#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright 1999-2020 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 # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import numpy as np from ... import opcodes as OperandDef from .core import TensorRandomOperandMixin, TensorDistribution class TensorStandardNormal(TensorDistribution, TensorRandomOperandMixin): __slots__ = '_size', _op_type_ = OperandDef.RAND_STANDARD_NORMAL _func_name = 'standard_normal' def __init__(self, size=None, state=None, dtype=None, gpu=None, **kw): dtype = np.dtype(dtype) if dtype is not None else dtype super().__init__(_size=size, _state=state, _dtype=dtype, _gpu=gpu, **kw) 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) op = TensorStandardNormal(size=size, state=random_state.to_numpy(), gpu=gpu, dtype=dtype) return op(chunk_size=chunk_size)