Source code for mars.tensor.random.triangular

#!/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 TensorTriangular(TensorDistribution, TensorRandomOperandMixin):
    _input_fields_ = ["left", "mode", "right"]
    _op_type_ = OperandDef.RAND_TRIANGULAR

    _fields_ = "left", "mode", "right", "size"
    left = AnyField("left")
    mode = AnyField("mode")
    right = AnyField("right")
    _func_name = "triangular"

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

[docs]def triangular( random_state, left, mode, right, size=None, chunk_size=None, gpu=None, dtype=None ): r""" Draw samples from the triangular distribution over the interval ``[left, right]``. The triangular distribution is a continuous probability distribution with lower limit left, peak at mode, and upper limit right. Unlike the other distributions, these parameters directly define the shape of the pdf. Parameters ---------- left : float or array_like of floats Lower limit. mode : float or array_like of floats The value where the peak of the distribution occurs. The value should fulfill the condition ``left <= mode <= right``. right : float or array_like of floats Upper limit, should be larger than `left`. 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 ``left``, ``mode``, and ``right`` are all scalars. Otherwise, ``mt.broadcast(left, mode, right).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 triangular distribution. Notes ----- The probability density function for the triangular distribution is .. math:: P(x;l, m, r) = \begin{cases} \frac{2(x-l)}{(r-l)(m-l)}& \text{for $l \leq x \leq m$},\\ \frac{2(r-x)}{(r-l)(r-m)}& \text{for $m \leq x \leq r$},\\ 0& \text{otherwise}. \end{cases} The triangular distribution is often used in ill-defined problems where the underlying distribution is not known, but some knowledge of the limits and mode exists. Often it is used in simulations. References ---------- .. [1] Wikipedia, "Triangular distribution" Examples -------- Draw values from the distribution and plot the histogram: >>> import matplotlib.pyplot as plt >>> import mars.tensor as mt >>> h = plt.hist(mt.random.triangular(-3, 0, 8, 100000).execute(), bins=200, ... normed=True) >>> """ if dtype is None: dtype = ( np.random.RandomState() .triangular( handle_array(left), handle_array(mode), handle_array(right), size=(0,) ) .dtype ) size = random_state._handle_size(size) seed = gen_random_seeds(1, random_state.to_numpy())[0] op = TensorTriangular(size=size, seed=seed, gpu=gpu, dtype=dtype) return op(left, mode, right, chunk_size=chunk_size)