public String fulltraining(Pair dataset) {
StringBuilder sb = new StringBuilder();
ArrayList<Data> dataTable = dataset.getDataSet();
reset();
init(dataset);
count(dataset, +1);
sb.append(printModel());
sb.append(printProbabilisticModel());
int correct = 0;
for (int i = 0; i < dataTable.size(); i++) {
String result = test(dataTable.get(i));
if (result.equals(dataTable.get(i).getAttributes().get(nAttr - 1))) {
correct++;
}
}
sb.append(printConfusion());
sb.append("\n--- Full Training Set ---\n");
sb.append("Correct Answer\t: ").append(correct).append("\n");
sb.append("Wrong Answer \t: ").append(dataTable.size() - correct).append("\n");
sb.append("Total \t: ").append(dataTable.size()).append("\n");
sb.append("Accuracy \t: ").append(correct * 100.0 / dataTable.size()).append(" %\n");
return sb.toString();
}
public String ten_fold(Pair dataset) {
StringBuilder sb = new StringBuilder();
ArrayList<Data> dataTable = dataset.getDataSet();
reset();
init(dataset);
count(dataset, +1);
sb.append(printModel());
sb.append(printProbabilisticModel());
int sizeMin = dataTable.size() / 10, mod = dataTable.size() % 10;
int offset = 0;
int correct = 0;
for (int i = 0; i < 10; i++) {
ArrayList datas = new ArrayList();
int to = offset + sizeMin;
if (mod > 0) {
to++;
mod--;
}
if (to > dataTable.size()) to = dataTable.size();
for (int j = offset; j < to; j++) {
datas.add(dataTable.get(j));
}
Pair datatest = new Pair(dataset.getAttrLib(), datas);
count(datatest, -1);
for (int j = offset; j < to; j++) {
String result = test(dataTable.get(j));
if (result.equals(dataTable.get(i).getAttributes().get(nAttr - 1))) correct++;
}
count(datatest, +1);
offset = to;
}
sb.append(printConfusion());
sb.append("\n--- Cross Validation 10-fold ---\n");
sb.append("Correct Answer\t: ").append(correct).append("\n");
sb.append("Wrong Answer \t: ").append(dataTable.size() - correct).append("\n");
sb.append("Total \t: ").append(dataTable.size()).append("\n");
sb.append("Accuracy \t: ").append(correct * 100.0 / dataTable.size()).append(" %\n");
return sb.toString();
}
public void count(Pair dataset, int change) {
ArrayList<Data> dataTable = dataset.getDataSet();
for (int i = 0; i < dataTable.size(); i++) {
ArrayList<String> dataValue = dataTable.get(i).getAttributes();
int classId = domainAttr[nAttr - 1].indexOf(dataValue.get(dataValue.size() - 1));
if (classId < -1) continue;
for (int j = 0; j < nAttr; j++) {
int id = domainAttr[j].indexOf(dataValue.get(j));
if (id < 0) {
id = domainAttr[j].size();
domainAttr[j].add(dataValue.get(j));
countTable[j].add(new ArrayList());
for (int k = 0; k < domainAttr[nAttr - 1].size(); k++) countTable[j].get(id).add(0);
}
int bef = countTable[j].get(id).get(classId);
countTable[j].get(id).set(classId, bef + change);
bef = sumTable[j].get(classId);
sumTable[j].set(classId, bef + change);
}
nAll += change;
}
}
public void init(Pair dataset) {
ArrayList<Data> attribut = dataset.getAttrLib();
ArrayList<String> classAttr = attribut.get(attribut.size() - 1).getAttributes();
// Initailize domain Attribut
nAttr = attribut.size();
nAll = 0;
domainAttr = new ArrayList[nAttr];
nameAttr = new String[nAttr];
for (int i = 0; i < nAttr; i++) {
ArrayList<String> temp = attribut.get(i).getAttributes();
domainAttr[i] = new ArrayList<>();
for (String s : temp) {
domainAttr[i].add(s);
}
nameAttr[i] = new String(attribut.get(i).getName());
}
// Inisialisasi tabel count dan tabel sum
countTable = new ArrayList[nAttr];
sumTable = new ArrayList[nAttr];
for (int i = 0; i < nAttr; i++) {
countTable[i] = new ArrayList<>();
sumTable[i] = new ArrayList<>();
for (int j = 0; j < domainAttr[i].size(); j++) {
countTable[i].add(new ArrayList());
for (int k = 0; k < classAttr.size(); k++) countTable[i].get(j).add(0);
}
for (int j = 0; j < classAttr.size(); j++) sumTable[i].add(0);
}
// Inisialisasi Confusion Matrix
confusion = new ArrayList<>();
for (int i = 0; i < domainAttr[nAttr - 1].size(); i++) {
confusion.add(new ArrayList());
for (int j = 0; j < domainAttr[nAttr - 1].size(); j++) confusion.get(i).add(0);
}
}
