public double labelLogLikelihood(InstanceList ilist) {
double logLikelihood = 0;
for (int ii = 0; ii < ilist.size(); ii++) {
double instanceWeight = ilist.getInstanceWeight(ii);
Instance inst = ilist.get(ii);
Labeling labeling = inst.getLabeling();
if (labeling == null) continue;
Labeling predicted = this.classify(inst).getLabeling();
// System.err.println ("label = \n"+labeling);
// System.err.println ("predicted = \n"+predicted);
if (labeling.numLocations() == 1) {
logLikelihood += instanceWeight * Math.log(predicted.value(labeling.getBestIndex()));
} else {
for (int lpos = 0; lpos < labeling.numLocations(); lpos++) {
int li = labeling.indexAtLocation(lpos);
double labelWeight = labeling.valueAtLocation(lpos);
// System.err.print (", "+labelWeight);
if (labelWeight == 0) continue;
logLikelihood += instanceWeight * labelWeight * Math.log(predicted.value(li));
}
}
}
return logLikelihood;
}
public double dataLogLikelihood(InstanceList ilist) {
double logLikelihood = 0;
for (int ii = 0; ii < ilist.size(); ii++) {
double instanceWeight = ilist.getInstanceWeight(ii);
Instance inst = ilist.get(ii);
Labeling labeling = inst.getLabeling();
if (labeling != null)
logLikelihood += instanceWeight * dataLogProbability(inst, labeling.getBestIndex());
else {
Labeling predicted = this.classify(inst).getLabeling();
// System.err.println ("label = \n"+labeling);
// System.err.println ("predicted = \n"+predicted);
for (int lpos = 0; lpos < predicted.numLocations(); lpos++) {
int li = predicted.indexAtLocation(lpos);
double labelWeight = predicted.valueAtLocation(lpos);
// System.err.print (", "+labelWeight);
if (labelWeight == 0) continue;
logLikelihood += instanceWeight * labelWeight * dataLogProbability(inst, li);
}
}
}
return logLikelihood;
}
/** @param args */
public static void main(String[] args) {
final int[] dims = {50, 30};
final int n = 1500;
final short[] short_pixels = Util.rampShort(n, 0);
// final int[] int_pixels=Util.rampInt(n,0);
final float[] float_pixels = Util.rampFloat(n, 0);
final short[] labels = {0, 1, 2};
final int[] borders = {0, 10, 200, 1000};
final float[] flabels = {0, 1, 2};
final float[] fborders = {0.0f, 10, 200, 1000};
PixelCube<Short, BaseIndex> pci = new PixelCube<Short, BaseIndex>(dims, short_pixels);
pci.setIterationPattern(IP_FWD + IP_SINGLE);
pci.setIndexing(BASE_INDEXING);
PixelCube<Short, BaseIndex> outc = (PixelCube<Short, BaseIndex>) pci.clone();
RasterForwardIterator<Short> in = (RasterForwardIterator<Short>) pci.iterator();
RasterForwardIterator<Short> out = (RasterForwardIterator<Short>) outc.iterator();
Labeling<Short> labeling = new Labeling<Short>(labels, borders);
labeling.setIO(in, out);
labeling.run();
new ImageJ();
PixLib plib = new PixLib();
ImagePlus imp1 = null;
try {
imp1 = plib.imageFrom("test", pci);
} catch (UnsupportedTypeException e2) {
// TODO Auto-generated catch block
e2.printStackTrace();
}
imp1.show();
ImagePlus imp2 = null;
try {
imp2 = plib.imageFrom("thresholded", outc);
} catch (UnsupportedTypeException e1) {
// TODO Auto-generated catch block
e1.printStackTrace();
}
imp2.show();
PixelCube<Float, BaseIndex> pcif = new PixelCube<Float, BaseIndex>(dims, float_pixels);
pcif.setIterationPattern(IP_FWD + IP_SINGLE);
pcif.setIndexing(BASE_INDEXING);
PixelCube<Float, BaseIndex> outcf = (PixelCube<Float, BaseIndex>) pcif.clone();
RasterForwardIterator<Float> inf = (RasterForwardIterator<Float>) pcif.iterator();
RasterForwardIterator<Float> outf = (RasterForwardIterator<Float>) outcf.iterator();
Labeling<Float> labelingf = new Labeling<Float>(flabels, fborders);
labelingf.setIO(inf, outf);
Float elem = Float.valueOf(3.0f);
System.out.println(labelingf.mapFloat(elem).getClass());
labelingf.run();
// PixLib plibf=new PixLib();
ImagePlus imp3 = null;
try {
imp3 = plib.imageFrom("test", pcif);
} catch (UnsupportedTypeException e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
imp3.show();
ImagePlus imp4 = null;
try {
imp4 = plib.imageFrom("thresholded", outcf);
} catch (UnsupportedTypeException e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
imp4.show();
}
