svmregression.h

强化学习算法（R-Learning）难得的珍贵资料

// Copyright (C) 2003 Ronan Collobert ([e-mail])
//                
// This file is part of Torch 3.
//
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// modification, are permitted provided that the following conditions
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#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 ([e-mail])
*/
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