Source code for mars.tensor.random.rayleigh

#!/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 ...serialization.serializables import AnyField
from ..utils import gen_random_seeds
from .core import TensorRandomOperandMixin, handle_array, TensorDistribution

class TensorRayleigh(TensorDistribution, TensorRandomOperandMixin):
    _input_fields_ = ["scale"]
    _op_type_ = OperandDef.RAND_RAYLEIGH

    _fields_ = "scale", "size"
    scale = AnyField("scale")
    _func_name = "rayleigh"

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

[docs]def rayleigh(random_state, scale=1.0, size=None, chunk_size=None, gpu=None, dtype=None): r""" Draw samples from a Rayleigh distribution. The :math:`\chi` and Weibull distributions are generalizations of the Rayleigh. Parameters ---------- scale : float or array_like of floats, optional Scale, also equals the mode. Should be >= 0. Default is 1. 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. If size is ``None`` (default), a single value is returned if ``scale`` is a scalar. Otherwise, ``mt.array(scale).size`` samples are drawn. 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 : Tensor or scalar Drawn samples from the parameterized Rayleigh distribution. Notes ----- The probability density function for the Rayleigh distribution is .. math:: P(x;scale) = \frac{x}{scale^2}e^{\frac{-x^2}{2 \cdotp scale^2}} The Rayleigh distribution would arise, for example, if the East and North components of the wind velocity had identical zero-mean Gaussian distributions. Then the wind speed would have a Rayleigh distribution. References ---------- .. [1] Brighton Webs Ltd., "Rayleigh Distribution," .. [2] Wikipedia, "Rayleigh distribution" Examples -------- Draw values from the distribution and plot the histogram >>> import matplotlib.pyplot as plt >>> import mars.tensor as mt >>> values = plt.hist(mt.random.rayleigh(3, 100000).execute(), bins=200, normed=True) Wave heights tend to follow a Rayleigh distribution. If the mean wave height is 1 meter, what fraction of waves are likely to be larger than 3 meters? >>> meanvalue = 1 >>> modevalue = mt.sqrt(2 / mt.pi) * meanvalue >>> s = mt.random.rayleigh(modevalue, 1000000) The percentage of waves larger than 3 meters is: >>> (100.*mt.sum(s>3)/1000000.).execute() 0.087300000000000003 """ if dtype is None: dtype = np.random.RandomState().rayleigh(handle_array(scale), size=(0,)).dtype size = random_state._handle_size(size) seed = gen_random_seeds(1, random_state.to_numpy())[0] op = TensorRayleigh(size=size, seed=seed, gpu=gpu, dtype=dtype) return op(scale, chunk_size=chunk_size)