Source code for mars.tensor.random.geometric

#!/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
# 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 ...serialize import AnyField
from .core import TensorRandomOperandMixin, handle_array, TensorDistribution

class TensorGeometric(TensorDistribution, TensorRandomOperandMixin):
    __slots__ = '_p', '_size'
    _input_fields_ = ['_p']
    _op_type_ = OperandDef.RAND_GEOMETRIC

    _p = AnyField('p')
    _func_name = 'geometric'

    def p(self):
        return self._p

    def __init__(self, state=None, size=None, dtype=None, gpu=None, **kw):
        dtype = np.dtype(dtype) if dtype is not None else dtype
        super().__init__(_state=state, _size=size, _dtype=dtype, _gpu=gpu, **kw)

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

[docs]def geometric(random_state, p, size=None, chunk_size=None, gpu=None, dtype=None): """ Draw samples from the geometric distribution. Bernoulli trials are experiments with one of two outcomes: success or failure (an example of such an experiment is flipping a coin). The geometric distribution models the number of trials that must be run in order to achieve success. It is therefore supported on the positive integers, ``k = 1, 2, ...``. The probability mass function of the geometric distribution is .. math:: f(k) = (1 - p)^{k - 1} p where `p` is the probability of success of an individual trial. Parameters ---------- p : float or array_like of floats The probability of success of an individual trial. 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 ``p`` is a scalar. Otherwise, ``mt.array(p).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 geometric distribution. Examples -------- Draw ten thousand values from the geometric distribution, with the probability of an individual success equal to 0.35: >>> import mars.tensor as mt >>> z = mt.random.geometric(p=0.35, size=10000) How many trials succeeded after a single run? >>> ((z == 1).sum() / 10000.).execute() 0.34889999999999999 #random """ if dtype is None: dtype = np.random.RandomState().geometric( handle_array(p), size=(0,)).dtype size = random_state._handle_size(size) op = TensorGeometric(state=random_state.to_numpy(), size=size, gpu=gpu, dtype=dtype) return op(p, chunk_size=chunk_size)