Source code for mars.tensor.random.randn

#!/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, 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)