src/svm.h

/*****************************************************************************
* This file is provided under the Creative Commons Attribution 3.0 license.
*
* You are free to share, copy, distribute, transmit, or adapt this work
* PROVIDED THAT you attribute the work to the authors listed below.
* For more information, please see the following web page:
* http://creativecommons.org/licenses/by/3.0/
*
* This file is a component of the Sleipnir library for functional genomics,
* authored by:
* Curtis Huttenhower (chuttenh@princeton.edu)
* Mark Schroeder
* Maria D. Chikina
* Olga G. Troyanskaya (ogt@princeton.edu, primary contact)
*
* If you use this library, the included executable tools, or any related
* code in your work, please cite the following publication:
* Curtis Huttenhower, Mark Schroeder, Maria D. Chikina, and
* Olga G. Troyanskaya.
* "The Sleipnir library for computational functional genomics"
*****************************************************************************/
#ifndef SVM_H
#define SVM_H

#ifndef NO_SVM_PERF

#include "svmi.h"

namespace Sleipnir {

class CSVM : CSVMImpl {
public:
    enum EKernel {
        EKernelLinear       = 0,
        EKernelPolynomial   = EKernelLinear + 1,
        EKernelRBF          = EKernelPolynomial + 1
    };

    bool OpenAlphas( std::istream& istm );
    bool Open( std::istream& istm );
    bool Save( std::ostream& ostm ) const;
    bool Learn( const CPCL& PCL, const CGenes& GenesPositive );
    bool Learn( const CPCL& PCL, const CGenes& GenesPositive, const CGenes& GenesNegative );
    bool Evaluate( const CPCL& PCL, std::vector<float>& vecdResults ) const;

    bool Learn( const char* szData ) {
        SData   sData;

        sData.m_eType = SData::EFile;
        sData.m_uData.m_szFile = szData;

        return CSVMImpl::Learn( sData ); }

    bool Learn( const CPCLSet& PCLs, const CDataPair& Answers ) {
        SData   sData;

        sData.m_eType = SData::EPCLs;
        sData.m_uData.m_pPCLs = &PCLs;
        sData.m_uAnswers.m_pAnswers = &Answers;

        return CSVMImpl::Learn( sData ); }

    bool Learn( const IDataset* pData, const CDataPair& Answers ) {
        SData   sData;

        sData.m_eType = SData::EData;
        sData.m_uData.m_pData = pData;
        sData.m_uAnswers.m_pAnswers = &Answers;

        return CSVMImpl::Learn( sData ); }

    bool Evaluate( const char* szFile, CDat& DatResults ) const {
        SData   sData;

        sData.m_eType = SData::EFile;
        sData.m_uData.m_szFile = szFile;

        return CSVMImpl::Evaluate( sData, NULL, DatResults ); }

    bool Evaluate( const CPCLSet& PCLs, CDat& DatResults ) const {
        SData   sData;

        sData.m_eType = SData::EPCLs;
        sData.m_uData.m_pPCLs = &PCLs;

        return CSVMImpl::Evaluate( sData, NULL, DatResults ); }

    bool Evaluate( const IDataset* pData, CDat& DatResults ) const {
        SData   sData;

        sData.m_eType = SData::EData;
        sData.m_uData.m_pData = pData;

        return CSVMImpl::Evaluate( sData, NULL, DatResults ); }

    bool Evaluate( const CPCLSet& PCLs, const CGenes& GenesInclude, CDat& DatResults ) const {
        SData   sData;

        sData.m_eType = SData::EPCLs;
        sData.m_uData.m_pPCLs = &PCLs;

        return CSVMImpl::Evaluate( sData, &GenesInclude, DatResults ); }

    bool Evaluate( const IDataset* pData, const CGenes& GenesInclude, CDat& DatResults ) const {
        SData   sData;

        sData.m_eType = SData::EData;
        sData.m_uData.m_pData = pData;

        return CSVMImpl::Evaluate( sData, &GenesInclude, DatResults ); }

    void SetIterations( size_t iIterations ) {

        m_sLearn.maxiter = (long)iIterations; }

    void SetCache( size_t iMegabytes ) {

        m_sLearn.kernel_cache_size = (long)iMegabytes; }

    void SetTradeoff( float dTradeoff ) {

        m_sLearn.svm_c = dTradeoff; }

    void SetGamma( float dGamma ) {

        m_sKernel.rbf_gamma = dGamma; }

    void SetDegree( size_t iDegree ) {

        m_sKernel.poly_degree = (long)iDegree; }

    void SetKernel( EKernel eKernel ) {

        m_sKernel.kernel_type = eKernel; }

    void SetVerbosity( size_t iVerbosity ) {

        verbosity = (long)iVerbosity; }
};

}

#endif // NO_SVM_PERF

#endif // SVM_H