Source code for mars.tensor.random.beta

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# Licensed under the Apache License, Version 2.0 (the "License");
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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 TensorRandBeta(TensorDistribution, TensorRandomOperandMixin):
    _input_fields_ = ["a", "b"]
    _op_type_ = OperandDef.RAND_BETA

    _fields_ = "a", "b", "size"
    a = AnyField("a")
    b = AnyField("b")
    _func_name = "beta"

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

[docs]def beta(random_state, a, b, size=None, chunk_size=None, gpu=None, dtype=None): r""" Draw samples from a Beta distribution. The Beta distribution is a special case of the Dirichlet distribution, and is related to the Gamma distribution. It has the probability distribution function .. math:: f(x; a,b) = \frac{1}{B(\alpha, \beta)} x^{\alpha - 1} (1 - x)^{\beta - 1}, where the normalisation, B, is the beta function, .. math:: B(\alpha, \beta) = \int_0^1 t^{\alpha - 1} (1 - t)^{\beta - 1} dt. It is often seen in Bayesian inference and order statistics. Parameters ---------- a : float or array_like of floats Alpha, non-negative. b : float or array_like of floats Beta, non-negative. 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 ``a`` and ``b`` are both scalars. Otherwise, ``mt.broadcast(a, b).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 beta distribution. """ if dtype is None: dtype = ( np.random.RandomState() .beta(handle_array(a), handle_array(b), size=(0,)) .dtype ) size = random_state._handle_size(size) seed = gen_random_seeds(1, random_state.to_numpy())[0] op = TensorRandBeta(seed=seed, size=size, gpu=gpu, dtype=dtype) return op(a, b, chunk_size=chunk_size)