wehub-resource-sync
78 lines
2.5 KiB
Python
78 lines
2.5 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import annotations
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from typing import TYPE_CHECKING
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import paddle
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from paddle.base.data_feeder import check_type, convert_dtype
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from paddle.base.framework import Variable
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from paddle.distribution.gamma import Gamma
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from paddle.framework import in_dynamic_mode
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if TYPE_CHECKING:
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from paddle import Tensor, dtype
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__all__ = ["Chi2"]
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class Chi2(Gamma):
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r"""
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Creates a Chi-squared distribution parameterized by shape parameter.
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This is exactly equivalent to Gamma(concentration=0.5*df, rate=0.5), :ref:`api_paddle_distribution_Gamma`.
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Args:
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df (float or Tensor): The degree of freedom of the distribution, which should be non-negative. If the input data type is Tensor, it indicates the batch creation of distributions with multiple different parameters, and the `batch_shape` (refer to the :ref:`api_paddle_distribution_Distribution` base class) is the parameter.
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Example:
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.. code-block:: pycon
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>>> import paddle
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>>> m = paddle.distribution.Chi2(paddle.to_tensor([1.0]))
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>>> sample = m.sample()
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>>> sample.shape
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paddle.Size([1])
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"""
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df: Tensor
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rate: Tensor
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dtype: dtype
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def __init__(self, df: float | Tensor) -> None:
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if not in_dynamic_mode():
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check_type(
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df,
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'df',
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(float, Variable, paddle.pir.Value),
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'Chi2',
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)
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# Get/convert concentration to tensor.
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if self._validate_args(df):
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self.df = df
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self.dtype = convert_dtype(df.dtype)
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else:
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[self.df] = self._to_tensor(df)
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self.dtype = paddle.get_default_dtype()
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self.rate = paddle.full_like(self.df, 0.5)
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if in_dynamic_mode():
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if not paddle.all(self.df > 0):
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raise ValueError("The arg of `df` must be positive.")
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super().__init__(self.df * 0.5, self.rate)
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