private void _cluster(double[][] data, int k) {
long clock = System.currentTimeMillis();
SpectralClustering cluster = new SpectralClustering(data, k, 0.355);
System.out.format(
"DBSCAN clusterings %d samples in %dms\n", data.length, System.currentTimeMillis() - clock);
System.out.println("getNumClusters:" + cluster.getNumClusters());
System.out.println("getClusterSize:" + cluster.getClusterSize());
// System.out.println(JSON.toJSONString(dbscan.getClusterSize()));
System.out.println("toString:" + cluster.toString());
/** ************************************************************ */
boolean more = true;
EigenValueDecomposition eigen = cluster.getEigen();
double[] lab = eigen.getEigenValues();
double sd = smile.math.Math.sd(eigen.getEigenValues());
System.out.println("sd(eigen.getEigenValues()):" + sd);
if (Math.min(eigen.getEigenValues()) > 0.3) {
result = cluster;
cluster(data, k + 1);
} else {
return;
}
}
/**
* Constructor. Learn Fisher's linear discriminant.
*
* @param x training instances.
* @param y training labels in [0, k), where k is the number of classes.
* @param L the dimensionality of mapped space.
* @param tol a tolerance to decide if a covariance matrix is singular; it will reject variables
* whose variance is less than tol<sup>2</sup>.
*/
public FLD(double[][] x, int[] y, int L, double tol) {
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));
}
// class label set.
int[] labels = Math.unique(y);
Arrays.sort(labels);
for (int i = 0; i < labels.length; i++) {
if (labels[i] < 0) {
throw new IllegalArgumentException("Negative class label: " + labels[i]);
}
if (i > 0 && labels[i] - labels[i - 1] > 1) {
throw new IllegalArgumentException("Missing class: " + labels[i] + 1);
}
}
k = labels.length;
if (k < 2) {
throw new IllegalArgumentException("Only one class.");
}
if (tol < 0.0) {
throw new IllegalArgumentException("Invalid tol: " + tol);
}
if (x.length <= k) {
throw new IllegalArgumentException(
String.format("Sample size is too small: %d <= %d", x.length, k));
}
if (L >= k) {
throw new IllegalArgumentException(
String.format("The dimensionality of mapped space is too high: %d >= %d", L, k));
}
if (L <= 0) {
L = k - 1;
}
final int n = x.length;
p = x[0].length;
// The number of instances in each class.
int[] ni = new int[k];
// Common mean vector.
mean = Math.colMean(x);
// Common covariance.
double[][] T = new double[p][p];
// Class mean vectors.
mu = new double[k][p];
for (int i = 0; i < n; i++) {
int c = y[i];
ni[c]++;
for (int j = 0; j < p; j++) {
mu[c][j] += x[i][j];
}
}
for (int i = 0; i < k; i++) {
for (int j = 0; j < p; j++) {
mu[i][j] = mu[i][j] / ni[i] - mean[j];
}
}
for (int i = 0; i < n; i++) {
for (int j = 0; j < p; j++) {
for (int l = 0; l <= j; l++) {
T[j][l] += (x[i][j] - mean[j]) * (x[i][l] - mean[l]);
}
}
}
for (int j = 0; j < p; j++) {
for (int l = 0; l <= j; l++) {
T[j][l] /= n;
T[l][j] = T[j][l];
}
}
// Between class scatter
double[][] B = new double[p][p];
for (int i = 0; i < k; i++) {
for (int j = 0; j < p; j++) {
for (int l = 0; l <= j; l++) {
B[j][l] += mu[i][j] * mu[i][l];
}
}
}
for (int j = 0; j < p; j++) {
for (int l = 0; l <= j; l++) {
B[j][l] /= k;
B[l][j] = B[j][l];
}
}
EigenValueDecomposition eigen = EigenValueDecomposition.decompose(T, true);
tol = tol * tol;
double[] s = eigen.getEigenValues();
for (int i = 0; i < s.length; i++) {
if (s[i] < tol) {
throw new IllegalArgumentException("The covariance matrix is close to singular.");
}
s[i] = 1.0 / s[i];
}
double[][] U = eigen.getEigenVectors();
double[][] UB = Math.atbmm(U, B);
for (int i = 0; i < k; i++) {
for (int j = 0; j < p; j++) {
UB[i][j] *= s[j];
}
}
Math.abmm(U, UB, B);
eigen = EigenValueDecomposition.decompose(B, true);
U = eigen.getEigenVectors();
scaling = new double[p][L];
for (int i = 0; i < p; i++) {
System.arraycopy(U[i], 0, scaling[i], 0, L);
}
smean = new double[L];
Math.atx(scaling, mean, smean);
smu = Math.abmm(mu, scaling);
}
