SingleGP.cpp
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00001 /*
00002  * SingleGP.cpp
00003  *
00004  *  Created on: Feb 10, 2010
00005  *      Author: sturm
00006  */
00007 
00008 
00009 #include "gaussian_process/SingleGP.h"
00010 
00011 namespace gaussian_process {
00012 
00013 #define sqr(a) ((a)*(a))
00014 
00015 SingleGP::SingleGP(CovFuncND initialCovFunc,double initialSigmaNoise)
00016 :
00017         initialCovFunc(initialCovFunc), initialSigmaNoise(initialSigmaNoise),
00018         covFunc(initialCovFunc), sigmaNoise(initialSigmaNoise),
00019         GP(&covFunc, &sigmaNoise)
00020 {
00021         GP.m_dataPoints = &dataPoints;
00022         GP.m_t = &targetPoints;
00023         GP.m_numDataPoints = 0;
00024 
00025 }
00026 
00027 SingleGP::~SingleGP() {
00028         GP.m_dataPoints = NULL;
00029         GP.m_t = NULL;
00030 }
00031 
00032 void SingleGP::Reset() {
00033         dataPoints.clear();
00034         targetPoints.clear();
00035         covFunc = initialCovFunc;
00036         sigmaNoise = initialSigmaNoise;
00037         GP.m_numDataPoints = 0;
00038 }
00039 
00040 void SingleGP::SetData(TVector<TDoubleVector> &dataPoints,TVector<double> &targetPoints) {
00041         this->dataPoints = dataPoints;
00042         this->targetPoints = targetPoints;
00043         GP.m_numDataPoints = dataPoints.size();
00044         GP.m_dataPoints = &this->dataPoints;
00045         GP.m_t = &this->targetPoints;
00046         BuildGP();
00047 }
00048 
00049 void SingleGP::BuildGP() {
00050         if(GP.m_numDataPoints ==0) {
00051                 return;
00052         }
00053 
00054         if(dataPoints[0].size() == 0) {
00055                 double sum = 0;
00056                 for(unsigned int i=0;i<dataPoints.size();i++) {
00057                         sum += targetPoints[i];
00058                 }
00059                 mean = sum/dataPoints.size();
00060 
00061                 sum = 0;
00062                 for(unsigned int i=0;i<dataPoints.size();i++) {
00063                         sum += sqr(targetPoints[i]-mean);
00064                 }
00065                 var = sum/dataPoints.size();
00066                 sum = 0;
00067                 return;
00068         } else {
00069                 GP.buildGP(true);       // build iCov and targets
00070         }
00071 }
00072 
00073 void SingleGP::OptimizeGP() {
00074         if(GP.m_numDataPoints ==0) return;
00075 
00076         if(dataPoints[0].size() != 0) {
00077 
00078                 if(!GP.minimizeGSL(100)) {
00079                         covFunc = initialCovFunc;
00080                         sigmaNoise = initialSigmaNoise;
00081 //                      cout << "minimize failed, resetting values"<<endl;
00082                 } else {
00083                         return; // done!
00084                 }
00085 
00086         }
00087         BuildGP();
00088 }
00089 
00090 void SingleGP::Evaluate(TDoubleVector data, double &targetMean, double &targetVar) {
00091         targetMean = NAN;
00092         targetVar = NAN;
00093         if(GP.m_numDataPoints ==0) return;
00094 
00095         if(dataPoints[0].size() == 0) {
00096                 targetMean = mean;
00097                 targetVar = var;
00098         } else {
00099                 //cout << "datapoitns="<<GP.m_numDataPoints<<endl;
00100                 GP.evalGP(data, targetMean, targetVar);
00101         }
00102 }
00103 
00104 double SingleGP::GetDataLikelihood() {
00105         if(GP.m_numDataPoints ==0) return(NAN);
00106 
00107         if(dataPoints[0].size() == 0) {
00108                 return(NAN);
00109         }
00110 
00111         return(GP.getDataLikelihood());
00112 }
00113 
00114 }
00115 
00116 #include "gaussian_process/gpRegression.hxx"
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gaussian_process
Author(s): Maintained by Juergen Sturm
autogenerated on Wed Dec 26 2012 15:34:14