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LinalgSpecialPurposes.h
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LinalgSpecialPurposes.h
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/*
* Copyright (c) 2016, Shogun-Toolbox e.V. <shogun-team@shogun-toolbox.org>
* 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.
*
* 3. Neither the name of the copyright holder nor the names of its
* contributors may be used to endorse or promote products derived from
* this software without specific prior written permission.
*
* 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 HOLDER OR CONTRIBUTORS BE
* LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
* CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
* SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
* INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
* CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
* ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*
* Authors: 2017 Pan Deng, 2014 Khaled Nasr
*/
#ifndef LINALG_SPECIAL_PURPOSE_H_
#define LINALG_SPECIAL_PURPOSE_H_
#include <shogun/mathematics/linalg/LinalgNamespace.h>
namespace shogun
{
namespace linalg
{
/** Applies the elementwise logistic function f(x) = 1/(1+exp(-x)) to a
* matrix
* This method returns the result in-place.
*
* @param a The input matrix
* @param result The output matrix
*/
template <typename T>
void logistic(const SGMatrix<T>& a, SGMatrix<T>& result)
{
REQUIRE(
(a.num_rows == result.num_rows), "Number of rows of matrix a "
"(%d) must match matrix "
"result (%d).\n",
a.num_rows, result.num_rows);
REQUIRE(
(a.num_cols == result.num_cols), "Number of columns of matrix "
"a (%d) must match matrix "
"result (%d).\n",
a.num_cols, result.num_cols);
infer_backend(a, result)->logistic(a, result);
}
/** Performs the operation C(i,j) = C(i,j) * A(i,j) * (1.0-A(i,j)) for
* all i and
* j
* This method returns the result in-place.
*
* @param a The input matrix
* @param result The output matrix
*/
template <typename T>
void multiply_by_logistic_derivative(
const SGMatrix<T>& a, SGMatrix<T>& result)
{
REQUIRE(
(a.num_rows == result.num_rows), "Number of rows of matrix a "
"(%d) must match matrix "
"result (%d).\n",
a.num_rows, result.num_rows);
REQUIRE(
(a.num_cols == result.num_cols), "Number of columns of matrix "
"a (%d) must match matrix "
"result (%d).\n",
a.num_cols, result.num_cols);
infer_backend(a, result)->multiply_by_logistic_derivative(
a, result);
}
/** Performs the operation C(i,j) = C(i,j) * (A(i,j)!=0) for all i and j
* This method returns the result in-place.
*
* @param a The input matrix
* @param result The output matrix
*/
template <typename T>
void multiply_by_rectified_linear_derivative(
const SGMatrix<T>& a, SGMatrix<T>& result)
{
REQUIRE(
(a.num_rows == result.num_rows), "Number of rows of matrix a "
"(%d) must match matrix "
"result (%d).\n",
a.num_rows, result.num_rows);
REQUIRE(
(a.num_cols == result.num_cols), "Number of columns of matrix "
"a (%d) must match matrix "
"result (%d).\n",
a.num_cols, result.num_cols);
infer_backend(a, result)->multiply_by_rectified_linear_derivative(
a, result);
}
/** Applies the elementwise rectified linear function f(x) = max(0,x) to
* a
* matrix
*
* @param a The input matrix
* @param result The output matrix
*/
template <typename T>
void rectified_linear(const SGMatrix<T>& a, SGMatrix<T>& result)
{
REQUIRE(
(a.num_rows == result.num_rows), "Number of rows of matrix a "
"(%d) must match matrix "
"result (%d).\n",
a.num_rows, result.num_rows);
REQUIRE(
(a.num_cols == result.num_cols), "Number of columns of matrix "
"a (%d) must match matrix "
"result (%d).\n",
a.num_cols, result.num_cols);
infer_backend(a, result)->rectified_linear(a, result);
}
/** Applies the softmax function inplace to a matrix. The softmax
* function is
* defined as \f$ f(A[i,j]) = \frac{exp(A[i,j])}{\sum_i exp(A[i,j])} \f$
* This method returns the result in-place.
*
* @param a The input matrix
*/
template <typename T>
void softmax(SGMatrix<T>& a)
{
infer_backend(a)->softmax(a);
}
/** Returns the cross entropy between P and Q. The cross entropy is
* defined as
* \f$ H(P,Q) = - \sum_{ij} P[i,j]log(Q[i,j]) \f$
*
* @param p Input matrix 1
* @param q Input matrix 2
*/
template <typename T>
T cross_entropy(const SGMatrix<T> p, const SGMatrix<T> q)
{
REQUIRE(
(p.num_rows == q.num_rows),
"Number of rows of matrix p (%d) must match matrix q (%d).\n",
p.num_rows, q.num_rows);
REQUIRE(
(p.num_cols == q.num_cols), "Number of columns of matrix p "
"(%d) must match matrix q (%d).\n",
p.num_cols, q.num_cols);
return infer_backend(p, q)->cross_entropy(p, q);
}
/** Returns the squared error between P and Q. The squared error is
* defined as
* \f$ E(P,Q) = \frac{1}{2} \sum_{ij} (P[i,j]-Q[i,j])^2 \f$
*
* @param p Input matrix 1
* @param q Input matrix 2
*/
template <typename T>
T squared_error(const SGMatrix<T> p, const SGMatrix<T> q)
{
REQUIRE(
(p.num_rows == q.num_rows),
"Number of rows of matrix p (%d) must match matrix q (%d).\n",
p.num_rows, q.num_rows);
REQUIRE(
(p.num_cols == q.num_cols), "Number of columns of matrix p "
"(%d) must match matrix q (%d).\n",
p.num_cols, q.num_cols);
return infer_backend(p, q)->squared_error(p, q);
}
}
}
#endif // LINALG_SPECIAL_PURPOSE_H_