VectorSum.h

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/*
 * Copyright (c) The Shogun Machine Learning Toolbox
 * Written (w) 2014 Soumyajit De
 * 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
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 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
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#ifndef VECTOR_SUM_IMPL_H_
#define VECTOR_SUM_IMPL_H_

#include <shogun/lib/config.h>
#include <shogun/lib/SGVector.h>

#ifdef HAVE_VIENNACL
#include <shogun/mathematics/linalg/internal/implementation/Sum.h>
#include <shogun/lib/GPUVector.h>
#include <shogun/lib/GPUMatrix.h>
#endif

#include <shogun/mathematics/eigen3.h>

#include <numeric>

namespace shogun
{

namespace linalg
{

namespace implementation
{

template <enum Backend, class Vector>
struct vector_sum
{
    typedef typename Vector::Scalar T;

    static T compute(Vector a);
};

template <class Vector>
struct vector_sum<Backend::EIGEN3, Vector>
{
    typedef typename Vector::Scalar T;

    static T compute(SGVector<T> vec)
    {
        typedef Eigen::Matrix<T, Eigen::Dynamic, 1> VectorXt;
        Eigen::Map<VectorXt> v = vec;
        return v.sum();
    }
};


#ifdef HAVE_VIENNACL

template <class Vector>
struct vector_sum<Backend::VIENNACL, Vector>
{
    typedef typename Vector::Scalar T;

    static T compute(CGPUVector<T> vec)
    {
        CGPUMatrix<T> m(vec.vector, vec.vlen, 1, vec.offset);
        return sum<Backend::VIENNACL, CGPUMatrix<T> >::compute(m, false);
    }
};

#endif // HAVE_VIENNACL

}

}

}
#endif // VECTOR_SUM_IMPL_H_