/** Returns the function value after training. */
double predict(T x) {
double f = b;
if (kernel instanceof Linear && w != null) {
if (x instanceof double[]) {
f += Matrix.InnerProduct(w, (double[]) x);
} else if (x instanceof SparseArray) {
for (SparseArray.Entry e : (SparseArray) x) {
f += w[e.i] * e.x;
}
} else {
throw new UnsupportedOperationException("Unsupported data type for linear kernel");
}
} else {
for (SupportVector v : sv) {
if (v != null) {
f += v.alpha * kernel.Function(v.x, x);
}
}
}
return f;
}
