@Test
public void testInclinedPlane() throws IOException {
DoubleMatrix1D normal = new DenseDoubleMatrix1D(3);
normal.assign(new double[] {.0, .0, 1.0});
InclinedPlane3D inclinedPlane = new InclinedPlane3D();
inclinedPlane.setRandomGenerator(new MersenneTwister(123456789));
inclinedPlane.setNormal(normal);
inclinedPlane.setBounds(new Rectangle(-5, -5, 10, 10));
inclinedPlane.setNoiseStd(0.5);
DoubleMatrix2D data = inclinedPlane.generate(10);
SVDPCA pca = new SVDPCA(data);
System.out.println("Eigenvalues:");
System.out.println(pca.getEigenvalues());
System.out.println("Eigenvectors:");
System.out.println(pca.getEigenvectors());
System.out.println("Meanvector:");
System.out.println(pca.getMean());
// Recalculate the input from a truncated SVD, first calculate the mean
DoubleMatrix1D mean = new SparseDoubleMatrix1D(3);
for (int i = 0; i < data.rows(); ++i) {
mean.assign(data.viewRow(i), Functions.plus);
}
mean.assign(Functions.div(data.rows()));
// Truncate the SVD and calculate the coefficient matrix
DenseDoubleMatrix2D coefficients = new DenseDoubleMatrix2D(data.rows(), 2);
DoubleMatrix2D centeredInput = data.copy();
for (int i = 0; i < data.rows(); ++i) {
centeredInput.viewRow(i).assign(mean, Functions.minus);
}
centeredInput.zMult(
pca.getEigenvectors().viewPart(0, 0, 2, 3), coefficients, 1, 0, false, true);
// Reconstruct the data from the lower dimensional information
DoubleMatrix2D reconstruction = data.copy();
for (int i = 0; i < reconstruction.rows(); ++i) {
reconstruction.viewRow(i).assign(mean);
}
coefficients.zMult(
pca.getEigenvectors().viewPart(0, 0, 2, 3), reconstruction, 1, 1, false, false);
// Output to file (can be read by GNU Plot)
String fileName = "inclined-plane-svd-pca.dat";
String packagePath = this.getClass().getPackage().getName().replaceAll("\\.", "/");
File outputFile = new File("src/test/resources/" + packagePath + "/" + fileName);
PrintWriter writer = new PrintWriter(outputFile);
writer.write(data.toString());
writer.close();
}
/**
* Returns the best cut of a graph w.r.t. the degree of dissimilarity between points of different
* partitions and the degree of similarity between points of the same partition.
*
* @param W the weight matrix of the graph
* @return an array of two elements, each of these contains the points of a partition
*/
protected static int[][] bestCut(DoubleMatrix2D W) {
int n = W.columns();
// Builds the diagonal matrices D and D^(-1/2) (represented as their diagonals)
DoubleMatrix1D d = DoubleFactory1D.dense.make(n);
DoubleMatrix1D d_minus_1_2 = DoubleFactory1D.dense.make(n);
for (int i = 0; i < n; i++) {
double d_i = W.viewRow(i).zSum();
d.set(i, d_i);
d_minus_1_2.set(i, 1 / Math.sqrt(d_i));
}
DoubleMatrix2D D = DoubleFactory2D.sparse.diagonal(d);
// System.out.println("DoubleMatrix2D :\n"+D.toString());
DoubleMatrix2D X = D.copy();
// System.out.println("DoubleMatrix2D copy :\n"+X.toString());
// X = D^(-1/2) * (D - W) * D^(-1/2)
X.assign(W, Functions.minus);
// System.out.println("DoubleMatrix2D X: (D-W) :\n"+X.toString());
for (int i = 0; i < n; i++)
for (int j = 0; j < n; j++)
X.set(i, j, X.get(i, j) * d_minus_1_2.get(i) * d_minus_1_2.get(j));
// Computes the eigenvalues and the eigenvectors of X
EigenvalueDecomposition e = new EigenvalueDecomposition(X);
DoubleMatrix1D lambda = e.getRealEigenvalues();
// Selects the eigenvector z_2 associated with the second smallest eigenvalue
// Creates a map that contains the pairs <index, eigenvalue>
AbstractIntDoubleMap map = new OpenIntDoubleHashMap(n);
for (int i = 0; i < n; i++) map.put(i, Math.abs(lambda.get(i)));
IntArrayList list = new IntArrayList();
// Sorts the map on the value
map.keysSortedByValue(list);
// Gets the index of the second smallest element
int i_2 = list.get(1);
// y_2 = D^(-1/2) * z_2
DoubleMatrix1D y_2 = e.getV().viewColumn(i_2).copy();
y_2.assign(d_minus_1_2, Functions.mult);
// Creates a map that contains the pairs <i, y_2[i]>
map.clear();
for (int i = 0; i < n; i++) map.put(i, y_2.get(i));
// Sorts the map on the value
map.keysSortedByValue(list);
// Search the element in the map previuosly ordered that minimizes the cut
// of the partition
double best_cut = Double.POSITIVE_INFINITY;
int[][] partition = new int[2][];
// The array v contains all the elements of the graph ordered by their
// projection on vector y_2
int[] v = list.elements();
// For each admissible splitting point i
for (int i = 1; i < n; i++) {
// The array a contains all the elements that have a projection on vector
// y_2 less or equal to the one of i-th element
// The array b contains the remaining elements
int[] a = new int[i];
int[] b = new int[n - i];
System.arraycopy(v, 0, a, 0, i);
System.arraycopy(v, i, b, 0, n - i);
double cut = Ncut(W, a, b, v);
if (cut < best_cut) {
best_cut = cut;
partition[0] = a;
partition[1] = b;
}
}
// System.out.println("Partition:");
// UtilsJS.printMatrix(partition);
return partition;
}
public final DoubleMatrix2D getData() {
return data2.copy();
}
