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/**********************************************************************
pkoptsvm.cc: program to optimize parameters for support vector machine classifier pksvm
Copyright (C) 2008-2014 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 <iostream>
#include <sstream>
#include <fstream>
#include <vector>
#include <math.h>
//#include <nlopt.hpp>
#include "base/Optionpk.h"
#include "algorithms/ConfusionMatrix.h"
#include "algorithms/FeatureSelector.h"
//#include "algorithms/OptFactory.h"
#include "algorithms/CostFactorySVM.h"
#include "algorithms/svm.h"
#include "imageclasses/ImgReaderOgr.h"
#ifdef HAVE_CONFIG_H
#include <config.h>
#endif
/******************************************************************************/
/*! \page pkoptsvm pkoptsvm
program to optimize parameters for support vector machine classifier pksvm
## SYNOPSIS
<code>
Usage: pkoptsvm -t training
</code>
<code>
Options: [-cc startvalue -cc endvalue] [-g startvalue -g endvalue] [-stepcc stepsize] [-stepg stepsize]
Advanced options:
</code>
\section pkoptsvm_description Description
The support vector machine depends on several parameters. Ideally, these parameters should be optimized for each classification problem. In case of a radial basis kernel function, two important parameters are \em{cost} and \em{gamma}. The utility pkoptsvm can optimize these two parameters, based on an accuracy assessment (the Kappa value). If an input test set (-i) is provided, it is used for the accuracy assessment. If not, the accuracy assessment is based on a cross validation (-cv) of the training sample.
The optimization routine uses a grid search. The initial and final values of the parameters can be set with -cc startvalue -cc endvalue and -g startvalue -g endvalue for cost and gamma respectively. The search uses a multiplicative step for iterating the parameters (set with the options -stepcc and -stepg). An often used approach is to define a relatively large multiplicative step first (e.g 10) to obtain an initial estimate for both parameters. The estimate can then be optimized by defining a smaller step (>1) with constrained start and end values for the parameters cost and gamma.
\section pkoptsvm_options Options
- use either `-short` or `--long` options (both `--long=value` and `--long value` are supported)
- short option `-h` shows basic options only, long option `--help` shows all options
|short|long|type|default|description|
|-----|----|----|-------|-----------|
| t | training | std::string | |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). |
| cc | ccost | float | 1 |min and max boundaries the parameter C of C-SVC, epsilon-SVR, and nu-SVR (optional: initial value) |
| g | gamma | float | 0 |min max boundaries for gamma in kernel function (optional: initial value) |
| stepcc | stepcc | double | 2 |multiplicative step for ccost in GRID search |
| stepg | stepg | double | 2 |multiplicative step for gamma in GRID search |
| i | input | std::string | |input test vector file |
| tln | tln | std::string | |training layer name(s) |
| label | label | std::string | label |identifier for class label in training vector file. |
| bal | balance | unsigned int | 0 |balance the input data to this number of samples for each class |
| random | random | bool | true |in case of balance, randomize input data |
| min | min | int | 0 |if number of training pixels is less then min, do not take this class into account |
| b | band | unsigned short | |band index (starting from 0, either use band option or use start to end) |
| sband | startband | unsigned short | |Start band sequence number |
| eband | endband | unsigned short | |End band sequence number |
| offset | offset | double | 0 |offset value for each spectral band input features: refl[band]=(DN[band]-offset[band])/scale[band] |
| scale | scale | double | 0 |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) |
| svmt | svmtype | std::string | C_SVC |type of SVM (C_SVC, nu_SVC,one_class, epsilon_SVR, nu_SVR) |
| kt | kerneltype | std::string | radial |type of kernel function (linear,polynomial,radial,sigmoid) |
| kd | kd | unsigned short | 3 |degree in kernel function |
| c0 | coef0 | float | 0 |coef0 in kernel function |
| nu | nu | float | 0.5 |the parameter nu of nu-SVC, one-class SVM, and nu-SVR |
| eloss | eloss | float | 0.1 |the epsilon in loss function of epsilon-SVR |
| cache | cache | int | 100 |cache memory size in MB |
| etol | etol | float | 0.001 |the tolerance of termination criterion |
| shrink | shrink | bool | false |whether to use the shrinking heuristics |
| pe | probest | bool | true |whether to train a SVC or SVR model for probability estimates |
| cv | cv | unsigned short | 2 |n-fold cross validation mode |
| cf | cf | bool | false |use Overall Accuracy instead of kappa |
| maxit | maxit | unsigned int | 500 |maximum number of iterations |
| tol | tolerance | double | 0.0001 |relative tolerance for stopping criterion |
| c | class | std::string | |list of class names. |
| r | reclass | short | |list of class values (use same order as in class opt). |
Usage: pkoptsvm -t training
**/
using namespace std;
#define Malloc(type,n) (type *)malloc((n)*sizeof(type))
//declare objective function
double objFunction(const std::vector<double> &x, std::vector<double> &grad, void *my_func_data);
//global parameters used in objective function
map<string,short> classValueMap;
vector<std::string> nameVector;
vector<unsigned int> nctraining;
vector<unsigned int> nctest;
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> coef0_opt("c0", "coef0", "coef0 in kernel function",0);
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> costfunction_opt("cf", "cf", "use Overall Accuracy instead of kappa",false);
// Optionpk<bool> weight_opt("wi", "wi", "set the parameter C of class i to weight*C, for C-SVC",true);
Optionpk<unsigned short> cv_opt("cv", "cv", "n-fold cross validation mode",2);
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", "use 1 to output intermediate results for plotting",0,2);
double objFunction(const std::vector<double> &x, std::vector<double> &grad, void *my_func_data){
assert(grad.empty());
vector<Vector2d<float> > *tf=reinterpret_cast<vector<Vector2d<float> >*> (my_func_data);
float ccost=x[0];
float gamma=x[1];
double error=1.0/epsilon_tol_opt[0];
double kappa=1.0;
double oa=1.0;
CostFactorySVM costfactory(svm_type_opt[0], kernel_type_opt[0], kernel_degree_opt[0], gamma, coef0_opt[0], ccost, nu_opt[0], epsilon_loss_opt[0], cache_opt[0], epsilon_tol_opt[0], shrinking_opt[0], prob_est_opt[0], cv_opt[0], verbose_opt[0]);
assert(tf->size());
// if(nctest>0)
// costfactory.setCv(0);
costfactory.setCv(cv_opt[0]);
if(classname_opt.size()){
assert(classname_opt.size()==classvalue_opt.size());
for(int iclass=0;iclass<classname_opt.size();++iclass)
costfactory.setClassValueMap(classname_opt[iclass],classvalue_opt[iclass]);
}
//set names in confusion matrix using nameVector
costfactory.setNameVector(nameVector);
// vector<string> nameVector=costfactory.getNameVector();
for(int iname=0;iname<nameVector.size();++iname){
if(costfactory.getClassValueMap().empty()){
costfactory.pushBackClassName(nameVector[iname]);
// cm.pushBackClassName(nameVector[iname]);
}
else if(costfactory.getClassIndex(type2string<short>((costfactory.getClassValueMap())[nameVector[iname]]))<0)
costfactory.pushBackClassName(type2string<short>((costfactory.getClassValueMap())[nameVector[iname]]));
}
costfactory.setNcTraining(nctraining);
costfactory.setNcTest(nctest);
kappa=costfactory.getCost(*tf);
return(kappa);
}
int main(int argc, char *argv[])
{
map<short,int> reclassMap;
vector<int> vreclass;
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).");
Optionpk<float> ccost_opt("cc", "ccost", "min and max boundaries the parameter C of C-SVC, epsilon-SVR, and nu-SVR (optional: initial value)",1);
Optionpk<float> gamma_opt("g", "gamma", "min max boundaries for gamma in kernel function (optional: initial value)",0);
Optionpk<double> stepcc_opt("stepcc","stepcc","multiplicative step for ccost in GRID search",2);
Optionpk<double> stepg_opt("stepg","stepg","multiplicative step for gamma in GRID search",2);
Optionpk<string> input_opt("i", "input", "input test vector file");
Optionpk<string> tlayer_opt("tln", "tln", "training layer name(s)");
Optionpk<string> label_opt("label", "label", "identifier for class label in training vector file.","label");
// Optionpk<unsigned short> reclass_opt("\0", "rc", "reclass code (e.g. --rc=12 --rc=23 to reclass first two classes to 12 and 23 resp.).", 0);
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", "in case of balance, randomize input data", true);
Optionpk<int> minSize_opt("min", "min", "if number of training pixels is less then min, do not take this class into account", 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<unsigned int> maxit_opt("maxit","maxit","maximum number of iterations",500);
//Optionpk<string> algorithm_opt("a", "algorithm", "GRID, or any optimization algorithm from http://ab-initio.mit.edu/wiki/index.php/NLopt_Algorithms","GRID");
Optionpk<double> tolerance_opt("tol","tolerance","relative tolerance for stopping criterion",0.0001);
input_opt.setHide(1);
tlayer_opt.setHide(1);
label_opt.setHide(1);
balance_opt.setHide(1);
random_opt.setHide(1);
minSize_opt.setHide(1);
band_opt.setHide(1);
bstart_opt.setHide(1);
bend_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);
cv_opt.setHide(1);
costfunction_opt.setHide(1);
maxit_opt.setHide(1);
tolerance_opt.setHide(1);
// algorithm_opt.setHide(1);
classname_opt.setHide(1);
classvalue_opt.setHide(1);
bool doProcess;//stop process when program was invoked with help option (-h --help)
try{
doProcess=training_opt.retrieveOption(argc,argv);
ccost_opt.retrieveOption(argc,argv);
gamma_opt.retrieveOption(argc,argv);
stepcc_opt.retrieveOption(argc,argv);
stepg_opt.retrieveOption(argc,argv);
input_opt.retrieveOption(argc,argv);
tlayer_opt.retrieveOption(argc,argv);
label_opt.retrieveOption(argc,argv);
balance_opt.retrieveOption(argc,argv);
random_opt.retrieveOption(argc,argv);
minSize_opt.retrieveOption(argc,argv);
band_opt.retrieveOption(argc,argv);
bstart_opt.retrieveOption(argc,argv);
bend_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);
cv_opt.retrieveOption(argc,argv);
costfunction_opt.retrieveOption(argc,argv);
maxit_opt.retrieveOption(argc,argv);
tolerance_opt.retrieveOption(argc,argv);
// algorithm_opt.retrieveOption(argc,argv);
classname_opt.retrieveOption(argc,argv);
classvalue_opt.retrieveOption(argc,argv);
verbose_opt.retrieveOption(argc,argv);
}
catch(string predefinedString){
std::cout << predefinedString << std::endl;
exit(0);
}
if(!doProcess){
cout << endl;
cout << "Usage: pkoptsvm -t training" << 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
}
assert(training_opt.size());
if(input_opt.size())
cv_opt[0]=0;
if(verbose_opt[0]>=1){
if(input_opt.size())
std::cout << "input filename: " << input_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 int totalSamples=0;
unsigned int totalTestSamples=0;
unsigned short nclass=0;
int nband=0;
int startBand=2;//first two bands represent X and Y pos
vector<double> offset;
vector<double> scale;
vector< Vector2d<float> > trainingPixels;//[class][sample][band]
vector< Vector2d<float> > testPixels;//[class][sample][band]
// if(priors_opt.size()>1){//priors from argument list
// priors.resize(priors_opt.size());
// double normPrior=0;
// for(int iclass=0;iclass<priors_opt.size();++iclass){
// priors[iclass]=priors_opt[iclass];
// normPrior+=priors[iclass];
// }
// //normalize
// for(int 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;//global variable for now (due to getCost)
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 -------------------------------
struct svm_problem prob;
vector<string> fields;
//organize training data
trainingPixels.clear();
testPixels.clear();
map<string,Vector2d<float> > trainingMap;
map<string,Vector2d<float> > testMap;
if(verbose_opt[0]>=1)
std::cout << "reading training file " << training_opt[0] << std::endl;
try{
ImgReaderOgr trainingReader(training_opt[0]);
if(band_opt.size()){
totalSamples=trainingReader.readDataImageOgr(trainingMap,fields,band_opt,label_opt[0],tlayer_opt,verbose_opt[0]);
if(input_opt.size()){
ImgReaderOgr inputReader(input_opt[0]);
totalTestSamples=inputReader.readDataImageOgr(testMap,fields,band_opt,label_opt[0],tlayer_opt,verbose_opt[0]);
inputReader.close();
}
}
else{
totalSamples=trainingReader.readDataImageOgr(trainingMap,fields,0,0,label_opt[0],tlayer_opt,verbose_opt[0]);
if(input_opt.size()){
ImgReaderOgr inputReader(input_opt[0]);
totalTestSamples=inputReader.readDataImageOgr(testMap,fields,0,0,label_opt[0],tlayer_opt,verbose_opt[0]);
inputReader.close();
}
trainingReader.close();
}
if(trainingMap.size()<2){
// map<string,Vector2d<float> >::iterator mapit=trainingMap.begin();
// while(mapit!=trainingMap.end())
// cerr << mapit->first << " -> " << classValueMap[mapit->first] << std::endl;
string errorstring="Error: could not read at least two classes from training input file";
throw(errorstring);
}
if(input_opt.size()&&testMap.size()<2){
string errorstring="Error: could not read at least two classes from test input file";
throw(errorstring);
}
}
catch(string error){
cerr << error << std::endl;
exit(1);
}
catch(...){
cerr << "error caught" << std::endl;
exit(1);
}
//todo delete class 0 ?
// if(verbose_opt[0]>=1)
// std::cout << "erasing class 0 from training set (" << trainingMap[0].size() << " from " << totalSamples << ") samples" << std::endl;
// totalSamples-=trainingMap[0].size();
// trainingMap.erase(0);
if(verbose_opt[0]>1)
std::cout << "training pixels: " << std::endl;
map<string,Vector2d<float> >::iterator mapit;
mapit=trainingMap.begin();
while(mapit!=trainingMap.end()){
if(classValueMap.size()){
//check if name in training is covered by classname_opt (values can not be 0)
if(classValueMap[mapit->first]>0){
if(verbose_opt[0])
std::cout << mapit->first << " -> " << classValueMap[mapit->first] << std::endl;
}
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);
}
}
//delete small classes
if((mapit->second).size()<minSize_opt[0]){
trainingMap.erase(mapit);
continue;
}
nameVector.push_back(mapit->first);
trainingPixels.push_back(mapit->second);
if(verbose_opt[0]>1)
std::cout << mapit->first << ": " << (mapit->second).size() << " samples" << std::endl;
// trainingPixels.push_back(mapit->second); ??
// ++iclass;
++mapit;
}
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();
mapit=testMap.begin();
while(mapit!=testMap.end()){
if(classValueMap.size()){
//check if name in test is covered by classname_opt (values can not be 0)
if(classValueMap[mapit->first]>0){
;//ok, no need to print to std::cout
}
else{
std::cerr << "Error: names in classname option are not complete, please check names in test vector and make sure classvalue is > 0" << std::endl;
exit(1);
}
}
//no need to delete small classes for test sample
testPixels.push_back(mapit->second);
if(verbose_opt[0]>1)
std::cout << mapit->first << ": " << (mapit->second).size() << " samples" << std::endl;
++mapit;
}
if(input_opt.size()){
assert(nclass==testPixels.size());
assert(nband=testPixels[0][0].size()-2);//X and Y//testPixels[0][0].size();
assert(!cv_opt[0]);
}
//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){
if(random_opt[0])
srand(time(NULL));
totalSamples=0;
for(int iclass=0;iclass<nclass;++iclass){
if(trainingPixels[iclass].size()>balance_opt[0]){
while(trainingPixels[iclass].size()>balance_opt[0]){
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[0];++isample){
int index = rand()%oldsize;
trainingPixels[iclass].push_back(trainingPixels[iclass][index]);
}
}
totalSamples+=trainingPixels[iclass].size();
}
assert(totalSamples==nclass*balance_opt[0]);
}
//no need to balance test sample
//set scale and offset
offset.resize(nband);
scale.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[iband]=(offset_opt.size()==1)?offset_opt[0]:offset_opt[iband];
scale[iband]=(scale_opt.size()==1)?scale_opt[0]:scale_opt[iband];
//search for min and maximum
if(scale[iband]<=0){
float theMin=trainingPixels[0][0][iband+startBand];
float theMax=trainingPixels[0][0][iband+startBand];
for(int 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[iband]=theMin+(theMax-theMin)/2.0;
scale[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[iband] << ", " << scale[iband] << std::endl;
std::cout << "scaled values for band " << iband << ": [" << (theMin-offset[iband])/scale[iband] << "," << (theMax-offset[iband])/scale[iband] << "]" << std::endl;
}
}
}
// if(priors_opt.size()==1){//default: equal priors for each class
// priors.resize(nclass);
// for(int iclass=0;iclass<nclass;++iclass)
// priors[iclass]=1.0/nclass;
// }
// assert(priors_opt.size()==1||priors_opt.size()==nclass);
if(verbose_opt[0]>=1){
std::cout << "number of bands: " << nband << std::endl;
std::cout << "number of classes: " << nclass << std::endl;
// std::cout << "priors:";
// for(int iclass=0;iclass<nclass;++iclass)
// std::cout << " " << priors[iclass];
// std::cout << std::endl;
}
//Calculate features of training (and test) set
nctraining.resize(nclass);
nctest.resize(nclass);
vector< Vector2d<float> > trainingFeatures(nclass);
for(int iclass=0;iclass<nclass;++iclass){
if(verbose_opt[0]>=1)
std::cout << "calculating features for class " << iclass << std::endl;
nctraining[iclass]=trainingPixels[iclass].size();
if(verbose_opt[0]>=1)
std::cout << "nctraining[" << iclass << "]: " << nctraining[iclass] << std::endl;
if(testPixels.size()>iclass){
nctest[iclass]=testPixels[iclass].size();
if(verbose_opt[0]>=1){
std::cout << "nctest[" << iclass << "]: " << nctest[iclass] << std::endl;
}
}
else
nctest[iclass]=0;
// trainingFeatures[iclass].resize(nctraining[iclass]);
trainingFeatures[iclass].resize(nctraining[iclass]+nctest[iclass]);
for(int isample=0;isample<nctraining[iclass];++isample){
//scale pixel values according to scale and offset!!!
for(int iband=0;iband<nband;++iband){
assert(trainingPixels[iclass].size()>isample);
assert(trainingPixels[iclass][isample].size()>iband+startBand);
assert(offset.size()>iband);
assert(scale.size()>iband);
float value=trainingPixels[iclass][isample][iband+startBand];
trainingFeatures[iclass][isample].push_back((value-offset[iband])/scale[iband]);
}
}
// assert(trainingFeatures[iclass].size()==nctraining[iclass]);
for(int isample=0;isample<nctest[iclass];++isample){
//scale pixel values according to scale and offset!!!
for(int iband=0;iband<nband;++iband){
assert(testPixels[iclass].size()>isample);
assert(testPixels[iclass][isample].size()>iband+startBand);
assert(offset.size()>iband);
assert(scale.size()>iband);
float value=testPixels[iclass][isample][iband+startBand];
// testFeatures[iclass][isample].push_back((value-offset[iband])/scale[iband]);
trainingFeatures[iclass][nctraining[iclass]+isample].push_back((value-offset[iband])/scale[iband]);
}
}
assert(trainingFeatures[iclass].size()==nctraining[iclass]+nctest[iclass]);
}
assert(ccost_opt.size()>1);//must have boundaries at least (initial value is optional)
if(ccost_opt.size()<3)//create initial value
ccost_opt.push_back(sqrt(ccost_opt[0]*ccost_opt[1]));
assert(gamma_opt.size()>1);//must have boundaries at least (initial value is optional)
if(gamma_opt.size()<3)//create initial value
gamma_opt.push_back(sqrt(gamma_opt[0]*gamma_opt[1]));//will be translated to 1.0/nFeatures
assert(ccost_opt.size()==3);//min, init, max
assert(gamma_opt.size()==3);//min, init, max
assert(gamma_opt[0]<gamma_opt[1]);
assert(gamma_opt[0]<gamma_opt[2]);
assert(gamma_opt[2]<gamma_opt[1]);
assert(ccost_opt[0]<ccost_opt[1]);
assert(ccost_opt[0]<ccost_opt[2]);
assert(ccost_opt[2]<ccost_opt[1]);
std::vector<double> x(2);
// if(algorithm_opt[0]=="GRID"){
if (1){
// double minError=1000;
// double minCost=0;
// double minGamma=0;
double maxKappa=0;
double maxCost=0;
double maxGamma=0;
const char* pszMessage;
void* pProgressArg=NULL;
GDALProgressFunc pfnProgress=GDALTermProgress;
double progress=0;
if(!verbose_opt[0])
pfnProgress(progress,pszMessage,pProgressArg);
double ncost=log(ccost_opt[1])/log(stepcc_opt[0])-log(ccost_opt[0])/log(stepcc_opt[0]);
double ngamma=log(gamma_opt[1])/log(stepg_opt[0])-log(gamma_opt[0])/log(stepg_opt[0]);
for(double ccost=ccost_opt[0];ccost<=ccost_opt[1];ccost*=stepcc_opt[0]){
for(double gamma=gamma_opt[0];gamma<=gamma_opt[1];gamma*=stepg_opt[0]){
x[0]=ccost;
x[1]=gamma;
std::vector<double> theGrad;
double kappa=0;
kappa=objFunction(x,theGrad,&trainingFeatures);
if(kappa>maxKappa){
maxKappa=kappa;
maxCost=ccost;
maxGamma=gamma;
}
if(verbose_opt[0])
std::cout << ccost << " " << gamma << " " << kappa<< std::endl;
progress+=1.0/ncost/ngamma;
if(!verbose_opt[0])
pfnProgress(progress,pszMessage,pProgressArg);
}
}
progress=1.0;
if(!verbose_opt[0])
pfnProgress(progress,pszMessage,pProgressArg);
x[0]=maxCost;
x[1]=maxGamma;
}
//else{
// nlopt::opt optimizer=OptFactory::getOptimizer(algorithm_opt[0],2);
// if(verbose_opt[0]>1)
// std::cout << "optimization algorithm: " << optimizer.get_algorithm_name() << "..." << std::endl;
// std::vector<double> lb(2);
// std::vector<double> init(2);
// std::vector<double> ub(2);
// lb[0]=ccost_opt[0];
// lb[1]=(gamma_opt[0]>0)? gamma_opt[0] : 1.0/trainingFeatures[0][0].size();
// init[0]=ccost_opt[2];
// init[1]=(gamma_opt[2]>0)? gamma_opt[1] : 1.0/trainingFeatures[0][0].size();
// ub[0]=ccost_opt[1];
// ub[1]=(gamma_opt[1]>0)? gamma_opt[1] : 1.0/trainingFeatures[0][0].size();
// // optimizer.set_min_objective(objFunction, &trainingFeatures);
// optimizer.set_max_objective(objFunction, &trainingFeatures);
// optimizer.set_lower_bounds(lb);
// optimizer.set_upper_bounds(ub);
// if(verbose_opt[0]>1)
// std::cout << "set stopping criteria" << std::endl;
// //set stopping criteria
// if(maxit_opt[0])
// optimizer.set_maxeval(maxit_opt[0]);
// else
// optimizer.set_xtol_rel(tolerance_opt[0]);
// double minf=0;
// x=init;
// try{
// optimizer.optimize(x, minf);
// }
// catch(string error){
// cerr << error << std::endl;
// exit(1);
// }
// catch (exception& e){
// cout << e.what() << endl;
// }
// catch(...){
// cerr << "error caught" << std::endl;
// exit(1);
// }
// double ccost=x[0];
// double gamma=x[1];
// if(verbose_opt[0])
// std::cout << "optimized with " << optimizer.get_algorithm_name() << "..." << std::endl;
//}
std::cout << " --ccost " << x[0];
std::cout << " --gamma " << x[1];
std::cout << std::endl;
}
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