pksvmogr.cc

/**********************************************************************
pksvmogr.cc: classify vector dataset using Support Vector Machine
Copyright (C) 2008-2017 Pieter Kempeneers

This file is part of pktools

pktools is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

pktools is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with pktools.  If not, see <http://www.gnu.org/licenses/>.
***********************************************************************/
#include <stdlib.h>
#include <vector>
#include <map>
#include <algorithm>
#include "imageclasses/ImgReaderGdal.h"
#include "imageclasses/ImgWriterGdal.h"
#include "imageclasses/ImgReaderOgr.h"
#include "imageclasses/ImgWriterOgr.h"
#include "base/Optionpk.h"
#include "base/PosValue.h"
#include "algorithms/ConfusionMatrix.h"
#include "algorithms/svm.h"

#ifdef HAVE_CONFIG_H
#include <config.h>
#endif
/******************************************************************************/
namespace svm{
  enum SVM_TYPE {C_SVC=0, nu_SVC=1,one_class=2, epsilon_SVR=3, nu_SVR=4};
  enum KERNEL_TYPE {linear=0,polynomial=1,radial=2,sigmoid=3};
}

#define Malloc(type,n) (type *)malloc((n)*sizeof(type))

using namespace std;

int main(int argc, char *argv[])
{
  vector<double> priors;
  
  //--------------------------- command line options ------------------------------------
  Optionpk<string> input_opt("i", "input", "input image"); 
  Optionpk<string> training_opt("t", "training", "Training vector file. A single vector file contains all training features (must be set as: b0, b1, b2,...) for all classes (class numbers identified by label option). Use multiple training files for bootstrap aggregation (alternative to the bag and bsize options, where a random subset is taken from a single training file)");
  Optionpk<string> tlayer_opt("tln", "tln", "Training layer name(s)");
  Optionpk<string> label_opt("label", "label", "Attribute name for class label in training vector file.","label"); 
  Optionpk<unsigned int> balance_opt("bal", "balance", "Balance the input data to this number of samples for each class", 0);
  Optionpk<bool> random_opt("random", "random", "Randomize training data for balancing and bagging", true, 2);
  Optionpk<int> minSize_opt("min", "min", "If number of training pixels is less then min, do not take this class into account (0: consider all classes)", 0);
  Optionpk<unsigned short> band_opt("b", "band", "Band index (starting from 0, either use band option or use start to end)");
  Optionpk<unsigned short> bstart_opt("sband", "startband", "Start band sequence number"); 
  Optionpk<unsigned short> bend_opt("eband", "endband", "End band sequence number"); 
  Optionpk<double> offset_opt("offset", "offset", "Offset value for each spectral band input features: refl[band]=(DN[band]-offset[band])/scale[band]", 0.0);
  Optionpk<double> scale_opt("scale", "scale", "Scale value for each spectral band input features: refl=(DN[band]-offset[band])/scale[band] (use 0 if scale min and max in each band to -1.0 and 1.0)", 0.0);
  Optionpk<double> priors_opt("prior", "prior", "Prior probabilities for each class (e.g., -p 0.3 -p 0.3 -p 0.2 ). Used for input only (ignored for cross validation)", 0.0); 
  Optionpk<string> priorimg_opt("pim", "priorimg", "Prior probability image (multi-band img with band for each class","",2); 
  Optionpk<unsigned short> cv_opt("cv", "cv", "N-fold cross validation mode",0);
  Optionpk<string> cmformat_opt("cmf","cmf","Format for confusion matrix (ascii or latex)","ascii");
  Optionpk<std::string> svm_type_opt("svmt", "svmtype", "Type of SVM (C_SVC, nu_SVC,one_class, epsilon_SVR, nu_SVR)","C_SVC");
  Optionpk<std::string> kernel_type_opt("kt", "kerneltype", "Type of kernel function (linear,polynomial,radial,sigmoid) ","radial");
  Optionpk<unsigned short> kernel_degree_opt("kd", "kd", "Degree in kernel function",3);
  Optionpk<float> gamma_opt("g", "gamma", "Gamma in kernel function",1.0);
  Optionpk<float> coef0_opt("c0", "coef0", "Coef0 in kernel function",0);
  Optionpk<float> ccost_opt("cc", "ccost", "The parameter C of C_SVC, epsilon_SVR, and nu_SVR",1000);
  Optionpk<float> nu_opt("nu", "nu", "The parameter nu of nu_SVC, one_class SVM, and nu_SVR",0.5);
  Optionpk<float> epsilon_loss_opt("eloss", "eloss", "The epsilon in loss function of epsilon_SVR",0.1);
  Optionpk<int> cache_opt("cache", "cache", "Cache memory size in MB",100);
  Optionpk<float> epsilon_tol_opt("etol", "etol", "The tolerance of termination criterion",0.001);
  Optionpk<bool> shrinking_opt("shrink", "shrink", "Whether to use the shrinking heuristics",false);
  Optionpk<bool> prob_est_opt("pe", "probest", "Whether to train a SVC or SVR model for probability estimates",true,2);
  // Optionpk<bool> weight_opt("wi", "wi", "Set the parameter C of class i to weight*C, for C_SVC",true);
  Optionpk<unsigned short> comb_opt("comb", "comb", "How to combine bootstrap aggregation classifiers (0: sum rule, 1: product rule, 2: max rule). Also used to aggregate classes with rc option.",0); 
  Optionpk<unsigned short> bag_opt("bag", "bag", "Number of bootstrap aggregations", 1);
  Optionpk<int> bagSize_opt("bagsize", "bagsize", "Percentage of features used from available training features for each bootstrap aggregation (one size for all classes, or a different size for each class respectively", 100);
  Optionpk<string> classBag_opt("cb", "classbag", "Output for each individual bootstrap aggregation");
  Optionpk<string> mask_opt("m", "mask", "Only classify within specified mask (vector or raster).");
  Optionpk<unsigned short> nodata_opt("nodata", "nodata", "Nodata value to put where vector dataset is masked as nodata", 0);
  Optionpk<string> output_opt("o", "output", "Output vector dataset"); 
  Optionpk<string> option_opt("co", "co", "Creation option for output file. Multiple options can be specified.");
  Optionpk<string> colorTable_opt("ct", "ct", "Color table in ASCII format having 5 columns: id R G B ALFA (0: transparent, 255: solid)"); 
  Optionpk<string> entropy_opt("entropy", "entropy", "Entropy image (measure for uncertainty of classifier output","",2); 
  Optionpk<string> active_opt("active", "active", "Ogr output for active training sample.","",2); 
  Optionpk<string> ogrformat_opt("f", "f", "Output ogr format for active training sample","SQLite");
  Optionpk<unsigned int> nactive_opt("na", "nactive", "Number of active training points",1);
  Optionpk<string> classname_opt("c", "class", "List of class names."); 
  Optionpk<short> classvalue_opt("r", "reclass", "List of class values (use same order as in class opt)."); 
  Optionpk<short> verbose_opt("v", "verbose", "Verbose level",0,2);

  option_opt.setHide(1);
  band_opt.setHide(1);
  bstart_opt.setHide(1);
  bend_opt.setHide(1);
  balance_opt.setHide(1);
  minSize_opt.setHide(1);
  bag_opt.setHide(1);
  bagSize_opt.setHide(1);
  comb_opt.setHide(1);
  classBag_opt.setHide(1);
  priorimg_opt.setHide(1);
  offset_opt.setHide(1);
  scale_opt.setHide(1);
  svm_type_opt.setHide(1);
  kernel_type_opt.setHide(1);
  kernel_degree_opt.setHide(1);
  coef0_opt.setHide(1);
  nu_opt.setHide(1);
  epsilon_loss_opt.setHide(1);
  cache_opt.setHide(1);
  epsilon_tol_opt.setHide(1);
  shrinking_opt.setHide(1);
  prob_est_opt.setHide(1);
  entropy_opt.setHide(1);
  active_opt.setHide(1);
  nactive_opt.setHide(1);
  random_opt.setHide(1);

  verbose_opt.setHide(2);

  bool doProcess;//stop process when program was invoked with help option (-h --help)
  try{
    doProcess=training_opt.retrieveOption(argc,argv);
    input_opt.retrieveOption(argc,argv);
    output_opt.retrieveOption(argc,argv);
    cv_opt.retrieveOption(argc,argv);
    cmformat_opt.retrieveOption(argc,argv);
    tlayer_opt.retrieveOption(argc,argv);
    classname_opt.retrieveOption(argc,argv);
    classvalue_opt.retrieveOption(argc,argv);
    ogrformat_opt.retrieveOption(argc,argv);
    option_opt.retrieveOption(argc,argv);
    colorTable_opt.retrieveOption(argc,argv);
    label_opt.retrieveOption(argc,argv);
    priors_opt.retrieveOption(argc,argv);
    gamma_opt.retrieveOption(argc,argv);
    ccost_opt.retrieveOption(argc,argv);
    mask_opt.retrieveOption(argc,argv);
    nodata_opt.retrieveOption(argc,argv);
    // Advanced options
    band_opt.retrieveOption(argc,argv);
    bstart_opt.retrieveOption(argc,argv);
    bend_opt.retrieveOption(argc,argv);
    balance_opt.retrieveOption(argc,argv);
    minSize_opt.retrieveOption(argc,argv);
    bag_opt.retrieveOption(argc,argv);
    bagSize_opt.retrieveOption(argc,argv);
    comb_opt.retrieveOption(argc,argv);
    classBag_opt.retrieveOption(argc,argv);
    priorimg_opt.retrieveOption(argc,argv);
    offset_opt.retrieveOption(argc,argv);
    scale_opt.retrieveOption(argc,argv);
    svm_type_opt.retrieveOption(argc,argv);
    kernel_type_opt.retrieveOption(argc,argv);
    kernel_degree_opt.retrieveOption(argc,argv);
    coef0_opt.retrieveOption(argc,argv);
    nu_opt.retrieveOption(argc,argv);
    epsilon_loss_opt.retrieveOption(argc,argv);
    cache_opt.retrieveOption(argc,argv);
    epsilon_tol_opt.retrieveOption(argc,argv);
    shrinking_opt.retrieveOption(argc,argv);
    prob_est_opt.retrieveOption(argc,argv);
    entropy_opt.retrieveOption(argc,argv);
    active_opt.retrieveOption(argc,argv);
    nactive_opt.retrieveOption(argc,argv);
    verbose_opt.retrieveOption(argc,argv);
    random_opt.retrieveOption(argc,argv);
  }
  catch(string predefinedString){
    std::cout << predefinedString << std::endl;
    exit(0);
  }
  if(!doProcess){
    cout << endl;
    cout << "Usage: pksvmogr -t training [-i input -o output] [-cv value]" << endl;
    cout << endl;
    std::cout << "short option -h shows basic options only, use long option --help to show all options" << std::endl;
    exit(0);//help was invoked, stop processing
  }

  if(entropy_opt[0]=="")
    entropy_opt.clear();
  if(active_opt[0]=="")
    active_opt.clear();
  if(priorimg_opt[0]=="")
    priorimg_opt.clear();


  std::map<std::string, svm::SVM_TYPE> svmMap;

  svmMap["C_SVC"]=svm::C_SVC;
  svmMap["nu_SVC"]=svm::nu_SVC;
  svmMap["one_class"]=svm::one_class;
  svmMap["epsilon_SVR"]=svm::epsilon_SVR;
  svmMap["nu_SVR"]=svm::nu_SVR;

  std::map<std::string, svm::KERNEL_TYPE> kernelMap;

  kernelMap["linear"]=svm::linear;
  kernelMap["polynomial"]=svm::polynomial;
  kernelMap["radial"]=svm::radial;
  kernelMap["sigmoid;"]=svm::sigmoid;

  assert(training_opt.size());

  if(verbose_opt[0]>=1){
    if(input_opt.size())
      std::cout << "input filename: " << input_opt[0] << std::endl;
    if(mask_opt.size())
      std::cout << "mask filename: " << mask_opt[0] << std::endl;
    std::cout << "training vector file: " << std::endl;
    for(int ifile=0;ifile<training_opt.size();++ifile)
      std::cout << training_opt[ifile] << std::endl;
    std::cout << "verbose: " << verbose_opt[0] << std::endl;
  }
  unsigned short nbag=(training_opt.size()>1)?training_opt.size():bag_opt[0];
  if(verbose_opt[0]>=1)
    std::cout << "number of bootstrap aggregations: " << nbag << std::endl;

  ImgReaderOgr extentReader;
  OGRLayer  *readLayer;

  double ulx=0;
  double uly=0;
  double lrx=0;
  double lry=0;

  bool maskIsVector=false;
  if(mask_opt.size()){
    try{
      extentReader.open(mask_opt[0]);
      maskIsVector=true;
      readLayer = extentReader.getDataSource()->GetLayer(0);
      if(!(extentReader.getExtent(ulx,uly,lrx,lry))){
    cerr << "Error: could not get extent from " << mask_opt[0] << endl;
    exit(1);
      }
    }
    catch(string errorString){
      maskIsVector=false;
    }
  }

  ImgWriterOgr activeWriter;
  if(active_opt.size()){
    prob_est_opt[0]=true;
    ImgReaderOgr trainingReader(training_opt[0]);
    activeWriter.open(active_opt[0],ogrformat_opt[0]);
    activeWriter.createLayer(active_opt[0],trainingReader.getProjection(),wkbPoint,NULL);
    activeWriter.copyFields(trainingReader);
  }
  vector<PosValue> activePoints(nactive_opt[0]);
  for(int iactive=0;iactive<activePoints.size();++iactive){
    activePoints[iactive].value=1.0;
    activePoints[iactive].posx=0.0;
    activePoints[iactive].posy=0.0;
  }
  
  unsigned int totalSamples=0;
  unsigned int nactive=0;
  vector<struct svm_model*> svm(nbag);
  vector<struct svm_parameter> param(nbag);

  short nclass=0;
  int nband=0;
  int startBand=2;//first two bands represent X and Y pos

  //normalize priors from command line
  if(priors_opt.size()>1){//priors from argument list
    priors.resize(priors_opt.size());
    double normPrior=0;
    for(short iclass=0;iclass<priors_opt.size();++iclass){
      priors[iclass]=priors_opt[iclass];
      normPrior+=priors[iclass];
    }
    //normalize
    for(short iclass=0;iclass<priors_opt.size();++iclass)
      priors[iclass]/=normPrior;
  }

  //convert start and end band options to vector of band indexes
  try{
    if(bstart_opt.size()){
      if(bend_opt.size()!=bstart_opt.size()){
    string errorstring="Error: options for start and end band indexes must be provided as pairs, missing end band";
    throw(errorstring);
      }
      band_opt.clear();
      for(int ipair=0;ipair<bstart_opt.size();++ipair){
    if(bend_opt[ipair]<=bstart_opt[ipair]){
      string errorstring="Error: index for end band must be smaller then start band";
      throw(errorstring);
    }
    for(int iband=bstart_opt[ipair];iband<=bend_opt[ipair];++iband)
      band_opt.push_back(iband);
      }
    }
  }
  catch(string error){
    cerr << error << std::endl;
    exit(1);
  }
  //sort bands
  if(band_opt.size())
    std::sort(band_opt.begin(),band_opt.end());

  map<string,short> classValueMap;
  vector<std::string> nameVector;
  if(classname_opt.size()){
    assert(classname_opt.size()==classvalue_opt.size());
    for(int iclass=0;iclass<classname_opt.size();++iclass)
      classValueMap[classname_opt[iclass]]=classvalue_opt[iclass];
  }

  //----------------------------------- Training -------------------------------
  confusionmatrix::ConfusionMatrix cm;
  vector< vector<double> > offset(nbag);
  vector< vector<double> > scale(nbag);
  map<string,Vector2d<float> > trainingMap;
  vector< Vector2d<float> > trainingPixels;//[class][sample][band]
  vector<string> fields;

  vector<struct svm_problem> prob(nbag);
  vector<struct svm_node *> x_space(nbag);

  for(int ibag=0;ibag<nbag;++ibag){
    //organize training data
    if(ibag<training_opt.size()){//if bag contains new training pixels
      trainingMap.clear();
      trainingPixels.clear();
      if(verbose_opt[0]>=1)
        std::cout << "reading imageVector file " << training_opt[0] << std::endl;
      try{
    ImgReaderOgr trainingReaderBag(training_opt[ibag]);
        if(band_opt.size())
      //todo: when tlayer_opt is provided, readDataImageOgr does not read any layer
          totalSamples=trainingReaderBag.readDataImageOgr(trainingMap,fields,band_opt,label_opt[0],tlayer_opt,verbose_opt[0]);
        else
          totalSamples=trainingReaderBag.readDataImageOgr(trainingMap,fields,0,0,label_opt[0],tlayer_opt,verbose_opt[0]);
        if(trainingMap.size()<2){
          string errorstring="Error: could not read at least two classes from training file, did you provide class labels in training sample (see option label)?";
          throw(errorstring);
        }
    trainingReaderBag.close();
      }
      catch(string error){
        cerr << error << std::endl;
        exit(1);
      }
      catch(std::exception& e){
        std::cerr << "Error: ";
        std::cerr << e.what() << std::endl;
        std::cerr << CPLGetLastErrorMsg() << std::endl; 
        exit(1);
      }
      catch(...){
        cerr << "error caught" << std::endl;
        exit(1);
      }

      //convert map to vector
      // short iclass=0;
      if(verbose_opt[0]>1)
        std::cout << "training pixels: " << std::endl;
      map<string,Vector2d<float> >::iterator mapit=trainingMap.begin();
      while(mapit!=trainingMap.end()){
        //delete small classes
        if((mapit->second).size()<minSize_opt[0]){
          trainingMap.erase(mapit);
          continue;
        }
        trainingPixels.push_back(mapit->second);
        if(verbose_opt[0]>1)
          std::cout << mapit->first << ": " << (mapit->second).size() << " samples" << std::endl;
        ++mapit;
      }
      if(!ibag){
        nclass=trainingPixels.size();
    if(classname_opt.size())
      assert(nclass==classname_opt.size());
        nband=trainingPixels[0][0].size()-2;//X and Y//trainingPixels[0][0].size();
      }
      else{
        assert(nclass==trainingPixels.size());
        assert(nband==trainingPixels[0][0].size()-2);
      }

      //do not remove outliers here: could easily be obtained through ogr2ogr -where 'B2<110' output.shp input.shp
      //balance training data
      if(balance_opt[0]>0){
        while(balance_opt.size()<nclass)
          balance_opt.push_back(balance_opt.back());
        if(random_opt[0])
          srand(time(NULL));
        totalSamples=0;
        for(short iclass=0;iclass<nclass;++iclass){
          if(trainingPixels[iclass].size()>balance_opt[iclass]){
            while(trainingPixels[iclass].size()>balance_opt[iclass]){
              int index=rand()%trainingPixels[iclass].size();
              trainingPixels[iclass].erase(trainingPixels[iclass].begin()+index);
            }
          }
          else{
            int oldsize=trainingPixels[iclass].size();
            for(int isample=trainingPixels[iclass].size();isample<balance_opt[iclass];++isample){
              int index = rand()%oldsize;
              trainingPixels[iclass].push_back(trainingPixels[iclass][index]);
            }
          }
          totalSamples+=trainingPixels[iclass].size();
        }
      }
    
      //set scale and offset
      offset[ibag].resize(nband);
      scale[ibag].resize(nband);
      if(offset_opt.size()>1)
        assert(offset_opt.size()==nband);
      if(scale_opt.size()>1)
        assert(scale_opt.size()==nband);
      for(int iband=0;iband<nband;++iband){
        if(verbose_opt[0]>=1)
          std::cout << "scaling for band" << iband << std::endl;
        offset[ibag][iband]=(offset_opt.size()==1)?offset_opt[0]:offset_opt[iband];
        scale[ibag][iband]=(scale_opt.size()==1)?scale_opt[0]:scale_opt[iband];
        //search for min and maximum
        if(scale[ibag][iband]<=0){
          float theMin=trainingPixels[0][0][iband+startBand];
          float theMax=trainingPixels[0][0][iband+startBand];
          for(short iclass=0;iclass<nclass;++iclass){
            for(int isample=0;isample<trainingPixels[iclass].size();++isample){
              if(theMin>trainingPixels[iclass][isample][iband+startBand])
                theMin=trainingPixels[iclass][isample][iband+startBand];
              if(theMax<trainingPixels[iclass][isample][iband+startBand])
                theMax=trainingPixels[iclass][isample][iband+startBand];
            }
          }
          offset[ibag][iband]=theMin+(theMax-theMin)/2.0;
          scale[ibag][iband]=(theMax-theMin)/2.0;
          if(verbose_opt[0]>=1){
            std::cout << "Extreme image values for band " << iband << ": [" << theMin << "," << theMax << "]" << std::endl;
            std::cout << "Using offset, scale: " << offset[ibag][iband] << ", " << scale[ibag][iband] << std::endl;
            std::cout << "scaled values for band " << iband << ": [" << (theMin-offset[ibag][iband])/scale[ibag][iband] << "," << (theMax-offset[ibag][iband])/scale[ibag][iband] << "]" << std::endl;
          }
        }
      }
    }
    else{//use same offset and scale 
      offset[ibag].resize(nband);
      scale[ibag].resize(nband);
      for(int iband=0;iband<nband;++iband){
        offset[ibag][iband]=offset[0][iband];
        scale[ibag][iband]=scale[0][iband];
      }
    }
      
    if(!ibag){
      if(priors_opt.size()==1){//default: equal priors for each class
        priors.resize(nclass);
        for(short iclass=0;iclass<nclass;++iclass)
          priors[iclass]=1.0/nclass;
      }
      assert(priors_opt.size()==1||priors_opt.size()==nclass);

      //set bagsize for each class if not done already via command line
      while(bagSize_opt.size()<nclass)
        bagSize_opt.push_back(bagSize_opt.back());

      if(verbose_opt[0]>=1){
        std::cout << "number of bands: " << nband << std::endl;
        std::cout << "number of classes: " << nclass << std::endl;
    if(priorimg_opt.empty()){
      std::cout << "priors:";
      for(short iclass=0;iclass<nclass;++iclass)
        std::cout << " " << priors[iclass];
      std::cout << std::endl;
    }
      }
      map<string,Vector2d<float> >::iterator mapit=trainingMap.begin();
      bool doSort=true;
      try{
    while(mapit!=trainingMap.end()){
      nameVector.push_back(mapit->first);
      if(classValueMap.size()){
        //check if name in training is covered by classname_opt (values can not be 0)
        if(classValueMap[mapit->first]>0){
          if(cm.getClassIndex(type2string<short>(classValueMap[mapit->first]))<0){
        cm.pushBackClassName(type2string<short>(classValueMap[mapit->first]),doSort);
          }
        }
        else{
          std::cerr << "Error: names in classname option are not complete, please check names in training vector and make sure classvalue is > 0" << std::endl;
          exit(1);
        }
      }
      else
        cm.pushBackClassName(mapit->first,doSort);
      ++mapit;
    }
      }
      catch(BadConversion conversionString){
    std::cerr << "Error: did you provide class pairs names (-c) and integer values (-r) for each class in training vector?" << std::endl;
    exit(1);
      }
      if(classname_opt.empty()){
        //std::cerr << "Warning: no class name and value pair provided for all " << nclass << " classes, using string2type<int> instead!" << std::endl;
        for(int iclass=0;iclass<nclass;++iclass){
          if(verbose_opt[0])
            std::cout << iclass << " " << cm.getClass(iclass) << " -> " << string2type<short>(cm.getClass(iclass)) << std::endl;
          classValueMap[cm.getClass(iclass)]=string2type<short>(cm.getClass(iclass));
        }
      }

      // if(priors_opt.size()==nameVector.size()){
      //    std::cerr << "Warning: please check if priors are provided in correct order!!!" << std::endl;
      //    for(int iclass=0;iclass<nameVector.size();++iclass)
      //      std::cerr << nameVector[iclass] << " " << priors_opt[iclass] << std::endl;
      // }
    }//if(!ibag)

    //Calculate features of training set
    vector< Vector2d<float> > trainingFeatures(nclass);
    for(short iclass=0;iclass<nclass;++iclass){
      int nctraining=0;
      if(verbose_opt[0]>=1)
        std::cout << "calculating features for class " << iclass << std::endl;
      if(random_opt[0])
        srand(time(NULL));
      nctraining=(bagSize_opt[iclass]<100)? trainingPixels[iclass].size()/100.0*bagSize_opt[iclass] : trainingPixels[iclass].size();//bagSize_opt[iclass] given in % of training size
      if(nctraining<=0)
        nctraining=1;
      assert(nctraining<=trainingPixels[iclass].size());
      int index=0;
      if(bagSize_opt[iclass]<100)
        random_shuffle(trainingPixels[iclass].begin(),trainingPixels[iclass].end());
      if(verbose_opt[0]>1)
    std::cout << "nctraining (class " << iclass << "): " << nctraining << std::endl;
      trainingFeatures[iclass].resize(nctraining);
      for(int isample=0;isample<nctraining;++isample){
        //scale pixel values according to scale and offset!!!
        for(int iband=0;iband<nband;++iband){
          float value=trainingPixels[iclass][isample][iband+startBand];
          trainingFeatures[iclass][isample].push_back((value-offset[ibag][iband])/scale[ibag][iband]);
        }
      }
      assert(trainingFeatures[iclass].size()==nctraining);
    }
    
    unsigned int nFeatures=trainingFeatures[0][0].size();
    if(verbose_opt[0]>=1)
      std::cout << "number of features: " << nFeatures << std::endl;
    unsigned int ntraining=0;
    for(short iclass=0;iclass<nclass;++iclass)
      ntraining+=trainingFeatures[iclass].size();
    if(verbose_opt[0]>=1)
      std::cout << "training size over all classes: " << ntraining << std::endl;

    prob[ibag].l=ntraining;
    prob[ibag].y = Malloc(double,prob[ibag].l);
    prob[ibag].x = Malloc(struct svm_node *,prob[ibag].l);
    x_space[ibag] = Malloc(struct svm_node,(nFeatures+1)*ntraining);
    unsigned long int spaceIndex=0;
    int lIndex=0;
    for(short iclass=0;iclass<nclass;++iclass){
      for(int isample=0;isample<trainingFeatures[iclass].size();++isample){
        prob[ibag].x[lIndex]=&(x_space[ibag][spaceIndex]);
        for(int ifeature=0;ifeature<nFeatures;++ifeature){
          x_space[ibag][spaceIndex].index=ifeature+1;
          x_space[ibag][spaceIndex].value=trainingFeatures[iclass][isample][ifeature];
          ++spaceIndex;
        }
        x_space[ibag][spaceIndex++].index=-1;
        prob[ibag].y[lIndex]=iclass;
        ++lIndex;
      }
    }
    assert(lIndex==prob[ibag].l);

    //set SVM parameters through command line options
    param[ibag].svm_type = svmMap[svm_type_opt[0]];
    param[ibag].kernel_type = kernelMap[kernel_type_opt[0]];
    param[ibag].degree = kernel_degree_opt[0];
    param[ibag].gamma = (gamma_opt[0]>0)? gamma_opt[0] : 1.0/nFeatures;
    param[ibag].coef0 = coef0_opt[0];
    param[ibag].nu = nu_opt[0];
    param[ibag].cache_size = cache_opt[0];
    param[ibag].C = ccost_opt[0];
    param[ibag].eps = epsilon_tol_opt[0];
    param[ibag].p = epsilon_loss_opt[0];
    param[ibag].shrinking = (shrinking_opt[0])? 1 : 0;
    param[ibag].probability = (prob_est_opt[0])? 1 : 0;
    param[ibag].nr_weight = 0;//not used: I use priors and balancing
    param[ibag].weight_label = NULL;
    param[ibag].weight = NULL;
    param[ibag].verbose=(verbose_opt[0]>1)? true:false;

    if(verbose_opt[0]>1)
      std::cout << "checking parameters" << std::endl;
    svm_check_parameter(&prob[ibag],&param[ibag]);
    if(verbose_opt[0])
      std::cout << "parameters ok, training" << std::endl;
    svm[ibag]=svm_train(&prob[ibag],&param[ibag]);
    if(verbose_opt[0]>1)
      std::cout << "SVM is now trained" << std::endl;
    if(cv_opt[0]>1){
      if(verbose_opt[0]>1)
    std::cout << "Cross validating" << std::endl;
      double *target = Malloc(double,prob[ibag].l);
      svm_cross_validation(&prob[ibag],&param[ibag],cv_opt[0],target);
      assert(param[ibag].svm_type != EPSILON_SVR&&param[ibag].svm_type != NU_SVR);//only for regression

      for(int i=0;i<prob[ibag].l;i++){
    string refClassName=nameVector[prob[ibag].y[i]];
    string className=nameVector[target[i]];
    if(classValueMap.size())
      cm.incrementResult(type2string<short>(classValueMap[refClassName]),type2string<short>(classValueMap[className]),1.0/nbag);
    else
      cm.incrementResult(cm.getClass(prob[ibag].y[i]),cm.getClass(target[i]),1.0/nbag);
      }
      free(target);
    }
    // *NOTE* Because svm_model contains pointers to svm_problem, you can
    // not free the memory used by svm_problem if you are still using the
    // svm_model produced by svm_train(). 
  }//for ibag
  if(cv_opt[0]>1){
    assert(cm.nReference());
    cm.setFormat(cmformat_opt[0]);
    cm.reportSE95(false);
    std::cout << cm << std::endl;
    // cout << "class #samples userAcc prodAcc" << endl;
    // double se95_ua=0;
    // double se95_pa=0;
    // double se95_oa=0;
    // double dua=0;
    // double dpa=0;
    // double doa=0;
    // for(short iclass=0;iclass<cm.nClasses();++iclass){
    //   dua=cm.ua(cm.getClass(iclass),&se95_ua);
    //   dpa=cm.pa(cm.getClass(iclass),&se95_pa);
    //   cout << cm.getClass(iclass) << " " << cm.nReference(cm.getClass(iclass)) << " " << dua << " (" << se95_ua << ")" << " " << dpa << " (" << se95_pa << ")" << endl;
    // }
    // std::cout << "Kappa: " << cm.kappa() << std::endl;
    // doa=cm.oa(&se95_oa);
    // std::cout << "Overall Accuracy: " << 100*doa << " (" << 100*se95_oa << ")"  << std::endl;
  }

  //--------------------------------- end of training -----------------------------------
  if(input_opt.empty())
    exit(0);

  const char* pszMessage;
  void* pProgressArg=NULL;
  GDALProgressFunc pfnProgress=GDALTermProgress;
  float progress=0;
  if(!verbose_opt[0])
    pfnProgress(progress,pszMessage,pProgressArg);
  //-------------------------------- open image file ------------------------------------
  bool inputIsRaster=false;
  ImgReaderOgr imgReaderOgr;
  try{
    imgReaderOgr.open(input_opt[0]);
    imgReaderOgr.close();
  }
  catch(string errorString){
    inputIsRaster=true;
  }
  if(!inputIsRaster){//classify vector file
    cm.clearResults();
    //notice that fields have already been set by readDataImageOgr (taking into account appropriate bands)
    for(int ivalidation=0;ivalidation<input_opt.size();++ivalidation){
      if(output_opt.size())
    assert(output_opt.size()==input_opt.size());
      if(verbose_opt[0])
        std::cout << "opening img reader " << input_opt[ivalidation] << std::endl;
      imgReaderOgr.open(input_opt[ivalidation]);
      ImgWriterOgr imgWriterOgr;

      if(output_opt.size()){
    if(verbose_opt[0])
      std::cout << "opening img writer and copying fields from img reader" << output_opt[ivalidation] << std::endl;
    imgWriterOgr.open(output_opt[ivalidation],imgReaderOgr);
      }
      if(verbose_opt[0])
    cout << "number of layers in input ogr file: " << imgReaderOgr.getLayerCount() << endl;
      for(int ilayer=0;ilayer<imgReaderOgr.getLayerCount();++ilayer){
    if(verbose_opt[0])
      cout << "processing input layer " << ilayer << endl;
    if(output_opt.size()){
      if(verbose_opt[0])
        std::cout << "creating field class" << std::endl;
      if(classValueMap.size())
        imgWriterOgr.createField("class",OFTInteger,ilayer);
      else
        imgWriterOgr.createField("class",OFTString,ilayer);
    }
    unsigned int nFeatures=imgReaderOgr.getFeatureCount(ilayer);
    unsigned int ifeature=0;
    progress=0;
    pfnProgress(progress,pszMessage,pProgressArg);
    OGRFeature *poFeature;
    while( (poFeature = imgReaderOgr.getLayer(ilayer)->GetNextFeature()) != NULL ){
      if(verbose_opt[0]>1)
        std::cout << "feature " << ifeature << std::endl;
      if( poFeature == NULL ){
          cout << "Warning: could not read feature " << ifeature << " in layer " << imgReaderOgr.getLayerName(ilayer) << endl;
          continue;
      }
      OGRFeature *poDstFeature = NULL;
      if(output_opt.size()){
        poDstFeature=imgWriterOgr.createFeature(ilayer);
        if( poDstFeature->SetFrom( poFeature, TRUE ) != OGRERR_NONE ){
          CPLError( CE_Failure, CPLE_AppDefined,
            "Unable to translate feature %d from layer %s.\n",
            poFeature->GetFID(), imgWriterOgr.getLayerName(ilayer).c_str() );
          OGRFeature::DestroyFeature( poFeature );
          OGRFeature::DestroyFeature( poDstFeature );
        }
      }
      vector<float> validationPixel;
      vector<float> validationFeature;
        
      imgReaderOgr.readData(validationPixel,OFTReal,fields,poFeature,ilayer);
      assert(validationPixel.size()==nband);
      vector<float> probOut(nclass);//posterior prob for each class
      for(short iclass=0;iclass<nclass;++iclass)
        probOut[iclass]=0;
      for(int ibag=0;ibag<nbag;++ibag){
        for(int iband=0;iband<nband;++iband){
          validationFeature.push_back((validationPixel[iband]-offset[ibag][iband])/scale[ibag][iband]);
          if(verbose_opt[0]==2)
        std::cout << " " << validationFeature.back();
        }
        if(verbose_opt[0]==2)
          std::cout << std::endl;
        vector<double> result(nclass);
        struct svm_node *x;
        x = (struct svm_node *) malloc((validationFeature.size()+1)*sizeof(struct svm_node));
        for(int i=0;i<validationFeature.size();++i){
          x[i].index=i+1;
          x[i].value=validationFeature[i];
        }

        x[validationFeature.size()].index=-1;//to end svm feature vector
        double predict_label=0;
        if(!prob_est_opt[0]){
          predict_label = svm_predict(svm[ibag],x);
          for(short iclass=0;iclass<nclass;++iclass){
        if(iclass==static_cast<short>(predict_label))
          result[iclass]=1;
        else
          result[iclass]=0;
          }
        }
        else{
          assert(svm_check_probability_model(svm[ibag]));
          predict_label = svm_predict_probability(svm[ibag],x,&(result[0]));
        }
        if(verbose_opt[0]>1){
          std::cout << "predict_label: " << predict_label << std::endl;
          for(int iclass=0;iclass<result.size();++iclass)
        std::cout << result[iclass] << " ";
          std::cout << std::endl;
        }

        //calculate posterior prob of bag 
        for(short iclass=0;iclass<nclass;++iclass){
          switch(comb_opt[0]){
          default:
          case(0)://sum rule
        probOut[iclass]+=result[iclass]*priors[iclass];//add probabilities for each bag
          break;
          case(1)://product rule
        probOut[iclass]*=pow(static_cast<float>(priors[iclass]),static_cast<float>(1.0-nbag)/nbag)*result[iclass];//multiply probabilities for each bag
        break;
          case(2)://max rule
        if(priors[iclass]*result[iclass]>probOut[iclass])
          probOut[iclass]=priors[iclass]*result[iclass];
        break;
          }
        }
        free(x);
      }//for ibag

      //search for max class prob
      float maxBag=0;
      float normBag=0;
      string classOut="Unclassified";
      for(short iclass=0;iclass<nclass;++iclass){
        if(verbose_opt[0]>1)
          std::cout << probOut[iclass] << " ";
        if(probOut[iclass]>maxBag){
          maxBag=probOut[iclass];
          classOut=nameVector[iclass];
        }
      }
      //look for class name
      if(verbose_opt[0]>1){
        if(classValueMap.size())
          std::cout << "->" << classValueMap[classOut] << std::endl;
        else        
          std::cout << "->" << classOut << std::endl;
      }
      if(output_opt.size()){
        if(classValueMap.size())
          poDstFeature->SetField("class",classValueMap[classOut]);
        else        
          poDstFeature->SetField("class",classOut.c_str());
        poDstFeature->SetFID( poFeature->GetFID() );
      }
      int labelIndex=poFeature->GetFieldIndex(label_opt[0].c_str());
      if(labelIndex>=0){
        string classRef=poFeature->GetFieldAsString(labelIndex);
        if(classRef!="0"){
          if(classValueMap.size())
        cm.incrementResult(type2string<short>(classValueMap[classRef]),type2string<short>(classValueMap[classOut]),1);
          else
        cm.incrementResult(classRef,classOut,1);
        }
      }
      CPLErrorReset();
      if(output_opt.size()){
        if(imgWriterOgr.createFeature(poDstFeature,ilayer) != OGRERR_NONE){
          CPLError( CE_Failure, CPLE_AppDefined,
            "Unable to translate feature %d from layer %s.\n",
            poFeature->GetFID(), imgWriterOgr.getLayerName(ilayer).c_str() );
          OGRFeature::DestroyFeature( poDstFeature );
          OGRFeature::DestroyFeature( poDstFeature );
        }
      }
      ++ifeature;
      if(!verbose_opt[0]){
        progress=static_cast<float>(ifeature+1.0)/nFeatures;
        pfnProgress(progress,pszMessage,pProgressArg);
      }
      OGRFeature::DestroyFeature( poFeature );
      OGRFeature::DestroyFeature( poDstFeature );
    }//get next feature
      }//next layer
      imgReaderOgr.close();
      if(output_opt.size())
    imgWriterOgr.close();
    }
    if(cm.nReference()){
      std::cout << cm << std::endl;
      cout << "class #samples userAcc prodAcc" << endl;
      double se95_ua=0;
      double se95_pa=0;
      double se95_oa=0;
      double dua=0;
      double dpa=0;
      double doa=0;
      for(short iclass=0;iclass<cm.nClasses();++iclass){
    dua=cm.ua_pct(cm.getClass(iclass),&se95_ua);
    dpa=cm.pa_pct(cm.getClass(iclass),&se95_pa);
    cout << cm.getClass(iclass) << " " << cm.nReference(cm.getClass(iclass)) << " " << dua << " (" << se95_ua << ")" << " " << dpa << " (" << se95_pa << ")" << endl;
      }
      std::cout << "Kappa: " << cm.kappa() << std::endl;
      doa=cm.oa(&se95_oa);
      std::cout << "Overall Accuracy: " << 100*doa << " (" << 100*se95_oa << ")"  << std::endl;
    }
  }
  try{
    if(active_opt.size())
      activeWriter.close();
  }
  catch(string errorString){
    std::cerr << "Error: errorString" << std::endl;
  }

  for(int ibag=0;ibag<nbag;++ibag){
    // svm_destroy_param[ibag](&param[ibag]);
    svm_destroy_param(&param[ibag]);
    free(prob[ibag].y);
    free(prob[ibag].x);
    free(x_space[ibag]);
    svm_free_and_destroy_model(&(svm[ibag]));
  }
  return 0;
}