【Hackathon 6th No.5】Add chi2/LKJCholesky API to Paddle -part

python/paddle/distribution/__init__.py

@@ -18,6 +18,7 @@
 from .binomial import Binomial
 from .categorical import Categorical
 from .cauchy import Cauchy
+from .chi2 import Chi2
 from .continuous_bernoulli import ContinuousBernoulli
 from .dirichlet import Dirichlet
 from .distribution import Distribution
@@ -29,6 +30,7 @@
 from .independent import Independent
 from .kl import kl_divergence, register_kl
 from .laplace import Laplace
+from .lkj_cholesky import LKJCholesky
 from .lognormal import LogNormal
 from .multinomial import Multinomial
 from .multivariate_normal import MultivariateNormal
@@ -58,6 +60,7 @@
     'Beta',
     'Categorical',
     'Cauchy',
+    'Chi2',
     'ContinuousBernoulli',
     'Dirichlet',
     'Distribution',
@@ -73,6 +76,7 @@
     'TransformedDistribution',
     'Laplace',
     'LogNormal',
+    'LKJCholesky',
     'Gamma',
     'Gumbel',
     'Geometric',

python/paddle/distribution/chi2.py

@@ -0,0 +1,64 @@
+# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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 paddle
+from paddle.base.data_feeder import check_type, convert_dtype
+from paddle.base.framework import Variable
+from paddle.distribution.gamma import Gamma
+from paddle.framework import in_dynamic_mode
+
+__all__ = ["Chi2"]
+
+
+class Chi2(Gamma):
+    r"""
+    Creates a Chi-squared distribution parameterized by shape parameter.
+    This is exactly equivalent to Gamma(concentration=0.5*df, rate=0.5), :ref:`api_paddle_distribution_Gamma`.
+
+    Args:
+        df (float or Tensor): shape parameter of the distribution
+
+    Example:
+        .. code-block:: python
+
+            >>> import paddle
+            >>> m = paddle.distribution.Chi2(paddle.to_tensor([1.0]))
+            >>> sample = m.sample()
+            >>> sample.shape
+            [1]
+
+    """
+
+    def __init__(self, df):
+        if not in_dynamic_mode():
+            check_type(
+                df,
+                'df',
+                (float, Variable),
+                'Chi2',
+            )
+
+        # Get/convert concentration to tensor.
+        if self._validate_args(df):
+            self.df = df
+            self.dtype = convert_dtype(df.dtype)
+        else:
+            [self.df] = self._to_tensor(df)
+            self.dtype = paddle.get_default_dtype()
+
+        self.rate = paddle.full_like(self.df, 0.5)
+
+        if not paddle.all(self.df > 0):
+            raise ValueError("The arg of `df` must be positive.")
+
+        super().__init__(self.df * 0.5, self.rate)