@Override
public BinaryResults test(List<Input> inputs) throws Exception {
BinaryResults results = new BinaryResults(label);
for (Input unknown : inputs) {
// Take a majority vote
int[] votes = new int[] {0, 0};
for (svm_model model : models) {
int vote = (int) svm.svm_predict(model, unknown.getSVMFeatures());
if (vote == 1) {
votes[0]++;
} else {
votes[1]++;
}
}
int prediction = (votes[0] > votes[1] ? 1 : -1);
results.add(unknown, prediction == 1);
}
double[] confusion = results.confusionMatrix();
System.out.println(confusion[0] + " " + confusion[1] + " " + confusion[2] + " " + confusion[3]);
return results;
}
@Override
public void train(List<Input> inputs) throws Exception {
// What is the balance of the data set?
Map<Integer, Integer> labelCounts = new HashMap<Integer, Integer>();
for (Input input : inputs) {
int thisLabel = (input.label == label ? 1 : -1);
if (!labelCounts.containsKey(thisLabel)) {
labelCounts.put(thisLabel, 1);
} else {
labelCounts.put(thisLabel, labelCounts.get(thisLabel) + 1);
}
}
// How many models do we need to train?
int modelCount =
(int) Math.ceil(labelCounts.get(-1).intValue() / labelCounts.get(1).intValue());
for (int i = 0; i < modelCount; i++) {
svm_problem problem = new svm_problem();
// l appears to be the number of training samples
problem.l = labelCounts.get(1) * 2;
// The features of each sample
svm_node[][] svmTrainingSamples = new svm_node[problem.l][];
double[] labels = new double[problem.l];
int count = 0;
// Get all of the 1-labeled inputs
for (Input input : inputs) {
if (input.label == label) {
svmTrainingSamples[count] = input.getSVMFeatures();
labels[count++] = 1;
}
}
// Randomly get -1-labeled inputs
for (int j = 0; j < labelCounts.get(1); j++) {
Input input = inputs.get(MathUtils.RANDOM.nextInt(inputs.size()));
if (input.label == label) {
j--;
continue;
}
svmTrainingSamples[count] = input.getSVMFeatures();
labels[count++] = -1;
}
problem.x = svmTrainingSamples;
// y is probably the labels to each sample
problem.y = labels;
// Set the training parameters
svm_parameter param = new svm_parameter();
// SVM Type
// 0 -- C-SVC (classification)
// 1 -- nu-SVC (classification)
// 2 -- one-class SVM
// 3 -- epsilon-SVR (regression)
// 4 -- nu-SVR (regression)
param.svm_type = svm_parameter.C_SVC;
// Other C-SVC parameters:
// param.weight[] : sets the parameter C of class i to weight*c (default 1)
// param.C : cost, set the parameter C of C-SVC (default 1)
// Type of kernel
// 0 -- linear: u'*v
// 1 -- polynomial: (gamma*u'*v + coef0)^degree
// 2 -- radial basis function: exp(-gamma*|u-v|^2)
// 3 -- sigmoid: tanh(gamma*u'*v + coef0)
param.kernel_type = svm_parameter.RBF;
// Kernel gamma
param.gamma = 2e9;
// Cost
param.C = 2e9;
// Degree for poly
param.degree = 3;
// Coefficient for poly/sigmoid
param.coef0 = 0.1;
// Stopping criteria
param.eps = 0.05;
// Make larger for faster training
param.cache_size = 800;
// Number of label weights
param.nr_weight = 2;
// Weight labels
param.weight_label = new int[] {1, -1};
param.weight = new double[] {1, 1};
// Nu
param.nu = 0.1;
// p
param.p = 0.1;
// Shrinking heuristic
param.shrinking = 1;
// Estimate probabilities
param.probability = 0;
// Train the SVM classifier
svm.svm_set_print_string_function(
new svm_print_interface() {
@Override
public void print(String s) {}
});
svm_model model = svm.svm_train(problem, param);
models.add(model);
}
}