# 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 TensorBeta(TensorDistribution, TensorRandomOperandMixin): __slots__ = '_a', '_b', '_size' _input_fields_ = ['_a', '_b'] _op_type_ = OperandDef.RAND_BETA _a = AnyField('a') _b = AnyField('b') _func_name = 'beta' 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) @property def a(self): return self._a @property def b(self): return self._b 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) op = TensorBeta(state=random_state.to_numpy(), size=size, gpu=gpu, dtype=dtype) return op(a, b, chunk_size=chunk_size)