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sebastic-guest pushed a commit to branch upstream-master
in repository pktools.
commit d5c90b1a3b2dab6e91a2756c65e8eff02b9f9634
Author: Pieter Kempeneers <kempe...@gmail.com>
Date: Sun Nov 24 11:07:28 2013 +0100
floating point for accuracy in pkdiff.cc
---
ChangeLog | 10 +++--
src/apps/pkclassify_nn.cc | 26 ++++++++++---
src/apps/pkclassify_svm.cc | 9 ++---
src/apps/pkdiff.cc | 91 ++++++++++++++++++++++++---------------------
src/apps/pkfs_nn.cc | 48 +++++++++++++++++++-----
src/apps/pkfs_svm.cc | 2 +-
src/apps/pkregression_nn.cc | 21 +++++++++--
7 files changed, 137 insertions(+), 70 deletions(-)
diff --git a/ChangeLog b/ChangeLog
index ae3af38..c5ac434 100755
--- a/ChangeLog
+++ b/ChangeLog
@@ -119,14 +119,16 @@ version 2.4.1
version 2.4.2
- general
removed using namespace std from header files
- - PosValue.h
- remove using namespace std;
+ - clean up of using namespace std in header files
- apps/Makefile.am
add GSL_LIBS to AM_LDFLAGS and LDADD
todo: remove redundancy in AM_LDFLAGS and LDADD
- - clean up of using namespace std in header files
+ - PosValue.h
+ remove using namespace std;
- FileReaderAscii
corrected error for constructor with fieldseparator argument
+ - pkdiff
+ introduced short option -m for mask
- pkinfo
bug fixed with -min and -max in hist (thanks to Giuseppe Amatuli for
noticing)
- pkfilter
@@ -145,10 +147,12 @@ version 2.4.2
- pkclassify_nn.h
support reading ogr files with single feature (one band only: "B" or
"Band")
- pkclassify_nn.cc
+ changed create_sparse to create_spare_array due to error in FANN...
option -n for number of neurons
cross validation parameter can not be 1
- pkfs_nn.cc
cross validation parameter can not be 1
+ solved some bugs
- pkclassify_svm.cc
cross validation parameter can not be 1
- pkopt_svm.cc
diff --git a/src/apps/pkclassify_nn.cc b/src/apps/pkclassify_nn.cc
index e3091f0..4946700 100644
--- a/src/apps/pkclassify_nn.cc
+++ b/src/apps/pkclassify_nn.cc
@@ -417,19 +417,35 @@ int main(int argc, char *argv[])
for(int ilayer=0;ilayer<nneuron_opt.size();++ilayer)
cout << nneuron_opt[ilayer] << " ";
cout << "neurons" << endl;
- //test
cout << "connection_opt[0]: " << connection_opt[0] << std::endl;
cout << "num_layers: " << num_layers << std::endl;
cout << "nFeatures: " << nFeatures << std::endl;
cout << "nneuron_opt[0]: " << nneuron_opt[0] << std::endl;
+ cout << "number of classes (nclass): " << nclass << std::endl;
}
switch(num_layers){
- case(3):
- net[ibag].create_sparse(connection_opt[0],num_layers, nFeatures,
nneuron_opt[0], nclass);
+ case(3):{
+ // net[ibag].create_sparse(connection_opt[0],num_layers, nFeatures,
nneuron_opt[0], nclass);//replace all create_sparse with create_sparse_array
due to bug in FANN!
+ unsigned int layers[3];
+ layers[0]=nFeatures;
+ layers[1]=nneuron_opt[0];
+ layers[2]=nclass;
+ net[ibag].create_sparse_array(connection_opt[0],num_layers,layers);
break;
- case(4):
- net[ibag].create_sparse(connection_opt[0],num_layers, nFeatures,
nneuron_opt[0], nneuron_opt[1], nclass);
+ }
+ case(4):{
+ unsigned int layers[4];
+ layers[0]=nFeatures;
+ layers[1]=nneuron_opt[0];
+ layers[2]=nneuron_opt[1];
+ layers[3]=nclass;
+ // layers.push_back(nFeatures);
+ // for(int ihidden=0;ihidden<nneuron_opt.size();++ihidden)
+ // layers.push_back(nneuron_opt[ihidden]);
+ // layers.push_back(nclass);
+ net[ibag].create_sparse_array(connection_opt[0],num_layers,layers);
break;
+ }
default:
cerr << "Only 1 or 2 hidden layers are supported!" << endl;
exit(1);
diff --git a/src/apps/pkclassify_svm.cc b/src/apps/pkclassify_svm.cc
index ea68208..2205a2b 100644
--- a/src/apps/pkclassify_svm.cc
+++ b/src/apps/pkclassify_svm.cc
@@ -541,8 +541,8 @@ int main(int argc, char *argv[])
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_pct(&se95_oa);
- std::cout << "Overall Accuracy: " << doa << " (" << se95_oa << ")" <<
std::endl;
+ doa=cm.oa(&se95_oa);
+ std::cout << "Overall Accuracy: " << 100*doa << " (" << 100*se95_oa << ")"
<< std::endl;
}
//--------------------------------- end of training
-----------------------------------
@@ -732,7 +732,6 @@ int main(int argc, char *argv[])
exit(3);
}
}
-
//process per pixel
for(int icol=0;icol<ncol;++icol){
assert(hpixel[icol].size()==nband);
@@ -1119,8 +1118,8 @@ int main(int argc, char *argv[])
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_pct(&se95_oa);
- std::cout << "Overall Accuracy: " << doa << " (" << se95_oa << ")" <<
std::endl;
+ doa=cm.oa(&se95_oa);
+ std::cout << "Overall Accuracy: " << 100*doa << " (" << 100*se95_oa <<
")" << std::endl;
}
}
try{
diff --git a/src/apps/pkdiff.cc b/src/apps/pkdiff.cc
index 7a123d8..e8c7036 100644
--- a/src/apps/pkdiff.cc
+++ b/src/apps/pkdiff.cc
@@ -27,16 +27,17 @@ along with pktools. If not, see
<http://www.gnu.org/licenses/>.
int main(int argc, char *argv[])
{
- Optionpk<string> input_opt("i", "input", "Input image file.", "");
- Optionpk<string> reference_opt("r", "reference", "Reference image file", "");
- Optionpk<string> output_opt("o", "output", "Output image file. Default is
empty: no output image, only report difference or identical.", "");
- Optionpk<string> mask_opt("\0", "mask", "Mask image file. A single mask is
supported only, but several mask values can be used. See also mflag option.
(default is empty)", "");
- Optionpk<string> colorTable_opt("\0", "ct", "color table (file with 5
columns: id R G B ALFA (0: transparent, 255: solid)", "");
+ Optionpk<string> input_opt("i", "input", "Input image file.");
+ Optionpk<string> reference_opt("r", "reference", "Reference image file");
+ Optionpk<string> output_opt("o", "output", "Output image file. Default is
empty: no output image, only report difference or identical.");
+ Optionpk<string> mask_opt("m", "mask", "Mask image file. A single mask is
supported only, but several mask values can be used. See also mflag option.
(default is empty)");
+ Optionpk<string> colorTable_opt("ct", "ct", "color table (file with 5
columns: id R G B ALFA (0: transparent, 255: solid)", "");
Optionpk<short> valueE_opt("\0", "correct", "Value for correct pixels (0)",
0);
Optionpk<short> valueO_opt("\0", "omission", "Value for omission errors:
input label > reference label (default value is 1)", 1);
Optionpk<short> valueC_opt("\0", "commission", "Value for commission errors:
input label < reference label (default value is 2)", 2);
Optionpk<short> flag_opt("f", "flag", "No value flag(s)", 0);
- Optionpk<short> mflag_opt("m", "mflag", "Mask value(s) for invalid data
(positive value), or for valid data (negative value). Default is 0", 0);
+ Optionpk<int> invalid_opt("t", "invalid", "Mask value(s) where image is
invalid. Use negative value for valid data (example: use -t -1: if only -1 is
valid value)", 0);
+ // Optionpk<short> mflag_opt("t", "mflag", "Mask value(s) for invalid data
(positive value), or for valid data (negative value). Default is 0", 0);
Optionpk<short> band_opt("b", "band", "Band to extract (0)", 0);
Optionpk<bool> confusion_opt("cm", "confusion", "create confusion matrix (to
std out) (default value is 0)", false);
Optionpk<short> lzw_opt("\0", "lzw", "compression (default value is 1)", 1);
@@ -63,7 +64,7 @@ int main(int argc, char *argv[])
valueO_opt.retrieveOption(argc,argv);
valueC_opt.retrieveOption(argc,argv);
flag_opt.retrieveOption(argc,argv);
- mflag_opt.retrieveOption(argc,argv);
+ invalid_opt.retrieveOption(argc,argv);
band_opt.retrieveOption(argc,argv);
confusion_opt.retrieveOption(argc,argv);
lzw_opt.retrieveOption(argc,argv);
@@ -95,8 +96,12 @@ int main(int argc, char *argv[])
cout << " " << flag_opt[iflag];
cout << endl;
}
- if(mask_opt[0]!="")
- assert(mask_opt.size()==input_opt.size());
+
+ assert(input_opt.size());
+ assert(reference_opt.size());
+ if(mask_opt.size())
+ while(mask_opt.size()<input_opt.size())
+ mask_opt.push_back(mask_opt[0]);
vector<short> inputRange;
vector<short> referenceRange;
ConfusionMatrix cm;
@@ -185,7 +190,7 @@ int main(int argc, char *argv[])
pfnProgress(progress,pszMessage,pProgressArg);
if(reference_opt[0].find(".shp")!=string::npos){
for(int iinput=0;iinput<input_opt.size();++iinput){
- if(output_opt[0]!="")
+ if(output_opt.size())
assert(reference_opt.size()==output_opt.size());
for(int iref=0;iref<reference_opt.size();++iref){
if(verbose_opt[0])
@@ -194,7 +199,7 @@ int main(int argc, char *argv[])
ImgReaderOgr referenceReader;
try{
inputReader.open(input_opt[iinput]);//,imagicX_opt[0],imagicY_opt[0]);
- if(mask_opt[0]!=""){
+ if(mask_opt.size()){
maskReader.open(mask_opt[iinput]);
assert(inputReader.nrOfCol()==maskReader.nrOfCol());
assert(inputReader.nrOfRow()==maskReader.nrOfRow());
@@ -210,7 +215,7 @@ int main(int argc, char *argv[])
ImgWriterOgr ogrWriter;
OGRLayer *writeLayer;
- if(output_opt[0]!=""){
+ if(output_opt.size()){
if(verbose_opt[0])
cout << "creating output vector file " << output_opt[0] << endl;
assert(output_opt[0].find(".shp")!=string::npos);
@@ -322,7 +327,7 @@ int main(int argc, char *argv[])
if(static_cast<int>(i_centre)<0||static_cast<int>(i_centre)>=inputReader.nrOfCol())
continue;
OGRFeature *writeFeature;
- if(output_opt[0]!=""){
+ if(output_opt.size()){
writeFeature =
OGRFeature::CreateFeature(writeLayer->GetLayerDefn());
if(verbose_opt[0])
cout << "copying fields from " << reference_opt[0] << endl;
@@ -359,18 +364,18 @@ int main(int argc, char *argv[])
break;
}
}
- maskFlagged=false;//(mflag_opt[ivalue]>=0)?false:true;
- if(mask_opt[0]!=""){
+ maskFlagged=false;//(invalid_opt[ivalue]>=0)?false:true;
+ if(mask_opt.size()){
maskReader.readData(maskValue,GDT_Int16,i,j,band_opt[0]);
- for(int ivalue=0;ivalue<mflag_opt.size();++ivalue){
- if(mflag_opt[ivalue]>=0){//values set in mflag_opt are
invalid
- if(maskValue==mflag_opt[ivalue]){
+ for(int ivalue=0;ivalue<invalid_opt.size();++ivalue){
+ if(invalid_opt[ivalue]>=0){//values set in invalid_opt are
invalid
+ if(maskValue==invalid_opt[ivalue]){
maskFlagged=true;
break;
}
}
- else{//only values set in mflag_opt are valid
- if(maskValue!=-mflag_opt[ivalue])
+ else{//only values set in invalid_opt are valid
+ if(maskValue!=-invalid_opt[ivalue])
maskFlagged=true;
else{
maskFlagged=false;
@@ -393,7 +398,7 @@ int main(int argc, char *argv[])
//flag if not all pixels are homogeneous or if at least one pixel
flagged
if(!windowHasFlag&&isHomogeneous){
- if(output_opt[0]!="")
+ if(output_opt.size())
writeFeature->SetField(labelclass_opt[0].c_str(),static_cast<int>(inputValue));
if(confusion_opt[0]){
++ntotalValidation;
@@ -445,7 +450,7 @@ int main(int argc, char *argv[])
fs << labelclass_opt[0] << "_" << windowJ << "_" <<
windowI;
else
fs << labelclass_opt[0];
- if(output_opt[0]!="")
+ if(output_opt.size())
writeFeature->SetField(fs.str().c_str(),static_cast<int>(inputValue));
if(!windowJ&&!windowI){//centre pixel
if(confusion_opt[0]){
@@ -486,7 +491,7 @@ int main(int argc, char *argv[])
}
}
}
- if(output_opt[0]!=""){
+ if(output_opt.size()){
if(!windowAllFlagged){
if(verbose_opt[0])
cout << "creating feature" << endl;
@@ -498,11 +503,11 @@ int main(int argc, char *argv[])
OGRFeature::DestroyFeature( writeFeature );
}
}
- if(output_opt[0]!="")
+ if(output_opt.size())
ogrWriter.close();
referenceReader.close();
inputReader.close();
- if(mask_opt[0]!="")
+ if(mask_opt.size())
maskReader.close();
}
}
@@ -511,9 +516,9 @@ int main(int argc, char *argv[])
ImgWriterGdal imgWriter;
try{
inputReader.open(input_opt[0]);//,imagicX_opt[0],imagicY_opt[0]);
- if(mask_opt[0]!="")
+ if(mask_opt.size())
maskReader.open(mask_opt[0]);
- if(output_opt[0]!=""){
+ if(output_opt.size()){
if(verbose_opt[0])
cout << "opening output image " << output_opt[0] << endl;
string compression=(lzw_opt[0])? "LZW":"NONE";
@@ -547,7 +552,7 @@ int main(int argc, char *argv[])
vector<short> lineInput(inputReader.nrOfCol());
vector<short> lineMask(maskReader.nrOfCol());
vector<short> lineOutput;
- if(output_opt[0]!="")
+ if(output_opt.size())
lineOutput.resize(inputReader.nrOfCol());
int irow=0;
@@ -581,7 +586,7 @@ int main(int argc, char *argv[])
cout << referenceRange[rc] << endl;
}
if(referenceRange.size()!=inputRange.size()){
- if(confusion_opt[0]||output_opt[0]!=""){
+ if(confusion_opt[0]||output_opt.size()){
cout << "reference range is not equal to input range!" << endl;
cout << "Kappa: " << 0 << endl;
cout << "total weighted: " << 0 << endl;
@@ -595,8 +600,8 @@ int main(int argc, char *argv[])
for(irow=0;irow<inputReader.nrOfRow()&&!isDifferent;++irow){
//read line in lineInput, lineReference and lineMask
inputReader.readData(lineInput,GDT_Int16,irow,band_opt[0]);
- if(mask_opt[0]!="")
- maskReader.readData(lineMask,GDT_Int16,irow,band_opt[0]);
+ if(mask_opt.size())
+ maskReader.readData(lineMask,GDT_Int16,irow);
double x,y;//geo coordinates
double ireference,jreference;//image coordinates in reference image
for(icol=0;icol<inputReader.nrOfCol();++icol){
@@ -624,15 +629,15 @@ int main(int argc, char *argv[])
bool flagged=false;
for(int iflag=0;iflag<flag_opt.size();++iflag){
if((lineInput[icol]==flag_opt[iflag])||(lineReference[ireference]==flag_opt[iflag])){
- if(output_opt[0]!="")
+ if(output_opt.size())
lineOutput[icol]=flag_opt[iflag];
flagged=true;
break;
}
}
- if(mask_opt[0]!=""){
- for(int ivalue=0;ivalue<mflag_opt.size();++ivalue){
- if(lineMask[icol]==mflag_opt[ivalue]){
+ if(mask_opt.size()){
+ for(int ivalue=0;ivalue<invalid_opt.size();++ivalue){
+ if(lineMask[icol]==invalid_opt[ivalue]){
flagged=true;
break;
}
@@ -652,7 +657,7 @@ int main(int argc, char *argv[])
cm.incrementResult(cm.getClass(rc),cm.getClass(ic),1);
}
if(lineInput[icol]==lineReference[ireference]){//correct
- if(output_opt[0]!=""){
+ if(output_opt.size()){
if(valueE_opt[0]!=flag_opt[0])
lineOutput[icol]=valueE_opt[0];
else
@@ -664,7 +669,7 @@ int main(int argc, char *argv[])
isDifferent=true;
break;
}
- if(output_opt[0]!=""){
+ if(output_opt.size()){
if(lineInput[icol]<20){//forest
if(lineReference[icol]>=20)//gain
lineOutput[icol]=lineInput[icol]*10+1;//GAIN is 111,121,131
@@ -685,11 +690,11 @@ int main(int argc, char *argv[])
}
else{
++nflagged;
- if(output_opt[0]!="")
+ if(output_opt.size())
lineOutput[icol]=flag_opt[0];
}
}
- if(output_opt[0]!=""){
+ if(output_opt.size()){
try{
imgWriter.writeData(lineOutput,GDT_Int16,irow);
}
@@ -709,7 +714,7 @@ int main(int argc, char *argv[])
if(!verbose_opt[0])
pfnProgress(progress,pszMessage,pProgressArg);
}
- if(output_opt[0]!="")
+ if(output_opt.size())
imgWriter.close();
else if(!confusion_opt[0]){
if(isDifferent)
@@ -719,7 +724,7 @@ int main(int argc, char *argv[])
}
referenceReader.close();
inputReader.close();
- if(mask_opt[0]!="")
+ if(mask_opt.size())
maskReader.close();
}
@@ -796,8 +801,8 @@ int main(int argc, char *argv[])
dpa=cm.pa_pct(classNames[iclass],&se95_pa);
cout << cm.getClass(iclass) << " " << cm.nReference(cm.getClass(iclass))
<< " " << dua << " (" << se95_ua << ")" << " " << dpa << " (" << se95_pa << ")"
<< endl;
}
- doa=cm.oa_pct(&se95_oa);
+ doa=cm.oa(&se95_oa);
cout << "Kappa: " << cm.kappa() << endl;
- cout << "Overall Accuracy: " << doa << " (" << se95_oa << ")" << endl;
+ cout << "Overall Accuracy: " << 100*doa << " (" << 100*se95_oa << ")" <<
endl;
}
}
diff --git a/src/apps/pkfs_nn.cc b/src/apps/pkfs_nn.cc
index 2fbca98..53928ff 100644
--- a/src/apps/pkfs_nn.cc
+++ b/src/apps/pkfs_nn.cc
@@ -82,12 +82,25 @@ double getCost(const vector<Vector2d<float> >
&trainingFeatures)
cout << "neurons" << endl;
}
switch(num_layers){
- case(3):
- net.create_sparse(connection_opt[0],num_layers, nFeatures, nneuron_opt[0],
nclass);
+ case(3):{
+ unsigned int layers[3];
+ layers[0]=nFeatures;
+ layers[1]=nneuron_opt[0];
+ layers[2]=nclass;
+ net.create_sparse_array(connection_opt[0],num_layers,layers);
+ // net.create_sparse(connection_opt[0],num_layers, nFeatures,
nneuron_opt[0], nclass);
break;
- case(4):
- net.create_sparse(connection_opt[0],num_layers, nFeatures, nneuron_opt[0],
nneuron_opt[1], nclass);
+ }
+ case(4):{
+ unsigned int layers[4];
+ layers[0]=nFeatures;
+ layers[1]=nneuron_opt[0];
+ layers[2]=nneuron_opt[1];
+ layers[3]=nclass;
+ net.create_sparse_array(connection_opt[0],num_layers,layers);
+ // net.create_sparse(connection_opt[0],num_layers, nFeatures,
nneuron_opt[0], nneuron_opt[1], nclass);
break;
+ }
default:
cerr << "Only 1 or 2 hidden layers are supported!" << endl;
exit(1);
@@ -95,7 +108,6 @@ double getCost(const vector<Vector2d<float> >
&trainingFeatures)
}
net.set_learning_rate(learning_opt[0]);
-
// net.set_activation_steepness_hidden(1.0);
// net.set_activation_steepness_output(1.0);
@@ -116,8 +128,9 @@ double getCost(const vector<Vector2d<float> >
&trainingFeatures)
else
if(cm.getClassIndex(type2string<short>(classValueMap[nameVector[iname]]))<0)
cm.pushBackClassName(type2string<short>(classValueMap[nameVector[iname]]));
}
- vector<Vector2d<float> > tmpFeatures;
+ vector<Vector2d<float> > tmpFeatures(nclass);
for(int iclass=0;iclass<nclass;++iclass){
+ tmpFeatures[iclass].resize(trainingFeatures[iclass].size(),nFeatures);
for(unsigned int isample=0;isample<nctraining[iclass];++isample){
for(int ifeature=0;ifeature<nFeatures;++ifeature){
tmpFeatures[iclass][isample][ifeature]=trainingFeatures[iclass][isample][ifeature];
@@ -142,8 +155,14 @@ double getCost(const vector<Vector2d<float> >
&trainingFeatures)
cm.incrementResult(cm.getClass(referenceVector[isample]),cm.getClass(outputVector[isample]),1.0);
}
}
- else{
+ else{//not working yet. please repair...
+ assert(cv_opt[0]>0);
bool initWeights=true;
+ //test
+ cout << "tempFeatures.size(): " << tmpFeatures.size() << endl;
+ cout << "ntraining: " << ntraining << endl;
+ cout << "initWeights: " << initWeights << endl;
+ cout << "maxit_opt.size(): " << maxit_opt.size() << endl;
net.train_on_data(tmpFeatures,ntraining,initWeights, maxit_opt[0],
iterations_between_reports, desired_error);
vector<Vector2d<float> > testFeatures(nclass);
@@ -155,6 +174,8 @@ double getCost(const vector<Vector2d<float> >
&trainingFeatures)
for(int ifeature=0;ifeature<nFeatures;++ifeature){
testFeatures[iclass][isample][ifeature]=trainingFeatures[iclass][nctraining[iclass]+isample][ifeature];
}
+ //test
+ cout << "isample:" << isample<< endl;
result=net.run(testFeatures[iclass][isample]);
string refClassName=nameVector[iclass];
float maxP=-1;
@@ -165,13 +186,19 @@ double getCost(const vector<Vector2d<float> >
&trainingFeatures)
maxClass=ic;
}
}
+ //test
+ cout << "maxClass:" << maxClass << "(" << nameVector.size() << ")" <<
endl;
string className=nameVector[maxClass];
+ //test
+ cout << "className:" << nameVector[maxClass] << endl;
if(classValueMap.size())
cm.incrementResult(type2string<short>(classValueMap[refClassName]),type2string<short>(classValueMap[className]),1.0);
else
cm.incrementResult(cm.getClass(referenceVector[isample]),cm.getClass(outputVector[isample]),1.0);
}
}
+ //test
+ cout << "debug12" << endl;
}
assert(cm.nReference());
return(cm.kappa());
@@ -241,7 +268,9 @@ int main(int argc, char *argv[])
selMap["sbs"]=SBS;
selMap["bfs"]=BFS;
- assert(training_opt[0].size());
+ assert(training_opt.size());
+ if(input_opt.size())
+ cv_opt[0]=0;
if(verbose_opt[0]>=1)
std::cout << "training shape file: " << training_opt[0] << std::endl;
@@ -348,6 +377,7 @@ int main(int argc, char *argv[])
std::cout << mapit->first << ": " << (mapit->second).size() << "
samples" << std::endl;
++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();
@@ -511,7 +541,7 @@ int main(int argc, char *argv[])
cost=getCost(trainingFeatures);
}
else{
- while(cost-previousCost>epsilon_cost_opt[0]){
+ while(fabs(cost-previousCost)>epsilon_cost_opt[0]){
previousCost=cost;
switch(selMap[selector_opt[0]]){
case(SFFS):
diff --git a/src/apps/pkfs_svm.cc b/src/apps/pkfs_svm.cc
index b8b69de..1b12dd0 100644
--- a/src/apps/pkfs_svm.cc
+++ b/src/apps/pkfs_svm.cc
@@ -563,7 +563,7 @@ int main(int argc, char *argv[])
cost=getCost(trainingFeatures);
}
else{
- while(cost-previousCost>epsilon_cost_opt[0]){
+ while(fabs(cost-previousCost)>epsilon_cost_opt[0]){
previousCost=cost;
switch(selMap[selector_opt[0]]){
case(SFFS):
diff --git a/src/apps/pkregression_nn.cc b/src/apps/pkregression_nn.cc
index 4f3192c..88155e0 100644
--- a/src/apps/pkregression_nn.cc
+++ b/src/apps/pkregression_nn.cc
@@ -191,12 +191,25 @@ int main(int argc, char *argv[])
}
switch(num_layers){
- case(3):
- net.create_sparse(connection_opt[0],num_layers, ninput, nneuron_opt[0],
noutput);
+ case(3):{
+ unsigned int layers[3];
+ layers[0]=ninput;
+ layers[1]=nneuron_opt[0];
+ layers[2]=noutput;
+ net.create_sparse_array(connection_opt[0],num_layers,layers);
+ // net.create_sparse(connection_opt[0],num_layers, ninput, nneuron_opt[0],
noutput);
break;
- case(4):
- net.create_sparse(connection_opt[0],num_layers, ninput, nneuron_opt[0],
nneuron_opt[1], noutput);
+ }
+ case(4):{
+ unsigned int layers[3];
+ layers[0]=ninput;
+ layers[1]=nneuron_opt[0];
+ layers[2]=nneuron_opt[1];
+ layers[3]=noutput;
+ net.create_sparse_array(connection_opt[0],num_layers,layers);
+ // net.create_sparse(connection_opt[0],num_layers, ninput, nneuron_opt[0],
nneuron_opt[1], noutput);
break;
+ }
default:
cerr << "Only 1 or 2 hidden layers are supported!" << endl;
exit(1);
--
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