SVMClassifier Class Reference

#include <svm_classifier.h>

Collaboration diagram for SVMClassifier:

Public Member Functions
	SVMClassifier ()=default
	SVMClassifier (const SVMClassifier &other)
	~SVMClassifier ()
SVMClassifier &	operator= (const SVMClassifier &other)
void	SVMTrain (MultidimArray< double > &trainSet, MultidimArray< double > &lable)
double	predict (MultidimArray< double > &featVec, double &score)
void	SaveModel (const FileName &fnModel)
void	LoadModel (const FileName &fnModel)
void	setParameters (double c, double gamma)

Public Attributes
svm_parameter	param
svm_problem	prob
svm_model *	model =nullptr

Detailed Description

SVM classifier class. This class use the SVMLIB Library in order to classify the data using the svm method

Definition at line 45 of file svm_classifier.h.

Constructor & Destructor Documentation

SVMClassifier() [1/2]

SVMClassifier::SVMClassifier ( )

default

SVMClassifier() [2/2]

SVMClassifier::SVMClassifier ( const SVMClassifier &  other )

inline

Definition at line 54 of file svm_classifier.h.

    {
        *this=other;
    }

~SVMClassifier()

SVMClassifier::~SVMClassifier

(

)

Definition at line 70 of file svm_classifier.cpp.

{
    svm_free_and_destroy_model(&model);
    svm_destroy_param(&param);
    if (prob.y!=NULL)
        delete [] prob.y;
    if (prob.x!=NULL)
    {
        for(int i=0;i<prob.l;i++)
            delete [] prob.x[i];
        delete [] prob.x;
    }
}

Member Function Documentation

LoadModel()

void SVMClassifier::LoadModel

(

const FileName &

fnModel

)

Definition at line 153 of file svm_classifier.cpp.

{
    model=svm_load_model(fnModel.c_str());
}

operator=()

SVMClassifier & SVMClassifier::operator=

(

const SVMClassifier &

other

)

Definition at line 61 of file svm_classifier.cpp.

{
    param=other.param;
    prob=other.prob;
    delete model;
    model=new svm_model(*other.model);
    return *this;
}

predict()

double SVMClassifier::predict	(	MultidimArray< double > &	featVec,
		double &	score
	)

Definition at line 118 of file svm_classifier.cpp.

{
    svm_node *x_space;
    int cnt=0;
    int nr_class=svm_get_nr_class(model);
    double *prob_estimates=new double[nr_class];
    x_space=new svm_node[XSIZE(featVec)+1];

    for (size_t i=0;i<XSIZE(featVec);i++)
    {
        if (DIRECT_A1D_ELEM(featVec,i)==0)
            continue;
        else
        {
            x_space[cnt].value=DIRECT_A1D_ELEM(featVec,i);
            x_space[cnt].index=i+1;
            cnt++;
        }
    }
    x_space[cnt].index=-1;
    double label=svm_predict_probability(model,x_space,prob_estimates);
    // Extracting the probability of the selected class
    score=prob_estimates[0];
    for (int i=1;i<nr_class;++i)
        if (prob_estimates[i]>score)
            score=prob_estimates[i];
    delete [] prob_estimates;
    delete [] x_space;
    return label;
}

SaveModel()

void SVMClassifier::SaveModel

(

const FileName &

fnModel

)

Definition at line 148 of file svm_classifier.cpp.

{
    if (model->l!=0)
        svm_save_model(fnModel.c_str(),model);
}

setParameters()

void SVMClassifier::setParameters	(	double	c,
		double	gamma
	)

Definition at line 39 of file svm_classifier.cpp.

{
    param.svm_type = libsvm::C_SVC;
    param.kernel_type = libsvm::RBF;
    param.degree = 2;
    param.gamma = gamma;
    param.coef0 = 0;
    param.nu = 0.1;
    param.cache_size = 1000;
    param.C = c;
    param.eps = 0.001;
    param.p = 0.1;
    param.shrinking = 1;
    param.probability = 1;
    param.nr_weight = 0;
    param.weight_label = NULL;
    param.weight = NULL;
    model=NULL;
    prob.y=NULL;
    prob.x=NULL;
}

SVMTrain()

void SVMClassifier::SVMTrain	(	MultidimArray< double > &	trainSet,
		MultidimArray< double > &	lable
	)

Definition at line 84 of file svm_classifier.cpp.

{

    prob.l = YSIZE(trainSet);
    prob.y = new double[prob.l];
    prob.x = new svm_node *[prob.l+1];
    const char *error_msg;
    for (size_t i=0;i<YSIZE(trainSet);i++)
    {
        prob.x[i]=new svm_node[XSIZE(trainSet)+1];
        int cnt = 0;
        for (size_t j=0;j<XSIZE(trainSet);j++)
        {
            if (trainSet(i,j)==0)
                continue;
            else
            {
                prob.x[i][cnt].value=DIRECT_A2D_ELEM(trainSet,i,j);
                prob.x[i][cnt].index=j+1;
                cnt++;
            }
        }
        prob.x[i][cnt].index=-1;
        prob.x[i][cnt].value=2;
        prob.y[i] = DIRECT_A1D_ELEM(label,i);
    }
    error_msg = svm_check_parameter(&prob,&param);
    if(error_msg)
    {
        fprintf(stderr,"ERROR: %s\n",error_msg);
        exit(1);
    }
    model=svm_train(&prob,&param);
}

Member Data Documentation

model

svm_model* SVMClassifier::model =nullptr

Definition at line 50 of file svm_classifier.h.

param

svm_parameter SVMClassifier::param

Definition at line 48 of file svm_classifier.h.

prob

svm_problem SVMClassifier::prob

Definition at line 49 of file svm_classifier.h.

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The documentation for this class was generated from the following files:

• xmipp/libraries/classification/svm_classifier.h
• xmipp/libraries/classification/svm_classifier.cpp