void DataSetVOC::importPaulData(CStr &_inDir, CStr &vocDir)
{
string inDir, outImgDir = vocDir + "JPEGImages/", annoDir = vocDir + "Annotations/";
CmFile::MkDir(outImgDir);
CmFile::MkDir(annoDir);
CmFile::MkDir(vocDir + "ImageSets/Main/");
vecS namesNE;
FILE *f = fopen(_S(_inDir + "Possitive.txt"), "r");
CV_Assert(f != NULL);
char fName[1000];
int l, x, y, w, h;
while (fscanf(f, "%s %d %d %d %d %d\n", fName, &l, &x, &y, &w, &h) == 6){
string nameNE = CmFile::GetNameNE(fName);
namesNE.push_back(nameNE);
Mat img = imread(_inDir + fName), imgN;
double ratio = 500.0 / max(img.cols, img.rows);
resize(img, imgN, Size(), ratio, ratio);
imwrite(outImgDir + nameNE + ".jpg", imgN);
FileStorage fs(annoDir + nameNE + ".yml", FileStorage::WRITE);
fs<<"annotation"<<"{"<<"object"<<"{"<<"bndbox"<<"{";
fs<<"xmin"<<format("'%d'", 1 + cvRound(x*ratio))<<"ymin"<<format("'%d'", 1 + cvRound(y*ratio));
fs<<"xmax"<<format("'%d'", min(imgN.cols, cvRound((x+w)*ratio)))<<"ymax"<<format("'%d'", min(imgN.rows, cvRound((y+h)*ratio)));
fs<<"}"<<"name"<<"Salient"<<"}"<<"}";
}
fclose(f);
int imgNum = namesNE.size();
random_shuffle(namesNE.begin(), namesNE.end());
vecS trainSet(namesNE.begin(), namesNE.begin() + imgNum/2);
vecS testSet(namesNE.begin() + imgNum/2, namesNE.end());
CmFile::writeStrList(vocDir + "ImageSets/Main/TrainVal.txt", trainSet);
CmFile::writeStrList(vocDir + "ImageSets/Main/Test.txt", testSet);
}
bool DataSetVOC::importSaliencyBench(CStr &salDir, CStr &vocDir)
{
string inDir, outImgDir = vocDir + "JPEGImages/", annoDir = vocDir + "Annotations/";
CmFile::MkDir(outImgDir);
CmFile::MkDir(annoDir);
CmFile::MkDir(vocDir + "ImageSets/Main/");
vecS namesNE;
int imgNum = CmFile::GetNamesNE(salDir + "Src/*.jpg", namesNE, inDir);
random_shuffle(namesNE.begin(), namesNE.end());
for (int i = 0; i < imgNum; i++){
CmFile::Copy(inDir + namesNE[i] + ".jpg", outImgDir + namesNE[i] + ".jpg");
Mat mask1u = CmFile::LoadMask(inDir + namesNE[i] + ".png");
Vec4i bb = getMaskRange(mask1u);
FileStorage fs(annoDir + namesNE[i] + ".yml", FileStorage::WRITE);
fs<<"annotation"<<"{"<<"object"<<"{"<<"bndbox"<<"{";
fs<<"xmin"<<format("'%d'", bb[0])<<"ymin"<<format("'%d'", bb[1])<<"xmax"<<format("'%d'", bb[2])<<"ymax"<<format("'%d'", bb[3]);
fs<<"}"<<"name"<<"Salient"<<"}"<<"}";
}
vecS trainSet(namesNE.begin(), namesNE.begin() + imgNum/2);
vecS testSet(namesNE.begin() + imgNum/2, namesNE.end());
CmFile::writeStrList(vocDir + "ImageSets/Main/TrainVal.txt", trainSet);
CmFile::writeStrList(vocDir + "ImageSets/Main/Test.txt", testSet);
vecS classNames;
classNames.push_back("Salient");
CmFile::writeStrList(vocDir + "ImageSets/Main/Class.txt", classNames);
return true;
}
void webMain()
{
testSet();
testMultiSet();
testUnorderedSet();
testPointerSet();
testUnorderedSetOfPointers();
}
int main()
{
srand(time(0));
if(!testSet())
return 1;
if(!testMap())
return 1;
cout<<"LockHashMap test succ\n";
return 0;
}
void testGet() {
testSet();
for(int i=0;i<8;i++){
if(vector_->Get(i)){
LOG4CXX_INFO(Sp::core_logger, "true");
}
else{
LOG4CXX_INFO(Sp::core_logger, "false");
}
}
}
int main(int argc, char **argv) {
testTime();
printf("\n");
testClock();
printf("\n");
testSysTime();
printf("\n");
// TODO: timeofday
testSet();
printf("\n");
testCopy();
return 0;
}
UnlabelledClassificationData UnlabelledClassificationData::partition(UINT trainingSizePercentage){
//Partitions the dataset into a training dataset (which is kept by this instance of the UnlabelledClassificationData) and
//a testing/validation dataset (which is return as a new instance of the UnlabelledClassificationData). The trainingSizePercentage
//therefore sets the size of the data which remains in this instance and the remaining percentage of data is then added to
//the testing/validation dataset
//The dataset has changed so flag that any previous cross validation setup will now not work
crossValidationSetup = false;
crossValidationIndexs.clear();
const UINT numTrainingExamples = (UINT) floor( double(totalNumSamples) / 100.0 * double(trainingSizePercentage) );
UnlabelledClassificationData trainingSet(numDimensions);
UnlabelledClassificationData testSet(numDimensions);
vector< UINT > indexs( totalNumSamples );
//Create the random partion indexs
Random random;
UINT indexA = 0;
UINT indexB = 0;
UINT temp = 0;
for(UINT i=0; i<totalNumSamples; i++) indexs[i] = i;
for(UINT x=0; x<totalNumSamples*1000; x++){
//Pick two random indexs
indexA = random.getRandomNumberInt(0,totalNumSamples);
indexB = random.getRandomNumberInt(0,totalNumSamples);
//Swap the indexs
temp = indexs[ indexA ];
indexs[ indexA ] = indexs[ indexB ];
indexs[ indexB ] = temp;
}
//Add the data to the training and test sets
for(UINT i=0; i<numTrainingExamples; i++){
trainingSet.addSample( data.getRowVector( indexs[i] ) );
}
for(UINT i=numTrainingExamples; i<totalNumSamples; i++){
testSet.addSample( data.getRowVector( indexs[i] ) );
}
//Overwrite the training data in this instance with the training data of the trainingSet
data = trainingSet.getData();
totalNumSamples = trainingSet.getNumSamples();
return testSet;
}
int
main(int argc, char **argv)
{
uint16_t i;
PQ_PARAM_SET_ID plist[] = {DRAFT_401, DRAFT_439, DRAFT_593, DRAFT_743};
size_t numParams = sizeof(plist)/sizeof(PQ_PARAM_SET_ID);
for(i = 0; i<numParams; i++)
{
testPack(plist[i]);
testKeyGen(plist[i]);
testSet(plist[i]);
}
rng_cleanup();
exit(EXIT_SUCCESS);
}
LabelledRegressionData LabelledRegressionData::partition(const UINT trainingSizePercentage){
//Partitions the dataset into a training dataset (which is kept by this instance of the LabelledRegressionData) and
//a testing/validation dataset (which is return as a new instance of the LabelledRegressionData). The trainingSizePercentage
//therefore sets the size of the data which remains in this instance and the remaining percentage of data is then added to
//the testing/validation dataset
const UINT numTrainingExamples = (UINT) floor( double(totalNumSamples) / 100.0 * double(trainingSizePercentage) );
LabelledRegressionData trainingSet(numInputDimensions,numTargetDimensions);
LabelledRegressionData testSet(numInputDimensions,numTargetDimensions);
vector< UINT > indexs( totalNumSamples );
//Create the random partion indexs
Random random;
UINT randomIndex = 0;
for(UINT i=0; i<totalNumSamples; i++) indexs[i] = i;
for(UINT x=0; x<totalNumSamples; x++){
randomIndex = random.getRandomNumberInt(0,totalNumSamples);
SWAP( indexs[ x ] , indexs[ randomIndex ] );
}
//Add the data to the training and test sets
for(UINT i=0; i<numTrainingExamples; i++){
trainingSet.addSample( data[ indexs[i] ].getInputVector(), data[ indexs[i] ].getTargetVector() );
}
for(UINT i=numTrainingExamples; i<totalNumSamples; i++){
testSet.addSample( data[ indexs[i] ].getInputVector(), data[ indexs[i] ].getTargetVector() );
}
//Overwrite the training data in this instance with the training data of the trainingSet
data = trainingSet.getData();
totalNumSamples = trainingSet.getNumSamples();
//The dataset has changed so flag that any previous cross validation setup will now not work
crossValidationSetup = false;
crossValidationIndexs.clear();
return testSet;
}
void create( size_t countCards, size_t countSets, std::vector<card>& cards, std::vector<set>& sets ) {
while( true ) {
sets.clear();
cards.clear();
std::random_shuffle( _cards.begin(), _cards.end() );
for( size_t f = 0; f < countCards; f++ ) {
cards.push_back( _cards.at( f ) );
}
for( size_t c1 = 0; c1 < cards.size() - 2; c1++ ) {
for( size_t c2 = c1 + 1; c2 < cards.size() - 1; c2++ ) {
for( size_t c3 = c2 + 1; c3 < cards.size(); c3++ ) {
if( testSet( &cards.at( c1 ), &cards.at( c2 ), &cards.at( c3 ) ) ) {
set s;
s.index.push_back( c1 ); s.index.push_back( c2 ); s.index.push_back( c3 );
sets.push_back( s );
}
}
}
}
if( sets.size() == countSets ) return;
}
}
int main(int argc, char** argv)
{
#ifdef PARALLEL_CORES
omp_set_num_threads(PARALLEL_CORES);
#endif
std::string directory = "./";
if(argc > 1)
directory = std::string(argv[1]);
IDXLoader loader(28, 28, 10000, 1, directory);
OpenANN::DirectStorageDataSet trainSet(&loader.trainingInput,
&loader.trainingInput);
int H = 196;
OpenANN::SparseAutoEncoder sae(loader.D, H, 3.0, 0.1, 3e-3, OpenANN::LOGISTIC);
sae.trainingSet(trainSet);
OpenANN::LBFGS optimizer(20);
OpenANN::StoppingCriteria stop;
stop.maximalIterations = 400;
optimizer.setOptimizable(sae);
optimizer.setStopCriteria(stop);
optimizer.optimize();
OpenANN::MulticlassEvaluator evaluator(1, OpenANN::Logger::FILE);
OpenANN::DirectStorageDataSet testSet(&loader.testInput, &loader.testInput,
&evaluator);
sae.validationSet(testSet);
QApplication app(argc, argv);
SparseAutoEncoderVisualization visual(sae, trainSet, H, 5, 7, 800, 600);
visual.show();
visual.resize(800, 600);
return app.exec();
}
int main(int argc, char **argv) {
OptionParser opts;
string mapFile, evidFile;//interactFile,ignoreFile;
int factor;
opts.addOption(new StringOption("map",
"--map <filename> : map file",
"../input/grid.bmp", mapFile, false));
opts.addOption(new StringOption("evidence",
"--evidence <filename> : evidence file",
"", evidFile, true));
opts.addOption(new IntOption("factor",
"--factor <int> : scaling factor",
1, factor, true));
opts.parse(argc,argv);
cout << "Loading Map File"<<endl;
BMPFile bmpFile(mapFile);
Grid grid(bmpFile, black);
// cout << "xdim: "<<grid.dims().first<<" yDim: "<<grid.dims().second<<endl;
cout << "Loading Evidence"<<endl;
//Evidence trainSet(evidFile, grid, factor);
/* used when need to train two seperate models
Evidence evid_int(interactFile, grid, factor);
Evidence evid_ig(ignoreFile, grid, factor);
Evidence train_int(grid),test_int(grid),train_ig(grid), test_ig(grid);
evid_int.split(train_int, test_int, 0.05);
evid_ig.split(train_ig, test_ig, 0.05);
*/
Evidence evid(evidFile,grid,factor);
Evidence trainSet(grid),testSet(grid);
evid.split(trainSet,testSet,0.05);
cout<<"Optimize over "<<trainSet.size()<<" examples"<<endl;
#if 0
for (int i=0; i < evid.size(); i++) {
cout << "Evid "<<i<<endl;
vector<pair<int, int> > traj = evid.at(i);
vector<double> timestamps = evid.getTimes(i);
cout << timestamps.size()<<" "<<traj.size()<<endl;
for (int j=0; j < traj.size(); j++) {
cout << timestamps.at(j)<<" "<<traj.at(j).first
<< " "<<traj.at(j).second<<endl;
}
}
#endif
// testSet.write("testTraj.data");
cout << "Generating Feature Set"<<endl;
vector<PosFeature> features;
cout << " Constant Feature"<<endl;
ConstantFeature constFeat(grid);
features.push_back(constFeat);
cout << " Obstacle Feature"<<endl;
ObstacleFeature obsFeat(grid);
features.push_back(obsFeat);
for (int i=1; i < 5; i++) {
cout << " Blur Feature "<<i<<endl;
ObstacleBlurFeature blurFeat(grid, 5*i);
features.push_back(blurFeat);
}
/*
cout << " Robot Feature"<<endl;
RobotGlobalFeature robglobal(grid,snackbot,factor);
features.push_back(robglobal);
// robot local blurres features
for (int i=1; i < 5; i++) {
cout << " RobotBlur Feature "<<i<<endl;
RobotLocalBlurFeature robblur(grid,snackbot,5*i,factor);
features.push_back(robblur);
}
*/
/*
cout << " Creating feature array"<<endl;
FeatureArray featArray2(features);
cout << " Creating lower resolution feature array"<<endl;
FeatureArray featArray(featArray2, factor);
*/
cout << " Speed Feature"<<endl;
vector<double> speedTable(2,0.0);
speedTable.at(1) = 0.75;
//speedTable.at(2) = 1.1;
DisVecSeqFeature speedfeat(speedTable);
/* Robset training weights:
* -3.83 -8.35991 -2.6512 -5.43475 -3.15203 -3.29758
* 0.596987 0.439284
* 0.589445 -0.82448
* Non-robot-ending trainng weights:
* -4.57257 -6.2 -0.3537 -2.7385 -0.9357 -0.2797
* -0.495205 -0.2863
* -1.2225 0.43993
*/
vector<double> weights(6+2+2, -0.0);
weights.at(0) = -25;
weights.at(1) = -8.36;
weights.at(2) = -2.65;
weights.at(3) = -5.43;
weights.at(4) = -3.17;
weights.at(5) = -3.34;
weights.at(6) = 0.5; // robot feature
weights.at(7) = 0.3; // robot feature
weights.at(8) = -0.29; // velocity feature
weights.at(9) = -1.11; // velocity feature
//weights.push_back(1.5);//the last parameter is for velocity feature
Parameters params(weights);
DisSeqOrderInferEngine engine(8,InferenceEngine::GRID8);
trajOptimizerplus optimizer(grid,trainSet,features,speedfeat,engine);
optimizer.optimize(params,0.005,1000,1.0,OPT_EXP);
return 0;
}
TimeSeriesClassificationData TimeSeriesClassificationData::split(const UINT trainingSizePercentage,const bool useStratifiedSampling){
//Partitions the dataset into a training dataset (which is kept by this instance of the TimeSeriesClassificationData) and
//a testing/validation dataset (which is return as a new instance of the TimeSeriesClassificationData). The trainingSizePercentage
//therefore sets the size of the data which remains in this instance and the remaining percentage of data is then added to
//the testing/validation dataset
//The dataset has changed so flag that any previous cross validation setup will now not work
crossValidationSetup = false;
crossValidationIndexs.clear();
TimeSeriesClassificationData trainingSet(numDimensions);
TimeSeriesClassificationData testSet(numDimensions);
trainingSet.setAllowNullGestureClass(allowNullGestureClass);
testSet.setAllowNullGestureClass(allowNullGestureClass);
Vector< UINT > indexs( totalNumSamples );
//Create the random partion indexs
Random random;
UINT randomIndex = 0;
if( useStratifiedSampling ){
//Break the data into seperate classes
Vector< Vector< UINT > > classData( getNumClasses() );
//Add the indexs to their respective classes
for(UINT i=0; i<totalNumSamples; i++){
classData[ getClassLabelIndexValue( data[i].getClassLabel() ) ].push_back( i );
}
//Randomize the order of the indexs in each of the class index buffers
for(UINT k=0; k<getNumClasses(); k++){
UINT numSamples = (UINT)classData[k].size();
for(UINT x=0; x<numSamples; x++){
//Pick a random index
randomIndex = random.getRandomNumberInt(0,numSamples);
//Swap the indexs
SWAP( classData[k][ x ] ,classData[k][ randomIndex ] );
}
}
//Loop over each class and add the data to the trainingSet and testSet
for(UINT k=0; k<getNumClasses(); k++){
UINT numTrainingExamples = (UINT) floor( Float(classData[k].size()) / 100.0 * Float(trainingSizePercentage) );
//Add the data to the training and test sets
for(UINT i=0; i<numTrainingExamples; i++){
trainingSet.addSample( data[ classData[k][i] ].getClassLabel(), data[ classData[k][i] ].getData() );
}
for(UINT i=numTrainingExamples; i<classData[k].size(); i++){
testSet.addSample( data[ classData[k][i] ].getClassLabel(), data[ classData[k][i] ].getData() );
}
}
//Overwrite the training data in this instance with the training data of the trainingSet
data = trainingSet.getClassificationData();
totalNumSamples = trainingSet.getNumSamples();
}else{
const UINT numTrainingExamples = (UINT) floor( Float(totalNumSamples) / 100.0 * Float(trainingSizePercentage) );
//Create the random partion indexs
Random random;
for(UINT i=0; i<totalNumSamples; i++) indexs[i] = i;
for(UINT x=0; x<totalNumSamples; x++){
//Pick a random index
randomIndex = random.getRandomNumberInt(0,totalNumSamples);
//Swap the indexs
SWAP( indexs[ x ] , indexs[ randomIndex ] );
}
//Add the data to the training and test sets
for(UINT i=0; i<numTrainingExamples; i++){
trainingSet.addSample( data[ indexs[i] ].getClassLabel(), data[ indexs[i] ].getData() );
}
for(UINT i=numTrainingExamples; i<totalNumSamples; i++){
testSet.addSample( data[ indexs[i] ].getClassLabel(), data[ indexs[i] ].getData() );
}
//Overwrite the training data in this instance with the training data of the trainingSet
data = trainingSet.getClassificationData();
totalNumSamples = trainingSet.getNumSamples();
}
return testSet;
}
int main (int argc, char ** argv)
{
printf ("MATHCHECK TESTS\n");
printf ("==================\n\n");
init (argc, argv);
KeySet * ks = create_ks ("153", "== + ../bla/val1 + ../bla/val2 ../bla/val3");
test (ks, 1);
ksDel (ks);
ks = create_ks ("250", "< + ../bla/val1 + ../bla/val2 ../bla/val3");
test (ks, (-1));
ksDel (ks);
ks = create_ks ("250", ">= + @/bla/val1 + @/bla/val2 @/bla/val3");
test (ks, 1);
ksDel (ks);
ks = create_ks ("2", "== / @/bla/val1 @/bla/val2");
test (ks, 1);
ksDel (ks);
ks = create_ks ("", ":= / @/bla/val1 @/bla/val2");
testSet (ks, "2");
ksDel (ks);
ks = create_ks ("1", "== / ../bla/val1 ../bla/val3");
test (ks, (-1));
ksDel (ks);
ks = create_ks ("3", "== + '1.5' '1.5'");
test (ks, 1);
ksDel (ks);
ks = create_ks ("4.5", "== + '1.5' + '1.5' '1.5'");
test (ks, 1);
ksDel (ks);
ks = create_ks ("", ":= + '1.5' + '1.5' '1.5'");
testSet (ks, "4.5");
ksDel (ks);
ks = create_ks ("1", "== + '1.5' '1.5'");
test (ks, (-1));
ksDel (ks);
ks = create_ks ("10", "== + ../bla/val3 '7'");
test (ks, 1);
ksDel (ks);
ks = create_ks ("7", "== + @/bla/nonExisting '7'");
test (ks, 1);
ksDel (ks);
ks = create_ks ("", ":= + @/bla/nonExisting '7'");
testSet (ks, "7");
ksDel (ks);
ks = create_ks ("7", "== * @/bla/nonExisting '7'");
test (ks, 1);
ksDel (ks);
ks = create_ks ("3", "== + ../bla/nonExisting + ../bla/nonExistingToo ../bla/val3");
test (ks, 1);
ksDel (ks);
ks = create_ks ("", ":= + ../bla/nonExisting + ../bla/nonExistingToo ../bla/val3");
testSet (ks, "3");
ksDel (ks);
ks = create_ks ("3", "== / @/bla/nonExisting / ../bla/nonExistingToo @/bla/val3");
test (ks, 1);
ksDel (ks);
ks = create_ks ("3", "== + @/bla/nonExisting / ../bla/val3 ../bla/nonExistingToo");
test (ks, 1);
ksDel (ks);
test_multiUp ();
printf ("\ntestmod_mathcheck RESULTS: %d test(s) done. %d error(s).\n", nbTest, nbError);
char buffer[24];
elektraFtoA (buffer, sizeof (buffer), (1.5));
succeed_if (!(strcmp (buffer, "1.5")), "elektraFtoA failed");
fprintf (stderr, "elektraFtoA: val: %g, ret: %s\n", (1.5), buffer);
fprintf (stderr, "elektraEFtoF: string: %s, ret: %g\n", buffer, elektraEFtoF (buffer));
succeed_if ((elektraEFtoF (buffer) - (1.5)) < 0.00001, "elektraEFtoF failed");
return nbError;
}
void testSimple()
{
testSet(false, 10000, true);
testSet(true, 10000, true);
}
ClassificationData ClassificationData::split(const UINT trainingSizePercentage,const bool useStratifiedSampling){
//Partitions the dataset into a training dataset (which is kept by this instance of the ClassificationData) and
//a testing/validation dataset (which is return as a new instance of the ClassificationData). The trainingSizePercentage
//therefore sets the size of the data which remains in this instance and the remaining percentage of data is then added to
//the testing/validation dataset
//The dataset has changed so flag that any previous cross validation setup will now not work
crossValidationSetup = false;
crossValidationIndexs.clear();
ClassificationData trainingSet(numDimensions);
ClassificationData testSet(numDimensions);
trainingSet.setAllowNullGestureClass( allowNullGestureClass );
testSet.setAllowNullGestureClass( allowNullGestureClass );
//Create the random partion indexs
Random random;
UINT randomIndex = 0;
UINT K = getNumClasses();
if( useStratifiedSampling ){
//Break the data into seperate classes
Vector< Vector< UINT > > classData( K );
//Add the indexs to their respective classes
for(UINT i=0; i<totalNumSamples; i++){
classData[ getClassLabelIndexValue( data[i].getClassLabel() ) ].push_back( i );
}
//Randomize the order of the indexs in each of the class index buffers
for(UINT k=0; k<K; k++){
std::random_shuffle(classData[k].begin(), classData[k].end());
}
//Reserve the memory
UINT numTrainingSamples = 0;
UINT numTestSamples = 0;
for(UINT k=0; k<K; k++){
UINT numTrainingExamples = (UINT) floor( Float(classData[k].size()) / 100.0 * Float(trainingSizePercentage) );
UINT numTestExamples = ((UINT)classData[k].size())-numTrainingExamples;
numTrainingSamples += numTrainingExamples;
numTestSamples += numTestExamples;
}
trainingSet.reserve( numTrainingSamples );
testSet.reserve( numTestSamples );
//Loop over each class and add the data to the trainingSet and testSet
for(UINT k=0; k<K; k++){
UINT numTrainingExamples = (UINT) floor( Float(classData[k].getSize()) / 100.0 * Float(trainingSizePercentage) );
//Add the data to the training and test sets
for(UINT i=0; i<numTrainingExamples; i++){
trainingSet.addSample( data[ classData[k][i] ].getClassLabel(), data[ classData[k][i] ].getSample() );
}
for(UINT i=numTrainingExamples; i<classData[k].getSize(); i++){
testSet.addSample( data[ classData[k][i] ].getClassLabel(), data[ classData[k][i] ].getSample() );
}
}
}else{
const UINT numTrainingExamples = (UINT) floor( Float(totalNumSamples) / 100.0 * Float(trainingSizePercentage) );
//Create the random partion indexs
UINT randomIndex = 0;
Vector< UINT > indexs( totalNumSamples );
for(UINT i=0; i<totalNumSamples; i++) indexs[i] = i;
std::random_shuffle(indexs.begin(), indexs.end());
//Reserve the memory
trainingSet.reserve( numTrainingExamples );
testSet.reserve( totalNumSamples-numTrainingExamples );
//Add the data to the training and test sets
for(UINT i=0; i<numTrainingExamples; i++){
trainingSet.addSample( data[ indexs[i] ].getClassLabel(), data[ indexs[i] ].getSample() );
}
for(UINT i=numTrainingExamples; i<totalNumSamples; i++){
testSet.addSample( data[ indexs[i] ].getClassLabel(), data[ indexs[i] ].getSample() );
}
}
//Overwrite the training data in this instance with the training data of the trainingSet
*this = trainingSet;
//Sort the class labels in this dataset
sortClassLabels();
//Sort the class labels of the test dataset
testSet.sortClassLabels();
return testSet;
}
string predictDigits(Mat &originalImage) {
string numbers = "";
Mat clon = originalImage.clone();
// Read the model from the XML file and create the neural network.
CvANN_MLP nnetwork;
CvFileStorage* storage = cvOpenFileStorage(
"/home/andersson/Escritorio/Temporales/neural_network.xml", 0,
CV_STORAGE_READ);
CvFileNode *n = cvGetFileNodeByName(storage, 0, "DigitOCR");
nnetwork.read(storage, n);
cvReleaseFileStorage(&storage);
int rows = originalImage.rows;
int cols = originalImage.cols;
int lx = 0;
int ty = 0;
int by = 0;
int rx = 0;
int flag = 0;
int currentColumn = 1;
bool temp = false;
while (!temp) {
/* Left X */
for (int i = currentColumn; i < cols; i++) {
for (int j = 1; j < rows; j++) {
if (i != (cols - 1)) {
if (originalImage.at<uchar> (j, i) == 0) {
lx = i;
flag = 1;
break;
}
} else {
temp = true;
break;
}
}
if (!temp) {
if (flag == 1) {
flag = 0;
break;
}
} else {
break;
}
}
if (temp) {
continue;
}
/* Right X */
int tempNum;
for (int i = lx; i < cols; i++) {
tempNum = 0;
for (int j = 1; j < rows; j++) {
if (originalImage.at<uchar> (j, i) == 0) {
tempNum += 1;
}
}
if (tempNum == 0) {
rx = (i - 1);
break;
}
}
currentColumn = rx + 1;
/* Top Y */
for (int i = 1; i < rows; i++) {
for (int j = lx; j <= rx; j++) {
if (originalImage.at<uchar> (i, j) == 0) {
ty = i;
flag = 1;
break;
}
}
if (flag == 1) {
flag = 0;
break;
}
}
/* Bottom Y */
for (int i = (rows - 1); i >= 1; i--) {
for (int j = lx; j <= rx; j++) {
if (originalImage.at<uchar> (i, j) == 0) {
by = i;
flag = 1;
break;
}
}
if (flag == 1) {
flag = 0;
break;
}
}
int width = rx - lx;
int height = by - ty;
// Cropping image
Mat crop(originalImage, Rect(lx, ty, width, height));
// Cloning image
Mat splittedImage;
splittedImage = crop.clone();
// imwrite("/home/andersson/Escritorio/Temporales/splitted.png",
// splittedImage);
// Processing image
Mat output;
cv::GaussianBlur(splittedImage, output, cv::Size(5, 5), 0);
cv::threshold(output, output, 50, ATTRIBUTES - 1, 0);
cv::Mat scaledDownImage(ROWCOLUMN, ROWCOLUMN, CV_8U, cv::Scalar(0));
scaleDownImage(output, scaledDownImage);
int pixelValueArray[ATTRIBUTES];
cv::Mat testSet(1, ATTRIBUTES, CV_32F);
// Mat to Pixel Value Array
convertToPixelValueArray(scaledDownImage, pixelValueArray);
// Pixel Value Array to Mat CV_32F
cv::Mat classificationResult(1, CLASSES, CV_32F);
for (int i = 0; i <= ATTRIBUTES; i++) {
testSet.at<float> (0, i) = pixelValueArray[i];
}
// Predicting the number
nnetwork.predict(testSet, classificationResult);
// Selecting the correct response
int maxIndex = 0;
float value = 0.0f;
float maxValue = classificationResult.at<float> (0, 0);
for (int index = 1; index < CLASSES; index++) {
value = classificationResult.at<float> (0, index);
if (value > maxValue) {
maxValue = value;
maxIndex = index;
}
}
printf("Class result: %d\n", maxIndex);
numbers = numbers + convertIntToString(maxIndex);
Scalar colorRect = Scalar(0.0, 0.0, 255.0);
rectangle(clon, Point(lx, ty), Point(rx, by), colorRect, 1, 8, 0);
namedWindow("Clon", CV_WINDOW_NORMAL);
imshow("Clon", clon);
waitKey(0);
namedWindow("Test", CV_WINDOW_NORMAL);
imshow("Test", splittedImage);
waitKey(0);
}
imwrite("/home/andersson/Escritorio/Temporales/clon.png", clon);
return numbers;
}
int main(int argc, char **argv) {
OptionParser opts;
string mapFile,trainFile,testFile;
int factor = 1;
double step;
opts.addOption(new StringOption("map",
"--map <filename> : map file",
"../input/grid.bmp", mapFile, false));
opts.addOption(new StringOption("evidence",
"--test evidence <filename> : evidence file",
"", testFile, true));
opts.addOption(new DoubleOption("step",
"--step <double> : inference interval",
1.0, step, true));
opts.parse(argc,argv);
JetColorMap jet;
RGBTRIPLE black = {0,0,0};
RGBTRIPLE white = {255,255,255};
RGBTRIPLE red;
red.R = 255;
red.G = 0;
red.B = 0;
RGBTRIPLE blue;
blue.R = 0;
blue.G = 0;
blue.B = 255;
RGBTRIPLE green;
green.R = 0;
green.G = 255;
green.B = 0;
RGBTRIPLE initialColor;
initialColor.R = 111;
initialColor.G = 49;
initialColor.B = 152;
RGBTRIPLE currentColor;
currentColor.R = 181;
currentColor.G = 165;
currentColor.B = 213;
RGBTRIPLE magenta;
magenta.R = 255;
magenta.G = 0;
magenta.B = 255;
RGBTRIPLE cyan;
cyan.R = 0;
cyan.G = 255;
cyan.B = 255;
RGBTRIPLE yellow;
yellow.R = 255;
yellow.G = 255;
yellow.B = 0;
BMPFile bmpFile(mapFile);
Grid grid(bmpFile, black);
Evidence testSet(testFile, grid, factor);
// Evidence trainSet(trainFile, grid, factor);
pair<int, int> dims = grid.dims();
cout << " Speed Feature"<<endl;
vector<double> speedTable(VEL_DIM,0.0);
speedTable.at(1) = 0.75;
DisVecSeqFeature speedfeat(speedTable);
vector<int> dimensions;
dimensions.push_back(dims.first);
dimensions.push_back(dims.second);
dimensions.push_back(VEL_DIM);
/* ****************************************
* INITIALIZE MARKOV DECESION PROCESS
* BASED MODEL PARAMETERS
* ****************************************/
vector<double> p_weights(NUMPOSFEAT,-0.0);
p_weights.at(0) = -2.23; //-2.23 for PPP forecast
p_weights.at(1) = -6.2;
p_weights.at(2) = -0.35;
p_weights.at(3) = -2.73;
p_weights.at(4) = -0.92;
p_weights.at(5) = -0.26;
vector<double> r_PosWeights(NUMPOSFEAT+NUMROBFEAT, -0.0);
r_PosWeights.at(0) = -3.83;
r_PosWeights.at(1) = -8.36;
r_PosWeights.at(2) = -2.65;
r_PosWeights.at(3) = -5.43;
r_PosWeights.at(4) = -3.15;
r_PosWeights.at(5) = -3.30;
//r_PosWeights.at(6) = 0.60;
//r_PosWeights.at(7) = 0.45;
vector<double> nr_PosWeights(NUMPOSFEAT+NUMROBFEAT, -0.0);
nr_PosWeights.at(0) = -4.51;
nr_PosWeights.at(1) = -6.2;
nr_PosWeights.at(2) = -0.35;
nr_PosWeights.at(3) = -2.73;
nr_PosWeights.at(4) = -0.93;
nr_PosWeights.at(5) = -0.28;
//nr_PosWeights.at(6) = -0.50;
//nr_PosWeights.at(7) = -0.286;
vector<double> r_SeqWeights(VEL_DIM, -0.0);
r_SeqWeights.at(0) = 0.59;
r_SeqWeights.at(1) = -0.83;
vector<double> nr_SeqWeights(VEL_DIM, -0.0);
nr_SeqWeights.at(0) = -1.21;
nr_SeqWeights.at(1) = 0.49;
Parameters p(p_weights);
Parameters r_Pos(r_PosWeights);
Parameters nr_Pos(nr_PosWeights);
Parameters r_Seq(r_SeqWeights);
Parameters nr_Seq(nr_SeqWeights);
/* ****************************************
* INITIALIZE LINEAR QUADRATIC CONTROL
* BASED MODEL PARAMETERS
* ****************************************/
M_6 A;
A.setZero();
A(0,0) = 1;
A(1,1) = 1;
A(4,2) = -1;
A(5,3) = -1;
M_6_2 B;
B<<1,0,
0,1,
1,0,
0,1,
1,0,
0,1;
M_6 costM;
ifstream infile("../params/nonrob2000.dat");
for(int row=0;row<costM.rows();row++){
for(int col=0;col<costM.cols();col++){
double temp;
infile>>temp;
costM(row,col) = temp;
}
}
infile.close();
M_6 sigma;
sigma<<0.001,0,0,0,0,0,
0,0.001,0,0,0,0,
0,0,0.005,0,0,0,
0,0,0,0.005,0,0,
0,0,0,0,0.005,0,
0,0,0,0,0,0.005;
/* ****************************************
* DECLARATION OF INFERENCE ENGINES
* ****************************************/
OrderedWaveInferenceEngine pp(InferenceEngine::GRID8);
DisSeqOrderInferEngine mdpr(InferenceEngine::GRID8);
DisSeqOrderInferEngine mdpnr(InferenceEngine::GRID8);
ContinuousState cState;
LQControlInference lq(A,B,sigma,costM,cState);
lq.valueInference();
IntentRecognizer IR(grid,p,r_Pos,r_Seq,nr_Pos,nr_Seq,
speedfeat,pp,mdpr,mdpnr,lq);
cout << testSet.size() <<" Examples"<<endl;
for (int i=0; i < testSet.size(); i++) {
vector<pair<int, int> > & traj = testSet.at(i);
vector<double> & vels = testSet.at_v(i);
vector<double> times = testSet.getTimes(i);
pair<int,int> & botinGrid = testSet.at_bot(i);
vector<pair<double,double> > & obs =
testSet.at_raw(i);
vector<double> & rawTimes = testSet.at_rawTime(i);
IR.combineForecast(traj,vels,obs,times,rawTimes,
botinGrid,i,step);
}
}
int main(int argc, char **argv) {
OptionParser opts;
string mapFile, evidFile;
int factor;
opts.addOption(new StringOption("map",
"--map <filename> : map file",
"../input/grid.bmp", mapFile, false));
opts.addOption(new StringOption("evidence",
"--evidence <filename> : evidence file",
"", evidFile, true));
opts.addOption(new IntOption("factor",
"--factor <int> : scaling factor",
1, factor, true));
opts.parse(argc,argv);
JetColorMap jet;
RGBTRIPLE black = {0,0,0};
RGBTRIPLE white = {255,255,255};
RGBTRIPLE red;
red.R = 255;
red.G = 0;
red.B = 0;
RGBTRIPLE blue;
blue.R = 0;
blue.G = 0;
blue.B = 255;
RGBTRIPLE green;
green.R = 0;
green.G = 255;
green.B = 0;
RGBTRIPLE initialColor;
initialColor.R = 111;
initialColor.G = 49;
initialColor.B = 152;
// initialColor.G = 152;
// initialColor.B = 49;
RGBTRIPLE currentColor;
currentColor.R = 181;
currentColor.G = 165;
currentColor.B = 213;
// currentColor.G = 213;
// currentColor.B = 165;
RGBTRIPLE magenta;
magenta.R = 255;
magenta.G = 0;
magenta.B = 255;
RGBTRIPLE cyan;
cyan.R = 0;
cyan.G = 255;
cyan.B = 255;
RGBTRIPLE yellow;
yellow.R = 255;
yellow.G = 255;
yellow.B = 0;
BMPFile bmpFile(mapFile);
Grid grid(bmpFile, black);
Evidence testSet(evidFile, grid, factor);
/*
if (1) {
evid.split(trainSet, testSet, 0.8);
}else{
evid.deterministicsplit(trainSet, testSet);
}*/
#if 0
cout << "Creating Markov Model"<<endl;
MarkovModel markmodel(grid, trainSet);
double totalObj = 0.0;
for (int i=0; i < testSet.size(); i++) {
vector<pair<int, int> > path = testSet.at(i);
cout << "Calling eval"<<endl;
double obj = markmodel.eval(path);
cout << "OBJ: "<<i<<" "<<obj<<endl;
totalObj += obj;
}
cout << "TOTAL OBJ: "<<totalObj<<endl;
cout << "AVERAGE OBJ: "<<totalObj/testSet.size()<<endl;
return 0;
#endif
vector<PosFeature> features;
cout << "Constant Feature"<<endl;
ConstantFeature constFeat(grid);
features.push_back(constFeat);
cout << "Obstacle Feature"<<endl;
ObstacleFeature obsFeat(grid);
features.push_back(obsFeat);
for (int i=1; i < 5; i++) {
cout << "Blur Feature "<<i<<endl;
ObstacleBlurFeature blurFeat(grid, 5*i);
features.push_back(blurFeat);
}
cout << "Creating feature array"<<endl;
FeatureArray featArray2(features);
cout << "Creating lower resolution feature array"<<endl;
FeatureArray featArray(featArray2, factor);
pair<int, int> dims = grid.dims();
pair<int, int> lowDims((int)ceil((float)dims.first/factor),
(int)ceil((float)dims.second/factor));
vector<double> weights(features.size(), -0.0);
weights.at(1) = -6.2;
//for (int i=2; i < weights.size(); i++)
// weights.at(i) = -1.0;
weights.at(0) = -2.23;//-2.23
weights.at(2) = -0.35;
weights.at(3) = -2.73;
weights.at(4) = -0.92;
weights.at(5) = -0.26;
Parameters params(weights);
OrderedWaveInferenceEngine engine(InferenceEngine::GRID8);
vector<vector<double> > prior(dims.first,vector<double> (dims.second,0.0));
/*
double divide = 1.0;
vector<double> radiusWeight;
for (int i=0; i < 20; i++) {
radiusWeight.push_back(1.0/divide);
divide*=2;
}
generatePrior(grid, trainSet, priorOrig, radiusWeight, factor);
reducePrior(priorOrig, prior, factor);
*/
vector<vector<vector<double> > > partition, backpartition;
int time0 = time(0);
BMPFile gridView(dims.first, dims.second);
RewardMap rewards(featArray, params);
vector<double> sums(params.size(),0.00001);
vector<vector<double> > occupancy;
Predictor predictor(grid, rewards, engine);
predictor.setPrior(prior);
cout << testSet.size() <<" Examples"<<endl;
for (int i=0; i < testSet.size(); i++) {
int index = 0;
vector<pair<int, int> > traj = testSet.at(i);
vector<double> times = testSet.getTimes(i);
pair<int, int> initial = traj.front();
pair<int,int> & botinGrid = testSet.at_bot(i);
pair<double,double>& botinPoint = testSet.at_rbot(i);
pair<double,double>& end = testSet.at_raw(i).back();
predictor.setStart(initial);
double thresh = -20.0;
double startTime = times.front();
char buf[1024];
sprintf(buf, "../output/pppredict%03d.dat", i);
ofstream file(buf);
for (double tick = startTime; index < traj.size(); tick+=0.4) {
for ( ; index < traj.size() && times.at(index) < tick; index++);
if (index == traj.size() ) break;
cout << "Evidence: "<<i<<" timestep: "<<tick
<<" index: "<<index<<endl;
predictor.predict(traj.at(index), occupancy);
cout << "SIZE: "<<prior.size()<<endl;
vector<vector<double> > pos
= predictor.getPosterior();
gridView.addBelief(pos, -30.0, 0.0,jet);
grid.addObstacles(gridView, black);
gridView.addLabel(botinGrid,green);
vector<pair<int, int> > subTraj;
subTraj.insert(subTraj.end(), traj.begin(), traj.begin()+index);
gridView.addVector(subTraj, red, factor);
sprintf(buf, "../compare/pp%03d-%03f.bmp", i, tick-startTime);
gridView.write(buf);
//pair<double,double> values = predictor.check(traj.back());
double cost = 0.0;
for(int itr = 0;itr<index;itr++)
cost +=rewards.at(traj[itr].first,traj[itr].second);
cout<<i<<" Normalizer: "<<predictor.getNormalizer(traj.back())<<
" path cost: "<<cost<<" Probability: "<<cost+predictor.getNormalizer(traj.back())<<endl;
vector<vector<vector<double> > > timeOcc
= predictor.getTimeOccupancy();
vector<vector<double > > posterior = predictor.getPosterior();
double maxV = -HUGE_VAL;
pair<int,int> predestGrid;
pair<double,double> predestPoint;
for (int ii=0; ii< dims.first; ii++) {
for (int jj=0; jj < dims.second; jj++) {
if(posterior[ii][jj]>maxV){
predestGrid.first = ii;
predestGrid.second = jj;
}
maxV = max(maxV, posterior.at(ii).at(jj));
}
}
predestPoint = grid.grid2Real(predestGrid.first,predestGrid.second);
double dist = sqrt((end.first-predestPoint.first)*(end.first-predestPoint.first)
+(end.second-predestPoint.second)*(end.second-predestPoint.second));
double logloss = entropy(posterior);
cout<<"final belief: "<<posterior.at(traj.back().first).at(traj.back().second)
<<" max: "<<maxV
<<" logloss: "<<logloss<<endl;
cout<<botinGrid.first<<" "<<botinGrid.second
<<" "<<predestGrid.first<<" "<<predestGrid.second<<endl;
file<<tick-startTime
<<" "<<logloss
<<" "<<posterior.at(botinGrid.first).at(botinGrid.second)
<<" "<<posterior.at(traj.back().first).at(traj.back().second)
<<" "<<maxV<<" "<<dist<<endl;
}
file.close();
}
}
