Redux.h

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/*
 * Copyright (c) The Shogun Machine Learning Toolbox
 * Written (w) 2014 Soumyajit De
 * Written (w) 2014 Khaled Nasr
 * All rights reserved.
 *
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions are met:
 *
 * 1. Redistributions of source code must retain the above copyright notice, this
 *    list of conditions and the following disclaimer.
 * 2. Redistributions in binary form must reproduce the above copyright notice,
 *    this list of conditions and the following disclaimer in the documentation
 *    and/or other materials provided with the distribution.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
 * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
 * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
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 * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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 * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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 */

#ifndef REDUX_H_
#define REDUX_H_

#include <shogun/mathematics/linalg/internal/implementation/Dot.h>
#include <shogun/mathematics/linalg/internal/implementation/Sum.h>
#include <shogun/mathematics/linalg/internal/implementation/VectorSum.h>
#include <shogun/mathematics/linalg/internal/implementation/Max.h>
#include <shogun/mathematics/linalg/internal/implementation/MeanEigen3.h>
#include <shogun/mathematics/linalg/internal/implementation/Cholesky.h>

namespace shogun
{

namespace linalg
{

template <Backend backend=linalg_traits<Redux>::backend, class Vector>
typename Vector::Scalar dot(Vector a, Vector b)
{
    return implementation::dot<backend,Vector>::compute(a, b);
}

template <Backend backend=linalg_traits<Redux>::backend, class Matrix>
typename Matrix::Scalar max(Matrix m)
{
    return implementation::max<backend,Matrix>::compute(m);
}

template <Backend backend=linalg_traits<Redux>::backend, class Vector>
typename Vector::Scalar vector_sum(Vector a)
{
    return implementation::vector_sum<backend,Vector>::compute(a);
}

#ifdef HAVE_LINALG_LIB

template <Backend backend=linalg_traits<Redux>::backend, class Matrix>
typename Matrix::Scalar sum(Matrix m, bool no_diag=false)
{
    return implementation::sum<backend,Matrix>::compute(m, no_diag);
}

template <Backend backend=linalg_traits<Redux>::backend,class Matrix>
typename Matrix::Scalar sum_symmetric(Matrix m, bool no_diag=false)
{
    return implementation::sum_symmetric<backend,Matrix>::compute(m, no_diag);
}

template <Backend backend=linalg_traits<Redux>::backend,class Matrix>
typename Matrix::Scalar sum_symmetric(Block<Matrix> b, bool no_diag=false)
{
    return implementation::sum_symmetric<backend,Matrix>
		::compute(b, no_diag);
}

template <Backend backend=linalg_traits<Redux>::backend,class Matrix>
typename implementation::colwise_sum<backend,Matrix>::ReturnType colwise_sum(
    Matrix m, bool no_diag=false)
{
    return implementation::colwise_sum<backend,Matrix>::compute(m, no_diag);
}

template <Backend backend=linalg_traits<Redux>::backend,class Matrix, class Vector>
void colwise_sum(Matrix m, Vector result, bool no_diag=false)
{
    implementation::colwise_sum<backend,Matrix>::compute(m, result, no_diag);
}

template <Backend backend=linalg_traits<Redux>::backend,class Matrix>
typename implementation::rowwise_sum<backend,Matrix>::ReturnType rowwise_sum(
    Matrix m, bool no_diag=false)
{
    return implementation::rowwise_sum<backend,Matrix>::compute(m, no_diag);
}

template <Backend backend=linalg_traits<Redux>::backend, class Matrix, class Vector>
void rowwise_sum(Matrix m, Vector result, bool no_diag=false)
{
    implementation::rowwise_sum<backend,Matrix>::compute(m, result, no_diag);
}

template <Backend backend=linalg_traits<Redux>::backend, class Vector>
typename implementation::int2float<typename Vector::Scalar>::floatType mean(Vector a)
{
    return implementation::mean<backend,Vector>::compute(a);
}

template <Backend backend=linalg_traits<Redux>::backend, class Matrix>
typename implementation::int2float<typename Matrix::Scalar>::floatType mean(
        Matrix m, bool no_diag)
{
    return implementation::mean<backend,Matrix>::compute(m, no_diag);
}

template <Backend backend=linalg_traits<Redux>::backend,class Matrix>
SGVector<typename implementation::rowwise_mean<backend,Matrix>::ReturnDataType> 
    rowwise_mean(Matrix m, bool no_diag=false)
{
    return implementation::rowwise_mean<backend,Matrix>::compute(m, no_diag);
}

template <Backend backend=linalg_traits<Redux>::backend, class Matrix>
typename implementation::cholesky<backend,Matrix>::ReturnType cholesky(Matrix m, bool lower=true)
{
    return implementation::cholesky<backend,Matrix>::compute(m, lower);
}

#endif // HAVE_LINALG_LIB

}

}
#endif // REDUX_H_