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 String printConfusion() {
StringBuilder sb = new StringBuilder();
sb.append("--- Confusion Matrix ---\n\n");
for (int i = 0; i < confusion.size(); i++) {
sb.append(String.format("%10s ", "attr" + (i + 1)));
}
sb.append("\n");
for (int i = 0; i < confusion.size(); i++) {
for (int j = 0; j < confusion.get(i).size(); j++) {
sb.append(String.format("%10s ", confusion.get(i).get(j)));
}
sb.append(" | ")
.append("attr" + (i + 1))
.append(" = ")
.append(domainAttr[nAttr - 1].get(i) + "\n");
}
return sb.toString();
}
public String printModel() {
StringBuilder sb = new StringBuilder();
sb.append("--- Classifier Model (Full Training Set) ---\n\n");
sb.append(String.format(" %10s : ", nameAttr[nAttr - 1]));
for (int i = 0; i < domainAttr[nAttr - 1].size(); i++)
sb.append(String.format(" %10s", domainAttr[nAttr - 1].get(i)));
sb.append("\n");
int cnt = 11 * domainAttr[nAttr - 1].size() + 14;
for (int i = 0; i < cnt; i++) sb.append("-");
sb.append("\n");
for (int i = 0; i < nAttr - 1; i++) {
sb.append(nameAttr[i]).append(" :\n");
for (int j = 0; j < domainAttr[i].size(); j++) {
sb.append(" ").append(String.format("%10s", domainAttr[i].get(j))).append(" = ");
for (int k = 0; k < domainAttr[nAttr - 1].size(); k++) {
sb.append(String.format(" %10s", countTable[i].get(j).get(k)));
}
sb.append("\n");
}
sb.append(" TOTAL = ");
for (int k = 0; k < domainAttr[nAttr - 1].size(); k++) {
sb.append(String.format(" %10s", sumTable[i].get(k)));
}
sb.append("\n\n");
}
return sb.toString();
}