def poisson(lam=1.0, size=None):
if size is None:
size = ()
elif np_utils.isscalar(size):
size = (size, )
return np_utils.tensor_to_ndarray(
random_ops.random_poisson(shape=size, lam=lam, dtype=np_dtypes.int64))
def standard_normal(size=None):
# TODO(wangpeng): Use new stateful RNG
if size is None:
size = ()
elif np_utils.isscalar(size):
size = (size,)
dtype = np_dtypes.default_float_type()
return random_ops.random_normal(size, dtype=dtype)
def randn(*args):
"""Returns samples from a normal distribution.
Uses `tf.random_normal`.
Args:
*args: The shape of the output array.
Returns:
An ndarray with shape `args` and dtype `float64`.
"""
# TODO(wangpeng): Use new stateful RNG
if np_utils.isscalar(args):
args = (args, )
dtype = np_dtypes.default_float_type()
return random_ops.random_normal(args, dtype=dtype)
def randn(*args):
"""Returns samples from a normal distribution.
Uses `tf.random_normal`.
Args:
*args: The shape of the output array.
Returns:
An ndarray with shape `args` and dtype `float64`.
"""
# TODO(wangpeng): Use new stateful RNG
if np_utils.isscalar(args):
args = (args, )
return np_utils.tensor_to_ndarray(
random_ops.random_normal(args, dtype=DEFAULT_RANDN_DTYPE))
def full(shape, fill_value, dtype=None): # pylint: disable=redefined-outer-name
"""Returns an array with given shape and dtype filled with `fill_value`.
Args:
shape: A valid shape object. Could be a native python object or an object of
type ndarray, numpy.ndarray or tf.TensorShape.
fill_value: 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 `fill_value`. The type of the
resulting ndarray. Could be a python type, a NumPy type or a TensorFlow
`DType`.
Returns:
An ndarray.
Raises:
ValueError: if `fill_value` can not be broadcast to shape `shape`.
"""
fill_value = asarray(fill_value, dtype=dtype)
if np_utils.isscalar(shape):
shape = array_ops.reshape(shape, [1])
return np_arrays.tensor_to_ndarray(
array_ops.broadcast_to(fill_value.data, shape))
def poisson(lam=1.0, size=None):
if size is None:
size = ()
elif np_utils.isscalar(size):
size = (size,)
return random_ops.random_poisson(shape=size, lam=lam, dtype=np_dtypes.int_)
