@Override
public double predict(double[] feature) {
double[] probs = probs(feature);
int[] index = RandomUtils.getIndexes(classCount);
SortIntDoubleUtils.sort(index, probs);
return index[index.length - 1];
}
@Override
public void initialize(DataSet d) {
trainData = d;
indices = new TIntArrayList(RandomUtils.getIndexes(d.getInstanceLength()));
classCount = trainData.getLabels().getClassIndexMap().size();
scoreCache = new float[trainData.getInstanceLength()][classCount];
roundData = new DataSet[classCount];
roundIndices = new int[(int) (trainData.getInstanceLength() * SAMPLE_RATE)];
tempLabels = new float[classCount][trainData.getInstanceLength()];
indices.shuffle(new Random());
for (int j = 0; j < roundIndices.length; j++) roundIndices[j] = indices.get(j);
for (int j = 0; j < classCount; j++) {
roundData[j] = new DataSet(trainData.getFeatureMatrix(), makeClassLabel(j));
}
log.info(
"initialize finished, tempLabels + ScoreCache MEM use ~ {} GB",
2.0 * scoreCache.length * scoreCache[0].length * 4 / 1024 / 1024 / 1024);
}