@Override
public Label predict(Instance instance) {
Label l = null;
if (instance.getLabel() instanceof ClassificationLabel || instance.getLabel() == null) {
// ----------------- declare variables ------------------
double lambda = 0.0;
RealVector x_instance = new ArrayRealVector(matrixX.getColumnDimension(), 0);
double result = 0.0;
// -------------------------- initialize xi -------------------------
for (int idx = 0; idx < matrixX.getColumnDimension(); idx++) {
x_instance.setEntry(idx, instance.getFeatureVector().get(idx + 1));
}
// ------------------ get lambda -----------------------
for (int j = 0; j < alpha.getDimension(); j++) {
lambda += alpha.getEntry(j) * kernelFunction(matrixX.getRowVector(j), x_instance);
}
// ----------------- make prediction -----------------
Sigmoid g = new Sigmoid(); // helper function
result = g.value(lambda);
l = new ClassificationLabel(result < 0.5 ? 0 : 1);
} else {
System.out.println("label type error!");
}
return l;
}
@Override
public void train(List<Instance> instances) {
// ------------------------ initialize rows and columns ---------------------
int rows = instances.size();
int columns = 0;
// get max columns
for (Instance i : instances) {
int localColumns = Collections.max(i.getFeatureVector().getFeatureMap().keySet());
if (localColumns > columns) columns = localColumns;
}
// ------------------------ initialize alpha vector -----------------------
alpha = new ArrayRealVector(rows, 0);
// ------------------------ initialize base X and Y for use --------------------------
double[][] X = new double[rows][columns];
double[] Y = new double[rows];
for (int i = 0; i < rows; i++) {
Y[i] = ((ClassificationLabel) instances.get(i).getLabel()).getLabelValue();
for (int j = 0; j < columns; j++) {
X[i][j] = instances.get(i).getFeatureVector().get(j + 1);
}
}
// ---------------------- gram matrix -------------------
matrixX = new Array2DRowRealMatrix(X);
RealMatrix gram = new Array2DRowRealMatrix(rows, rows);
for (int i = 0; i < rows; i++) {
for (int j = 0; j < rows; j++) {
gram.setEntry(i, j, kernelFunction(matrixX.getRowVector(i), matrixX.getRowVector(j)));
}
}
// ---------------------- gradient ascent --------------------------
Sigmoid g = new Sigmoid(); // helper function
System.out.println("Training start...");
System.out.println(
"Learning rate: " + _learning_rate + " Training times: " + _training_iterations);
for (int idx = 0; idx < _training_iterations; idx++) {
System.out.println("Training iteration: " + (idx + 1));
for (int k = 0; k < rows; k++) {
double gradient_ascent = 0.0;
RealVector alpha_gram = gram.operate(alpha);
for (int i = 0; i < rows; i++) {
double lambda = alpha_gram.getEntry(i);
double kernel = gram.getEntry(i, k);
gradient_ascent =
gradient_ascent
+ Y[i] * g.value(-lambda) * kernel
+ (1 - Y[i]) * g.value(lambda) * (-kernel);
}
alpha.setEntry(k, alpha.getEntry(k) + _learning_rate * gradient_ascent);
}
}
System.out.println("Training done!");
}
