FITC approx in testImagenetBinaryBruteForce

progs/testImageNetBinaryBruteForce.cpp

@@ -265,7 +265,7 @@ void inline trainGPSRMean(NICE::Vector & GPMeanRightPart, const double & noise,
   NICE::Matrix Kmn (indicesOfChosenExamples.size(), nrOfExamplesPerClass, 0.0);
   int rowCnt(0);
   //set every row
-  for (int i = 0; i < indicesOfChosenExamples.size(); i++, rowCnt++ )
+  for (uint i = 0; i < indicesOfChosenExamples.size(); i++, rowCnt++ )
   {
     //set every element of this row
     NICE::Vector col = kernelMatrix.getRow(indicesOfChosenExamples[i]);
@@ -278,9 +278,9 @@ void inline trainGPSRMean(NICE::Vector & GPMeanRightPart, const double & noise,
   //we could speed this up if we would order the indices
   NICE::Matrix Kmm (indicesOfChosenExamples.size(), indicesOfChosenExamples.size(), 0.0);
   double tmp(0.0);
-  for (int i = 0; i < indicesOfChosenExamples.size(); i++ )
+  for (uint i = 0; i < indicesOfChosenExamples.size(); i++ )
   {
-    for (int j = i; j < indicesOfChosenExamples.size(); j++ )
+    for (uint j = i; j < indicesOfChosenExamples.size(); j++ )
     {
       tmp = kernelMatrix(indicesOfChosenExamples[i], indicesOfChosenExamples[j]);
       Kmm(i,j) = tmp;
@@ -346,7 +346,7 @@ void inline trainGPSRVar(NICE::Matrix & choleskyMatrix, const double & noise, co
   NICE::Matrix Kmn (indicesOfChosenExamples.size(), nrOfExamplesPerClass, 0.0);
   int rowCnt(0);
   //set every row
-  for (int i = 0; i < indicesOfChosenExamples.size(); i++, rowCnt++ )
+  for (uint i = 0; i < indicesOfChosenExamples.size(); i++, rowCnt++ )
   {
     //set every element of this row
     NICE::Vector col = kernelMatrix.getRow(indicesOfChosenExamples[i]);
@@ -359,9 +359,9 @@ void inline trainGPSRVar(NICE::Matrix & choleskyMatrix, const double & noise, co
   //we could speed this up if we would order the indices
   NICE::Matrix Kmm (indicesOfChosenExamples.size(), indicesOfChosenExamples.size(), 0.0);
   double tmp(0.0);
-  for (int i = 0; i < indicesOfChosenExamples.size(); i++ )
+  for (uint i = 0; i < indicesOfChosenExamples.size(); i++ )
   {
-    for (int j = i; j < indicesOfChosenExamples.size(); j++ )
+    for (uint j = i; j < indicesOfChosenExamples.size(); j++ )
     {
       tmp = kernelMatrix(indicesOfChosenExamples[i], indicesOfChosenExamples[j]);
       Kmm(i,j) = tmp;
@@ -779,7 +779,7 @@ void inline evaluateGPVarApprox(const NICE::Vector & kernelVector, const double
       // uncertainty = k{**} - \k_*^T \cdot D^{-1} \cdot k_*  where D is our nice approximation of K
       
       NICE::Vector rightPart (kernelVector.size());
-      for (int j = 0; j < kernelVector.size(); j++)
+      for (uint j = 0; j < kernelVector.size(); j++)
       {
         rightPart[j] = kernelVector[j] * matrixDInv[j];
       }
@@ -1037,7 +1037,7 @@ void inline evaluateGPOptVar(const NICE::Vector & kernelVector, const double & k
       // uncertainty = k{**} - \k_*^T \cdot D^{-1} \cdot k_*  where D is our nice approximation of K
       
       NICE::Vector rightPart (kernelVector.size());
-      for (int j = 0; j < kernelVector.size(); j++)
+      for (uint j = 0; j < kernelVector.size(); j++)
       {
         rightPart[j] = kernelVector[j] * matrixDInv[j];
       }
@@ -1392,8 +1392,8 @@ int main (int argc, char **argv)
     kernelSigmaGPMean = sigmaGPMeanParas[cl];
     kernelSigmaGPSRMean = sigmaGPSRMeanParas[cl];
     kernelSigmaGPSRVar = sigmaGPSRVarParas[cl];
-    kernelSigmaGPSRMean = sigmaGPFITCMeanParas[cl];
-    kernelSigmaGPSRVar = sigmaGPFITCVarParas[cl];    
+    kernelSigmaGPFITCMean = sigmaGPFITCMeanParas[cl];
+    kernelSigmaGPFITCVar = sigmaGPFITCVarParas[cl];    
     kernelSigmaGPOptMean = sigmaGPOptMeanParas[cl];
     kernelSigmaGPOptVar = sigmaGPOptVarParas[cl];    
     kernelSigmaParzen = sigmaParzenParas[cl];
@@ -1447,10 +1447,11 @@ int main (int argc, char **argv)
     if (GPVar)
       trainGPVar(GPVarCholesky, noiseGPVarParas[cl], kernelMatrix, nrOfExamplesPerClass, cl, runsPerClassToAverageTraining );       
     
+    int nrOfRegressors (0);
     //train GP SR Mean
     NICE::Vector GPSRMeanRightPart;
     std::vector<int> indicesOfChosenExamplesGPSRMean;
-    int nrOfRegressors = conf.gI( "GPSR", "nrOfRegressors", nrOfExamplesPerClass/2);
+    nrOfRegressors = conf.gI( "GPSR", "nrOfRegressors", nrOfExamplesPerClass/2);
     nrOfRegressors = std::min( nrOfRegressors, nrOfExamplesPerClass );
     if (GPSRMean)
       trainGPSRMean(GPSRMeanRightPart, noiseGPSRMeanParas[cl], kernelMatrix, nrOfExamplesPerClass, cl, runsPerClassToAverageTraining, nrOfRegressors, indicesOfChosenExamplesGPSRMean );        
@@ -1464,7 +1465,7 @@ int main (int argc, char **argv)
     //train GP FITC Mean
     NICE::Vector GPFITCMeanRightPart;
     std::vector<int> indicesOfChosenExamplesGPFITCMean;
-    int nrOfRegressors = conf.gI( "GPFITC", "nrOfRegressors", nrOfExamplesPerClass/5);
+    nrOfRegressors = conf.gI( "GPFITC", "nrOfRegressors", nrOfExamplesPerClass/5);
     nrOfRegressors = std::min( nrOfRegressors, nrOfExamplesPerClass );
     if (GPFITCMean)
       trainGPFITCMean(GPFITCMeanRightPart, noiseGPFITCMeanParas[cl], kernelMatrix, nrOfExamplesPerClass, cl, runsPerClassToAverageTraining, nrOfRegressors, indicesOfChosenExamplesGPFITCMean );