Source code for xorbits._mars.tensor.random.rand
# Copyright 2022-2023 XProbe Inc.
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#
# 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
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# 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 TensorRand(TensorSimpleRandomData, TensorRandomOperandMixin):
_op_type_ = OperandDef.RAND_RAND
_func_name = "rand"
def __call__(self, chunk_size=None):
return self.new_tensor(None, None, raw_chunk_size=chunk_size)
[docs]def rand(random_state, *dn, **kw):
"""
Random values in a given shape.
Create a tensor of the given shape and populate it with
random samples from a uniform distributionc
over ``[0, 1)``.
Parameters
----------
d0, d1, ..., dn : int, optional
The dimensions of the returned tensor, should all be positive.
If no argument is given a single Python float is returned.
Returns
-------
out : Tensor, shape ``(d0, d1, ..., dn)``
Random values.
See Also
--------
random
Notes
-----
This is a convenience function. If you want an interface that
takes a shape-tuple as the first argument, refer to
mt.random.random_sample .
Examples
--------
>>> import mars.tensor as mt
>>> mt.random.rand(3, 2).execute()
array([[ 0.14022471, 0.96360618], #random
[ 0.37601032, 0.25528411], #random
[ 0.49313049, 0.94909878]]) #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 = TensorRand(seed=seed, size=dn, **kw)
for key in op.extra_params:
if not key.startswith("_"):
raise ValueError(f"rand got unexpected key arguments {key}")
return op(chunk_size=chunk_size)