/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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==============================================================================*/
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#ifndef TENSORFLOW_CORE_KERNELS_PARAMETERIZED_TRUNCATED_NORMAL_OP_H_
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#define TENSORFLOW_CORE_KERNELS_PARAMETERIZED_TRUNCATED_NORMAL_OP_H_
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#include "tensorflow/core/framework/tensor_types.h"
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#include "tensorflow/core/lib/random/random_distributions.h"
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namespace tensorflow {
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class OpKernelContext;
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namespace functor {
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// Sample a truncated normal random variable, with mean, stddev, minval, and
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// maxval parameters for each batch. Uses two rejection sampling algorithms
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// described in http://rd.springer.com/article/10.1007/BF00143942 and a randn
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// rejection sampler when most of the normal is inside the bounds.
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//
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// Either minval may be -infinity, or maxval may be +infinity. If the interval
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// (minval, maxval) is empty, the result is NaN.
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template <typename Device, typename T>
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struct TruncatedNormalFunctor {
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void operator()(OpKernelContext* ctx, const Device& d, int64 num_batches,
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int64 samples_per_batch, int64 num_elements,
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typename TTypes<T>::ConstFlat means,
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typename TTypes<T>::ConstFlat stddevs,
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typename TTypes<T>::ConstFlat minvals,
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typename TTypes<T>::ConstFlat maxvals,
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const random::PhiloxRandom& gen,
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typename TTypes<T>::Flat output);
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};
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} // namespace functor
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} // namespace tensorflow
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#endif // TENSORFLOW_CORE_KERNELS_PARAMETERIZED_TRUNCATED_NORMAL_OP_H_
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