Source code for mars.tensor.random.standard_normal

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

import numpy as np

from ... import opcodes as OperandDef
from ..utils import gen_random_seeds
from .core import TensorRandomOperandMixin, TensorDistribution

class TensorStandardNormal(TensorDistribution, TensorRandomOperandMixin):
    _op_type_ = OperandDef.RAND_STANDARD_NORMAL
    _func_name = "standard_normal"
    _fields_ = ("size",)

    def __call__(self, chunk_size=None):
        return self.new_tensor(None, None, raw_chunk_size=chunk_size)

[docs]def standard_normal(random_state, size=None, chunk_size=None, gpu=None, dtype=None): """ Draw samples from a standard Normal distribution (mean=0, stdev=1). Parameters ---------- size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. Default is None, in which case a single value is returned. chunk_size : int or tuple of int or tuple of ints, optional Desired chunk size on each dimension gpu : bool, optional Allocate the tensor on GPU if True, False as default dtype : data-type, optional Data-type of the returned tensor. Returns ------- out : float or Tensor Drawn samples. Examples -------- >>> import mars.tensor as mt >>> s = mt.random.standard_normal(8000) >>> s.execute() array([ 0.6888893 , 0.78096262, -0.89086505, ..., 0.49876311, #random -0.38672696, -0.4685006 ]) #random >>> s.shape (8000,) >>> s = mt.random.standard_normal(size=(3, 4, 2)) >>> s.shape (3, 4, 2) """ if dtype is None: dtype = np.random.RandomState().standard_normal(size=(0,)).dtype size = random_state._handle_size(size) seed = gen_random_seeds(1, random_state.to_numpy())[0] op = TensorStandardNormal(size=size, seed=seed, gpu=gpu, dtype=dtype) return op(chunk_size=chunk_size)