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# derived from copyright 1999-2021 Alibaba Group Holding Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
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import numpy as np
from ... import opcodes as OperandDef
from ..utils import gen_random_seeds
from .core import TensorRandomOperandMixin, TensorSimpleRandomData
class TensorRandn(TensorSimpleRandomData, TensorRandomOperandMixin):
_op_type_ = OperandDef.RAND_RANDN
_func_name = "randn"
def __call__(self, chunk_size=None):
return self.new_tensor(None, None, raw_chunk_size=chunk_size)
[docs]def randn(random_state, *dn, **kw):
r"""
Return a sample (or samples) from the "standard normal" distribution.
If positive, int_like or int-convertible arguments are provided,
`randn` generates an array of shape ``(d0, d1, ..., dn)``, filled
with random floats sampled from a univariate "normal" (Gaussian)
distribution of mean 0 and variance 1 (if any of the :math:`d_i` are
floats, they are first converted to integers by truncation). A single
float randomly sampled from the distribution is returned if no
argument is provided.
This is a convenience function. If you want an interface that takes a
tuple as the first argument, use `numpy.random.standard_normal` instead.
Parameters
----------
d0, d1, ..., dn : int, optional
The dimensions of the returned tensor, should be all positive.
If no argument is given a single Python float is returned.
Returns
-------
Z : Tensor or float
A ``(d0, d1, ..., dn)``-shaped array of floating-point samples from
the standard normal distribution, or a single such float if
no parameters were supplied.
See Also
--------
random.standard_normal : Similar, but takes a tuple as its argument.
Notes
-----
For random samples from :math:`N(\mu, \sigma^2)`, use:
``sigma * mt.random.randn(...) + mu``
Examples
--------
>>> import mars.tensor as mt
>>> mt.random.randn().execute()
2.1923875335537315 #random
Two-by-four tensor of samples from N(3, 6.25):
>>> (2.5 * mt.random.randn(2, 4) + 3).execute()
array([[-4.49401501, 4.00950034, -1.81814867, 7.29718677], #random
[ 0.39924804, 4.68456316, 4.99394529, 4.84057254]]) #random
"""
if len(dn) == 1 and isinstance(dn[0], (tuple, list)):
raise TypeError("'tuple' object cannot be interpreted as an integer")
if "dtype" not in kw:
kw["dtype"] = np.dtype("f8")
chunk_size = kw.pop("chunk_size", None)
seed = gen_random_seeds(1, random_state.to_numpy())[0]
op = TensorRandn(seed=seed, size=dn, **kw)
for key in op.extra_params:
if not key.startswith("_"):
raise ValueError(f"randn got unexpected key arguments {key}")
return op(chunk_size=chunk_size)