@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;
}
/**
* @param instances
* @throws Exception
*/
public void train(List<Instance> instances) {
assert (instances.size() > 0);
int numOfAttributes = instances.get(0).getNumOfAttributes();
initializeWeights(numOfAttributes);
// Stochastic gradient descent training algorithm
int size = instances.size();
for (int i = 0; i < numOfIterations; i++) {
double logit = 0d, loss = 0d;
int label, predicted, errorCount = 0;
double[] oldWeights = new double[numOfAttributes];
for (int j = 0; j < numOfAttributes; j++) oldWeights[j] = weights[j];
for (Instance instance : instances) {
double[] attributeValues = instance.getAttributes();
label = instance.getLabel();
logit = innerProduct(attributeValues);
assert (label == 1 || label == -1);
loss = sigmoid(label * logit);
// prediction based on current weight
if (sigmoid(logit) <= 0.5) predicted = -1;
else predicted = 1;
if (predicted != label) errorCount++;
// Weights updating by stochastic gradient descent
for (int j = 0; j < numOfAttributes; j++) {
weights[j] += learningRate * label * attributeValues[j] * (1 - loss);
}
}
if (i % 500 == 0)
logger.debug("Iteration = {}, training error = {}", i, (double) errorCount / size);
}
}
/**
* computes the cost function which is to be minimized.
*
* @param examples training data
* @return cost
*/
public double computeCost(ArrayList<Instance> examples) {
int m = examples.size();
double cost = 0.0;
for (Instance example : examples) {
double prediction = computeHypothesis(example.getAllInputs());
int label = example.getLabel();
cost += label * Math.log(prediction) + (1.0 - label) * Math.log(1.0 - prediction);
}
return -cost / m;
}
@Override
public void train(List<Instance> instances) {
// Initial settings
// loop for sgd_iters passes for the dataset
for (int p = 0; p < sgd_iters; ++p) {
int n = instances.size();
// loop through dataset
for (int i = 0; i < n; ++i) { // t=i+p*n
int t = i + p * n + 1;
Instance curInst = instances.get(i); // should be random in original Pegasos
int yi = Integer.parseInt(curInst.getLabel().toString()); // in this case should be int.
if (yi == 0) { // convert yi to {+1,-1}
yi = -1;
}
HashMap<Integer, Double> features = curInst.getFeatureVector().getFeatureMap();
double wx = this.hashMapVectorProduct(this.w, features); // w*x_i
Iterator<Map.Entry<Integer, Double>> wIt = w.entrySet().iterator();
while (wIt.hasNext()) {
Map.Entry<Integer, Double> entry = wIt.next();
int k = entry.getKey();
double v = entry.getValue();
v = (1.0 - 1.0 / t) * v;
w.put(k, v);
}
// loop through featureVector for w_j
Iterator<Map.Entry<Integer, Double>> featureIter = features.entrySet().iterator();
while (yi * wx < 1 && featureIter.hasNext()) {
Map.Entry<Integer, Double> entry = featureIter.next();
int j = entry.getKey();
double xij = entry.getValue();
if (null == w.get(j)) {
w.put(j, this.init_w);
}
double update = 1.0 / (pegasos_lambda * t) * yi * xij;
w.put(j, w.get(j) + update);
}
}
}
}
protected void _trainIteration(ArrayList<Instance> examples, double rate) {
double biasGradient = 0.0;
double[] gradients = new double[weights.length];
for (Instance example : examples) {
double error = example.getLabel() - computeHypothesis(example.getAllInputs());
biasGradient += (error);
for (int i = 0; i < gradients.length; i++) {
gradients[i] += (error) * example.getInput(i);
}
}
for (int i = 0; i < weights.length; i++) {
gradients[i] = gradients[i] / examples.size();
}
biasWeight += rate * biasGradient / examples.size();
for (int i = 0; i < weights.length; i++) {
weights[i] += rate * gradients[i] / examples.size();
}
}
