/** Returns a model containing all support vectors, i.e. the examples with non-zero alphas. */
private EvoSVMModel getModel(double[] alphas) {
// calculate support vectors
Iterator<Example> reader = exampleSet.iterator();
List<SupportVector> supportVectors = new ArrayList<SupportVector>();
int index = 0;
while (reader.hasNext()) {
double currentAlpha = alphas[index];
Example currentExample = reader.next();
if (currentAlpha != 0.0d) {
double[] x = new double[exampleSet.getAttributes().size()];
int a = 0;
for (Attribute attribute : exampleSet.getAttributes())
x[a++] = currentExample.getValue(attribute);
supportVectors.add(new SupportVector(x, ys[index], currentAlpha));
}
index++;
}
// calculate all sum values
double[] sum = new double[exampleSet.size()];
reader = exampleSet.iterator();
index = 0;
while (reader.hasNext()) {
Example current = reader.next();
double[] x = new double[exampleSet.getAttributes().size()];
int a = 0;
for (Attribute attribute : exampleSet.getAttributes()) x[a++] = current.getValue(attribute);
sum[index] = kernel.getSum(supportVectors, x);
index++;
}
// calculate b (from Stefan's mySVM code)
double bSum = 0.0d;
int bCounter = 0;
for (int i = 0; i < alphas.length; i++) {
if ((ys[i] * alphas[i] - c < -IS_ZERO) && (ys[i] * alphas[i] > IS_ZERO)) {
bSum += ys[i] - sum[i];
bCounter++;
} else if ((ys[i] * alphas[i] + c > IS_ZERO) && (ys[i] * alphas[i] < -IS_ZERO)) {
bSum += ys[i] - sum[i];
bCounter++;
}
}
if (bCounter == 0) {
// unlikely
bSum = 0.0d;
for (int i = 0; i < alphas.length; i++) {
if ((ys[i] * alphas[i] < IS_ZERO) && (ys[i] * alphas[i] > -IS_ZERO)) {
bSum += ys[i] - sum[i];
bCounter++;
}
}
if (bCounter == 0) {
// even unlikelier
bSum = 0.0d;
for (int i = 0; i < alphas.length; i++) {
bSum += ys[i] - sum[i];
bCounter++;
}
}
}
return new EvoSVMModel(exampleSet, supportVectors, kernel, bSum / bCounter);
}
