void TestWidget::test()
{
resize(800,600);
QApplication::setOverrideCursor(QCursor(Qt::WaitCursor));
logOld->clearLog();
logOld->close_logfile();
if (QFile::exists("oldlogwidget.log")) QFile::remove("oldlogwidget.log");
logOld->open_logfile("oldlogwidget.log", false);
log2->clearLog();
log2->close_logfile();
if (QFile::exists("logwidget2.log")) QFile::remove("logwidget2.log");
log2->open_logfile("logwidget2.log");
testLogs(logOld, labResultsOld, 0, 10);
testLogs(log2, labResults2 , 0, 10);
testLogs(logOld, labResultsOld, 10, 100);
testLogs(log2, labResults2 , 10, 100);
testLogs(logOld, labResultsOld, 100, 1000);
testLogs(log2, labResults2 , 100, 1000);
testLogs(logOld, labResultsOld, 1000, 10000);
testLogs(log2, labResults2 , 1000, 10000);
QApplication::restoreOverrideCursor();
}
int main()
{
testSequence2();
return 0;
testSequenceCMAES();
return 0;
testSequence();
return 0;
testLogs();
return 0;
//Plot real target function
Leph::Plot plot;
for (double x=0.0;x<=1.0;x+=0.03) {
for (double y=0.0;y<=1.0;y+=0.03) {
plot.add(Leph::VectorLabel(
"x", x,
"y", y,
"real", function(x, y)));
}
}
//Initialize Gaussian Process
libgp::GaussianProcess gp(2, "CovSum ( CovSEiso, CovNoise)");
//Adding noised data point to model
for (size_t k=0;k<100;k++) {
double x = 0.5*uniform(generator) + 0.5;
double y = 0.5*uniform(generator) + 0.5;
double f = function(x, y) + noise(1.0);
plot.add(Leph::VectorLabel(
"x", x,
"y", y,
"target", f));
double input[] = {x, y};
gp.add_pattern(input, f);
}
//Optimize hyper parameter
libgp::RProp rprop;
rprop.init();
rprop.maximize(&gp, 50, true);
//Predict fitted model
for (double x=0.0;x<=1.0;x+=0.04) {
for (double y=0.0;y<=1.0;y+=0.04) {
double input[] = {x, y};
plot.add(Leph::VectorLabel(
"x", x,
"y", y,
"fitted", gp.f(input)));
}
}
//Display plot
plot.plot("x", "y", "all").render();
return 0;
}
