/* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northestern University.
* 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.
*/

/*
* $Created by: XIAO Tong (email: xiaotong@mail.neu.edu.cn) 2018-04-24
*/

#ifndef __REDUCEVARIANCE_H__
#define __REDUCEVARIANCE_H__

#include "../../XTensor.h"

namespace nts{ // namespace nts(NiuTrans.Tensor)

/* 
variance of the items along a dimension of the tensor
For a 1-dimensional data array a, variance = 1/n * \sum_i (a_i - mean)^2
*/
void _ReduceVariance(const XTensor * input, XTensor * output, int dim, const XTensor * mean);

/* 
variance of the items along a dimension of the tensor (return an XTensor structure)
make a new tensor to keep the result and return it
For a 1-dimensional data array a, variance = 1/n * \sum_i (a_i - mean)^2
*/
XTensor ReduceVariance(const XTensor &input, int dim, const XTensor &mean);

/* 
variance of the items along a dimension of the tensor
For a 1-dimensional data array a, variance = 1/n * \sum_i (a_i - mean)^2
*/
void ReduceVariance(const XTensor &input, XTensor &output, int dim, const XTensor &mean, bool requireLink = false);

} // namespace nts(NiuTrans.Tensor)

#endif // __REDUCEVARIANCE_H__