ElementwiseSquare.h

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/*
 * Copyright (c) The Shogun Machine Learning Toolbox
 * Written (w) 2014 Soumyajit De
 * All rights reserved.
 *
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 * modification, are permitted provided that the following conditions are met:
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 *    list of conditions and the following disclaimer.
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 *    this list of conditions and the following disclaimer in the documentation
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#ifndef ELEMENTWISESQUARE_IMPL_H_
#define ELEMENTWISESQUARE_IMPL_H_

#include <shogun/lib/config.h>
#include <shogun/lib/SGMatrix.h>
#include <shogun/mathematics/linalg/internal/Block.h>
#include <shogun/io/SGIO.h>

#ifdef HAVE_EIGEN3
#include <shogun/mathematics/eigen3.h>
#endif // HAVE_EIGEN3

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

namespace shogun
{

namespace linalg
{

namespace implementation
{

template <enum Backend,class Matrix>
struct elementwise_square
{
    static Matrix compute(Matrix m);

    static Matrix compute(Block<Matrix> b);
};

#ifdef HAVE_EIGEN3

template <class Matrix>
struct elementwise_square<Backend::EIGEN3,Matrix>
{
    typedef typename Matrix::Scalar T;

    typedef SGMatrix<T> ReturnType;

    typedef Eigen::Matrix<T,Eigen::Dynamic,Eigen::Dynamic> MatrixXt;

    static SGMatrix<T> compute(SGMatrix<T> m)
    {
        SGMatrix<T> result(m.num_rows, m.num_cols);
        compute(m, result);
        return result;
    }

    static SGMatrix<T> compute(Block<SGMatrix<T> > b)
    {
        SGMatrix<T> result(b.m_row_size, b.m_col_size);
        compute(b, result);
        return result;
    }

    static void compute(SGMatrix<T> mat, SGMatrix<T> result)
    {
        Eigen::Map<MatrixXt> m = mat;
        Eigen::Map<MatrixXt> r = result;

        r = m.array().template square();
    }

    static void compute(Block<SGMatrix<T> > b, SGMatrix<T> result)
    {
        Eigen::Map<MatrixXt> map = b.m_matrix;
        Eigen::Map<MatrixXt> r = result;

        Eigen::Block< Eigen::Map<MatrixXt> > m = map.block(
            b.m_row_begin, b.m_col_begin,
            b.m_row_size, b.m_col_size);

        r = m.array().template square();
    }
};

#endif // HAVE_EIGEN3

#ifdef HAVE_VIENNACL

template <class Matrix>
struct elementwise_square<Backend::VIENNACL,Matrix>
{
    typedef typename Matrix::Scalar T;

    typedef CGPUMatrix<T> ReturnType;

    static CGPUMatrix<T> compute(CGPUMatrix<T> m)
    {
        CGPUMatrix<T> result(m.num_rows, m.num_cols);
        compute(m, result);
        return result;
    }

    static CGPUMatrix<T> compute(Block<CGPUMatrix<T> > b)
    {
        SG_SERROR("The operation elementwise_square() on a matrix block is currently not supported\n");
        return CGPUMatrix<T>();
    }

    static void compute(CGPUMatrix<T> mat, CGPUMatrix<T> result)
    {
        const std::string operation = "return element*element;";

        std::string kernel_name = "elementwise_square_" + ocl::get_type_string<T>();
        viennacl::ocl::kernel& kernel =
            ocl::generate_single_arg_elementwise_kernel<T>(kernel_name, operation);

        kernel.global_work_size(0, ocl::align_to_multiple_1d(mat.num_rows*mat.num_cols));

        viennacl::ocl::enqueue(kernel(mat.vcl_matrix(),
            cl_int(mat.num_rows*mat.num_cols), cl_int(mat.offset),
            result.vcl_matrix(), cl_int(result.offset)));
    }

    static void compute(Block<SGMatrix<T> > b, SGMatrix<T> result)
    {
        SG_SERROR("The operation elementwise_square() on a matrix block is currently not supported\n");
    }
};

#endif // HAVE_VIENNACL

}

}

}
#endif // ELEMENTWISESQUARE_IMPL_H_