/usr/include/torch/SVMRegression.h is in libtorch3-dev 3.1-2.1build1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 | // Copyright (C) 2003--2004 Ronan Collobert (collober@idiap.ch)
//
// This file is part of Torch 3.1.
//
// 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. The name of the author may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``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 AUTHOR 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.
#ifndef SVM_REGRESSION_INC
#define SVM_REGRESSION_INC
#include "SVM.h"
namespace Torch {
/** SVM in regression.
Try to find the hyperplane f(x) = w.x+b
as
$(w,b)$ minimize $0.5*||w||^2 + \sum_j C_j |w.x_j+b -y_j -eps|_+$
$+ \sum_j C_(j+n) |y_j -w.x_j-b -eps|_+$
(where $|x|_+ = x$ if $x > 0$, else $0$)
(and $n$ is the number of training examples)
(the size of $C$ is here 2*$n$)
("eps" is #eps_regression# in the code)
(in fact, we use a kernel #kernel# instead of
a dot product)
The $C_j$ coefficients are given by #C_# when you
call the constructor. If this one is NULL, all
#C_j# will have the value given by the "C" option.
(The size of #C_# \emph{must be} #2*data->n_real_examples#)
Options:
\begin{tabular}{lcll}
"C" & real & trade off between the weight decay and the error & [100] \\
"eps regression" & real & size of the error tube & [0.7] \\
"cache size" & real & cache size (in Mo) & [50]
\end{tabular}
@author Ronan Collobert (collober@idiap.ch)
*/
class SVMRegression : public SVM
{
private:
char *sequences_buffer;
char *frames_buffer;
public:
real cache_size_in_megs;
real eps_regression;
real *Cuser;
real C_cst;
//-----
///
SVMRegression(Kernel *kernel_, real *C_=NULL, IOSequenceArray *io_sequence_array_=NULL);
//-----
virtual void setDataSet(DataSet *dataset_);
virtual void checkSupportVectors();
virtual ~SVMRegression();
};
}
#endif
|