dtype))
def geomspace(start, stop, num=50, endpoint=True, dtype=float): # pylint: disable=missing-docstring
if dtype:
dtype = utils.result_type(dtype)
if num < 0:
raise ValueError('Number of samples {} must be non-negative.'.format(num))
if not num:
return empty([0])
step = 1.
if endpoint:
if num > 1:
step = tf.pow((stop / start), 1 / (num - 1))
else:
step = tf.pow((stop / start), 1 / num)
result = tf.cast(tf.range(num), step.dtype)
result = tf.pow(step, result)
result = tf.multiply(result, start)
if dtype:
result = tf.cast(result, dtype=dtype)
return arrays_lib.tensor_to_ndarray(result)
def tri(N, M=None, k=0, dtype=None): # pylint: disable=invalid-name,missing-docstring
M = M if M is not None else N
if dtype is not None:
dtype = utils.result_type(dtype)
else:
dtype = dtypes.default_float_type()
if k < 0:
lower = -k - 1
if lower > N:
r = tf.zeros([N, M], dtype)
else:
# Keep as tf bool, since we create an upper triangular matrix and invert
# it.
o = tf.ones([N, M], dtype=tf.bool)
r = tf.cast(tf.math.logical_not(tf.linalg.band_part(o, lower, -1)), dtype)
else:
o = tf.ones([N, M], dtype)
if k > M:
r = o
else:
r = tf.linalg.band_part(o, -1, k)
return utils.tensor_to_ndarray(r)
def linspace( # pylint: disable=missing-docstring
start,
stop,
num=50,
endpoint=True,
retstep=False,
dtype=float,
axis=0):
if dtype:
dtype = utils.result_type(dtype)
start = array_ops.array(start, dtype=dtype).data
stop = array_ops.array(stop, dtype=dtype).data
if num < 0:
raise ValueError(
'Number of samples {} must be non-negative.'.format(num))
step = tf.convert_to_tensor(np.nan)
if endpoint:
result = tf.linspace(start, stop, num, axis=axis)
if num > 1:
step = (stop - start) / (num - 1)
else:
# tf.linspace does not support endpoint=False so we manually handle it
# here.
if num > 1:
step = ((stop - start) / num)
new_stop = tf.cast(stop, step.dtype) - step
start = tf.cast(start, new_stop.dtype)
result = tf.linspace(start, new_stop, num, axis=axis)
else:
result = tf.linspace(start, stop, num, axis=axis)
if dtype:
result = tf.cast(result, dtype)
if retstep:
return arrays.tensor_to_ndarray(result), arrays.tensor_to_ndarray(step)
else:
return arrays.tensor_to_ndarray(result)
def _promote_dtype(*arrays): dtype = utils.result_type(*arrays) return [asarray(a, dtype=dtype) for a in arrays]
def asarray(a, dtype=None):
if dtype:
dtype = utils.result_type(dtype)
if isinstance(a, arrays_lib.ndarray) and (not dtype or dtype == a.dtype):
return a
return array(a, dtype, copy=False)