//--------------------------------------------------------------------------
double Performance(const DVector &ytest_truth, const DVector &ytest_predict,
int NumClasses) {
long n = ytest_truth.GetN();
if (n != ytest_predict.GetN()) {
printf("Performance. Error: Vector lengths mismatch. Return NAN");
return NAN;
}
if (NumClasses != 1 && NumClasses != 2) {
printf("Performance. Error: Neither regression nor binary classification. Return NAN");
return NAN;
}
double perf = 0.0;
if (NumClasses == 1) { // Relative error
DVector diff;
ytest_truth.Subtract(ytest_predict, diff);
perf = diff.Norm2()/ytest_truth.Norm2();
}
else if (NumClasses == 2) { // Accuracy
double *y1 = ytest_truth.GetPointer();
double *y2 = ytest_predict.GetPointer();
for (long i = 0; i < n; i++) {
perf += y1[i]*y2[i]>0 ? 1.0:0.0;
}
perf = perf/n * 100.0;
}
return perf;
}
//--------------------------------------------------------------------------
void ConvertYtrain(const DVector &ytrain, DMatrix &Ytrain, int NumClasses) {
Ytrain.Init(ytrain.GetN(), NumClasses);
// Lingfei: the y label is supposed to start from 0.
for (int i = 0; i < NumClasses; i++) {
DVector y = ytrain;
double *my = y.GetPointer();
for (long j = 0; j < y.GetN(); j++) {
my[j] = ((int)my[j])==i ? 1.0 : -1.0;
}
Ytrain.SetColumn(i, y);
}
}
//--------------------------------------------------------------------------
double Performance(const DVector &ytest_truth, const DMatrix &Ytest_predict,
int NumClasses) {
long n = ytest_truth.GetN();
if (n != Ytest_predict.GetM() || Ytest_predict.GetN() != NumClasses ) {
printf("Performance. Error: Size mismatch. Return NAN");
return NAN;
}
if (NumClasses <= 2) {
printf("Performance. Error: Not multiclass classification. Return NAN");
return NAN;
}
double perf = 0.0;
// Compute ytest_predict
DVector ytest_predict(n);
double *y = ytest_predict.GetPointer();
for (long i = 0; i < n; i++) {
DVector row(NumClasses);
Ytest_predict.GetRow(i, row);
long idx = -1;
row.Max(idx);
y[i] = (double)idx;
}
// Accuracy
double *y1 = ytest_truth.GetPointer();
double *y2 = ytest_predict.GetPointer();
for (long i = 0; i < n; i++) {
perf += ((int)y1[i])==((int)y2[i]) ? 1.0 : 0.0;
}
perf = perf/n * 100.0;
return perf;
}