Source code for mars.tensor.random.poisson

#!/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 TensorPoisson(TensorDistribution, TensorRandomOperandMixin):
    _input_fields_ = ["lam"]
    _op_type_ = OperandDef.RAND_POSSION

    _fields_ = "lam", "size"
    lam = AnyField("lam")
    _func_name = "poisson"

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

[docs]def poisson(random_state, lam=1.0, size=None, chunk_size=None, gpu=None, dtype=None): r""" Draw samples from a Poisson distribution. The Poisson distribution is the limit of the binomial distribution for large N. Parameters ---------- lam : float or array_like of floats Expectation of interval, should be >= 0. A sequence of expectation intervals must be broadcastable over the requested size. 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 ``lam`` is a scalar. Otherwise, ``mt.array(lam).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 Poisson distribution. Notes ----- The Poisson distribution .. math:: f(k; \lambda)=\frac{\lambda^k e^{-\lambda}}{k!} For events with an expected separation :math:`\lambda` the Poisson distribution :math:`f(k; \lambda)` describes the probability of :math:`k` events occurring within the observed interval :math:`\lambda`. Because the output is limited to the range of the C long type, a ValueError is raised when `lam` is within 10 sigma of the maximum representable value. References ---------- .. [1] Weisstein, Eric W. "Poisson Distribution." From MathWorld--A Wolfram Web Resource. .. [2] Wikipedia, "Poisson distribution", Examples -------- Draw samples from the distribution: >>> import mars.tensor as mt >>> s = mt.random.poisson(5, 10000) Display histogram of the sample: >>> import matplotlib.pyplot as plt >>> count, bins, ignored = plt.hist(s.execute(), 14, normed=True) >>> Draw each 100 values for lambda 100 and 500: >>> s = mt.random.poisson(lam=(100., 500.), size=(100, 2)) """ if dtype is None: dtype = np.random.RandomState().poisson(handle_array(lam), size=(0,)).dtype size = random_state._handle_size(size) seed = gen_random_seeds(1, random_state.to_numpy())[0] op = TensorPoisson(size=size, seed=seed, gpu=gpu, dtype=dtype) return op(lam, chunk_size=chunk_size)