/** Process support vectors until converge. */
public void finish() {
if (k == 2) {
svm.finish();
} else {
List<ProcessTask> tasks = new ArrayList<ProcessTask>(svms.size());
for (LASVM s : svms) {
tasks.add(new ProcessTask(s));
}
try {
MulticoreExecutor.run(tasks);
} catch (Exception e) {
System.err.println(e.getMessage());
}
}
}
/**
* Trains the SVM with the given dataset for one epoch. The caller may call this method multiple
* times to obtain better accuracy although one epoch is usually sufficient. After calling this
* method sufficient times (usually 1 or 2), the users should call {@link #finalize()} to further
* process support vectors.
*
* @param x training instances.
* @param y training labels in [0, k), where k is the number of classes.
* @param weight instance weight. Must be positive. The soft margin penalty parameter for instance
* i will be weight[i] * C.
*/
@SuppressWarnings("unchecked")
public void Learn(T[] x, int[] y, double[] weight) {
if (x.length != y.length) {
throw new IllegalArgumentException(
String.format("The sizes of X and Y don't match: %d != %d", x.length, y.length));
}
if (weight != null && x.length != weight.length) {
throw new IllegalArgumentException(
String.format(
"The sizes of X and instance weight don't match: %d != %d", x.length, weight.length));
}
int miny = Matrix.Min(y);
if (miny < 0) {
throw new IllegalArgumentException("Negative class label:" + miny);
}
int maxy = Matrix.Max(y);
if (maxy >= k) {
throw new IllegalArgumentException("Invalid class label:" + maxy);
}
if (k == 2) {
int[] yi = new int[y.length];
for (int i = 0; i < y.length; i++) {
if (y[i] == 1) {
yi[i] = +1;
} else {
yi[i] = -1;
}
}
if (weight == null) {
svm.learn(x, yi);
} else {
svm.learn(x, yi, weight);
}
} else if (strategy == Multiclass.ONE_VS_ALL) {
List<TrainingTask> tasks = new ArrayList<TrainingTask>(k);
for (int i = 0; i < k; i++) {
int[] yi = new int[y.length];
double[] w = wi == null ? weight : new double[y.length];
for (int l = 0; l < y.length; l++) {
if (y[l] == i) {
yi[l] = +1;
} else {
yi[l] = -1;
}
if (wi != null) {
w[l] = wi[y[l]];
if (weight != null) {
w[l] *= weight[l];
}
}
}
tasks.add(new TrainingTask(svms.get(i), x, yi, w));
}
try {
MulticoreExecutor.run(tasks);
} catch (Exception e) {
System.err.println(e.getMessage());
}
} else {
List<TrainingTask> tasks = new ArrayList<TrainingTask>(k * (k - 1) / 2);
for (int i = 0, m = 0; i < k; i++) {
for (int j = i + 1; j < k; j++, m++) {
int n = 0;
for (int l = 0; l < y.length; l++) {
if (y[l] == i || y[l] == j) {
n++;
}
}
T[] xij = (T[]) java.lang.reflect.Array.newInstance(x.getClass().getComponentType(), n);
int[] yij = new int[n];
double[] wij = weight == null ? null : new double[n];
for (int l = 0, q = 0; l < y.length; l++) {
if (y[l] == i) {
xij[q] = x[l];
yij[q] = +1;
if (weight != null) {
wij[q] = weight[l];
}
q++;
} else if (y[l] == j) {
xij[q] = x[l];
yij[q] = -1;
if (weight != null) {
wij[q] = weight[l];
}
q++;
}
}
tasks.add(new TrainingTask(svms.get(m), xij, yij, wij));
}
}
try {
MulticoreExecutor.run(tasks);
} catch (Exception e) {
System.err.println(e.getMessage());
}
}
}