int CountResults(MData& testSet, int *predictions, int *&posSlideCnt, int *&negSlideCnt)
{
int result = 0;
int *slideIdx = testSet.GetSlideIndices();
posSlideCnt = (int*)calloc(testSet.GetNumSlides(), sizeof(int));
negSlideCnt = (int*)calloc(testSet.GetNumSlides(), sizeof(int));
if( negSlideCnt && posSlideCnt ) {
for(int i = 0; i < testSet.GetNumObjs(); i++) {
if( predictions[i] == 1 ) {
posSlideCnt[slideIdx[i]]++;
} else {
negSlideCnt[slideIdx[i]]++;
}
}
}
return result;
}
int CountTrainingObjs(MData& trainSet, MData& testSet, int *&posCount, int *&negCount)
{
int result = 0,
numTrainSlides = trainSet.GetNumSlides(),
*slideIdx = trainSet.GetSlideIndices(),
*labels = trainSet.GetLabels();
posCount = (int*)calloc(numTrainSlides, sizeof(int));
negCount = (int*)calloc(numTrainSlides, sizeof(int));
for(int i = 0; i < trainSet.GetNumObjs(); i++) {
if( labels[i] == 1 ) {
posCount[slideIdx[i]]++;
} else {
negCount[slideIdx[i]]++;
}
}
return result;
}
// Apply the classifier to each slide in the test set. Save the counts of positive and negative
// classes for each slide to a csv file.
//
int ClassifySlides(MData& trainSet, MData& testSet, Classifier *classifier, string testFile,
string outFileName)
{
int result = 0;
int *pred = NULL, *posSlideCnt = NULL, *negSlideCnt = NULL;
char **slideNames = testSet.GetSlideNames();
ofstream outFile(outFileName.c_str());
if( classifier == NULL ) {
result = -10;
}
if( result == 0 ) {
if( outFile.is_open() ) {
outFile << "slides,";
for(int i = 0; i < testSet.GetNumSlides() - 1; i++) {
outFile << slideNames[i] << ",";
}
outFile << slideNames[testSet.GetNumSlides() - 1] << endl;
pred = (int*)malloc(testSet.GetNumObjs() * sizeof(int));
if( pred == NULL ) {
cerr << "Unable to allocate results buffer" << endl;
result = -11;
}
} else {
cerr << "Unable to open " << outFileName << endl;
result = -12;
}
}
if( result == 0 ) {
result = TrainClassifier(classifier, trainSet);
}
if( result == 0 ) {
if( !classifier->ClassifyBatch(testSet.GetData(), testSet.GetNumObjs(), testSet.GetDims(), pred) ) {
cerr << "Classification failed" << endl;
result = -13;
}
}
if( result == 0 ) {
result = CountResults(testSet, pred, posSlideCnt, negSlideCnt);
}
if( result == 0 ) {
outFile << "positive,";
for( int i = 0; i < testSet.GetNumSlides() - 1; i++ ) {
outFile << posSlideCnt[i] << ",";
}
outFile << posSlideCnt[testSet.GetNumSlides() - 1] << endl;
outFile << "negative,";
for( int i = 0; i < testSet.GetNumSlides() - 1; i++ ) {
outFile << negSlideCnt[i] << ",";
}
outFile << negSlideCnt[testSet.GetNumSlides() - 1] << endl;
}
if( outFile.is_open() ) {
outFile.close();
}
if( posSlideCnt )
free(posSlideCnt);
if( negSlideCnt )
free(negSlideCnt);
return result;
}
