public void updateModelWeight(List<Sample> sampleList) {
for (FeatureValue featureValue : featureMap.values()) {
featureValue.setTempWeight(0);
}
for (Sample sample : sampleList) {
Map<String, Double> probMap = predict(sample.getFeatures());
for (String feature : sample.getFeatures()) {
for (String label : labelSet) {
this.setTempWeight(
label,
feature,
this.getTempWeight(label, feature)
+ probMap.get(label) * (1.0 / sampleList.size()));
}
}
}
// GIS;
features.setLastWeight();
for (FeatureValue featureValue : featureMap.values()) {
featureValue.setModelWeight(
featureValue.getModelWeight()
+ 1.0
/ maxFeaturePerSample
* Math.log(featureValue.getEmpiricalWeight() / featureValue.getTempWeight()));
}
}
public boolean isConvergence(double threshold) {
double sum = 0;
for (FeatureValue featureValue : featureMap.values()) {
sum +=
(featureValue.getLastWeight() - featureValue.getModelWeight())
* (featureValue.getLastWeight() - featureValue.getModelWeight());
}
return Math.sqrt(sum) <= threshold ? true : false;
}
public void setEmpiricalWeight(int sampleNumber) {
for (FeatureValue featureValue : featureMap.values()) {
featureValue.setEmpiricalWeight(1.0 * featureValue.getCount() / sampleNumber);
}
}
public void setLastWeight() {
for (FeatureValue featureValue : featureMap.values()) {
featureValue.setLastWeight();
}
}