/* (non-Javadoc)
* @see categorizer.core.Categorizer#store()
*/
public DataContext store() throws Exception {
if (!valid) throw new CategorizerNotValidException();
DataContext dataContext = super.store();
DataContext catSpecific = new DataContext();
catSpecific.add(priorsTag, priors);
catSpecific.add(new NodePair(classIndexTag, dataSet.getDataHeaders()[classIndex].getLabel()));
dataContext.add(categorizerSpecificTag, catSpecific);
return dataContext;
}
/* (non-Javadoc)
* @see categorizer.core.Categorizer#load(common.DataContext)
*/
public void load(DataContext categorizerDataContext) throws Exception {
super.load(categorizerDataContext);
DataContext catSpecific = categorizerDataContext.getNode(categorizerSpecificTag);
priors = catSpecific.getNode(priorsTag);
Vector tempVector = catSpecific.getElements2(classIndexTag);
if (tempVector != null && tempVector.size() > 0) {
for (int i = 0; i < dataSet.getDataHeaders().length; i++) {
if (dataSet.getDataHeaders()[i].getLabel().equals((String) tempVector.get(0))) {
classIndex = i;
break;
}
}
}
valid = true;
}
/**
* Builds the naive bayes classifier by calculating prior and conditional probabilities.
*
* @see categorizer.aiCategorizer.core.AICategorizer#buildCategorizer()
*/
@Override
public ConfusionMatrix buildCategorizer() throws Exception {
classIndex = dataSet.getClassIndex();
if (classIndex == -1) {
for (int i = dataSet.getDataHeaders().length - 1; i >= 0; i--) {
if (dataSet.getDataHeaders()[i].isNominal()) {
classIndex = i;
break;
}
}
}
classes = dataSet.getDataHeaders()[classIndex].getAvailableValue();
if (!dataSet.getDataHeaders()[classIndex].isNominal()) throw new ClassNotNominalException();
for (int i = 0; i < classes.length; i++) {
priors.add(new NodePair(classes[i], String.valueOf(0)));
System.out.println(classes[i]);
}
System.out.println("#######################");
for (int i = 0; i < dataSet.getDataItems().length; i++) {
if (!dataSet.getDataItems()[i].getDataFields()[classIndex].getStringValue().equals("?")) {
Vector tempVector =
priors.getElements2(
dataSet.getDataItems()[i].getDataFields()[classIndex].getStringValue());
if (tempVector != null && tempVector.size() >= 0) {
int count = Integer.parseInt((String) tempVector.get(0));
count++;
priors.remove(dataSet.getDataItems()[i].getDataFields()[classIndex].getStringValue());
priors.add(
new NodePair(
dataSet.getDataItems()[i].getDataFields()[classIndex].getStringValue(),
String.valueOf(count)));
}
}
//
// System.out.println(dataSet.getDataItems()[i].getDataFields()[dataSet.getDataHeaders().length - 1].getStringValue());
}
for (int i = 0; i < classes.length; i++) {
int count = 0;
Vector tempVector = priors.getElements2(classes[i]);
if (tempVector != null && tempVector.size() >= 0)
count = Integer.parseInt((String) tempVector.get(0)) + 1;
priors.remove(classes[i]);
priors.add(
new NodePair(
classes[i],
String.valueOf(((double) count) / (dataSet.getDataItems().length + classes.length))));
}
DistributionFinder distFinder = new DistributionFinder();
distFinder.findDistributions(dataSet, classes);
// return null;
valid = true;
return super.validate(testSet);
}
/*
* (non-Javadoc)
*
* @see categorizer.aiCategorizer.core.AICategorizer#categorize(categorizer.core.DataItem)
*/
@Override
public DataItem categorize(DataItem dataItem) throws Exception {
// TODO Auto-generated method stub
dataItem = super.categorize(dataItem);
if (!valid) throw new CategorizerNotValidException();
HashMap priorMap = priors.getBaseMap();
System.out.println("######## Categorize #############");
classIndex = dataSet.getClassIndex();
if (classIndex == -1) {
for (int i = dataSet.getDataHeaders().length - 1; i >= 0; i--) {
if (dataSet.getDataHeaders()[i].isNominal()) {
classIndex = i;
break;
}
}
}
classes = dataSet.getDataHeaders()[classIndex].getAvailableValue();
double[] probs = new double[classes.length];
double maxValue = 0 - Double.MAX_VALUE;
int maxIndex = 0;
for (int i = 0; i < classes.length; i++) {
probs[i] = 0;
for (int j = 0; j < dataSet.getDataHeaders().length; j++) {
DataHeader dataHeader = dataSet.getDataHeaders()[j];
if (j != classIndex && dataHeader.isValid()) {
if (dataHeader.isNominal())
// probs[i] += Math.log(dataHeader.getDistributions().probability(classes[i],
// dataItem.getDataFields()[j].getStringValue()));
probs[i] +=
dataHeader
.getDistributions()
.probability(classes[i], dataItem.getDataFields()[j].getStringValue());
else
// probs[i] += Math.log(dataHeader.getDistributions().probability(classes[i],
// Double.parseDouble(dataItem.getDataFields()[j].getStringValue())));
probs[i] +=
dataHeader
.getDistributions()
.probability(
classes[i],
Double.parseDouble(dataItem.getDataFields()[j].getStringValue()));
// System.out.println("---------- : " + probs[i]);
}
}
probs[i] += Math.log(Double.parseDouble(String.valueOf(priorMap.get(classes[i]))));
System.out.println("Class : " + classes[i] + " prob: " + probs[i]);
if (probs[i] > maxValue) {
maxIndex = i;
maxValue = probs[i];
}
}
System.out.println("Decided Class : " + classes[maxIndex] + " prob: " + maxValue);
dataItem.getDataFields()[classIndex].load(classes[maxIndex]);
/*
System.out.println("##########################");
for(int i=0; i<dataItem.getDataFields().length; i++)
{
System.out.println(" >>>>>> " + dataItem.getDataFields()[i].getDataHeader().getLabel() + " : " + dataItem.getDataFields()[i].getStringValue());
}
*/
return dataItem;
}
