#!/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 ...serialize import AnyField from .core import TensorRandomOperandMixin, handle_array, TensorDistribution class TensorExponential(TensorDistribution, TensorRandomOperandMixin): __slots__ = '_scale', '_size' _input_fields_ = ['_scale'] _op_type_ = OperandDef.RAND_EXPONENTIAL _scale = AnyField('scale') _func_name = 'exponential' @property def scale(self): return self._scale 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, scale, chunk_size=None): return self.new_tensor([scale], self._size, raw_chunk_size=chunk_size) [docs]def exponential(random_state, scale=1.0, size=None, chunk_size=None, gpu=None, dtype=None): r""" Draw samples from an exponential distribution. Its probability density function is .. math:: f(x; \frac{1}{\beta}) = \frac{1}{\beta} \exp(-\frac{x}{\beta}), for ``x > 0`` and 0 elsewhere. :math:`\beta` is the scale parameter, which is the inverse of the rate parameter :math:`\lambda = 1/\beta`. The rate parameter is an alternative, widely used parameterization of the exponential distribution [3]_. The exponential distribution is a continuous analogue of the geometric distribution. It describes many common situations, such as the size of raindrops measured over many rainstorms [1]_, or the time between page requests to Wikipedia [2]_. Parameters ---------- scale : float or array_like of floats The scale parameter, :math:`\beta = 1/\lambda`. 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, ``np.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 exponential distribution. References ---------- .. [1] Peyton Z. Peebles Jr., "Probability, Random Variables and Random Signal Principles", 4th ed, 2001, p. 57. .. [2] Wikipedia, "Poisson process", http://en.wikipedia.org/wiki/Poisson_process .. [3] Wikipedia, "Exponential distribution", http://en.wikipedia.org/wiki/Exponential_distribution """ if dtype is None: dtype = np.random.RandomState().exponential( handle_array(scale), size=(0,)).dtype size = random_state._handle_size(size) op = TensorExponential(state=random_state.to_numpy(), size=size, gpu=gpu, dtype=dtype) return op(scale, chunk_size=chunk_size)