private Map<MatrixSlice, EigenStatus> verifyEigens() {
Map<MatrixSlice, EigenStatus> eigenMetaData = Maps.newHashMap();
for (MatrixSlice slice : eigensToVerify) {
EigenStatus status = eigenVerifier.verify(corpus, slice.vector());
eigenMetaData.put(slice, status);
}
return eigenMetaData;
}
public int printDistributedRowMatrix() {
System.out.println("RowPath: " + this.rowPath);
Iterator<MatrixSlice> iterator = this.iterateAll();
int count = 0;
while (iterator.hasNext()) {
MatrixSlice slice = iterator.next();
Vector v = slice.vector();
int size = v.size();
for (int i = 0; i < size; i++) {
Element e = v.getElement(i);
count++;
System.out.print(e.get() + " ");
}
System.out.println();
}
return count;
}
@Test
public void testEigen() {
double[] evals = {
0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 2.0e-7, 0.0, 0.0, -2.0e-7, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0
};
int i = 0;
Matrix a = new DenseMatrix(4, 4);
for (MatrixSlice row : a) {
for (Vector.Element element : row.vector()) {
element.set(evals[i++]);
}
}
EigenDecomposition eig = new EigenDecomposition(a);
Matrix d = eig.getD();
Matrix v = eig.getV();
check("EigenvalueDecomposition (nonsymmetric)...", a.times(v), v.times(d));
}
private void prepareEigens(Configuration conf, Path eigenInput, boolean inMemory) {
DistributedRowMatrix eigens = new DistributedRowMatrix(eigenInput, tmpOut, 1, 1);
eigens.setConf(conf);
if (inMemory) {
List<Vector> eigenVectors = Lists.newArrayList();
for (MatrixSlice slice : eigens) {
eigenVectors.add(slice.vector());
}
eigensToVerify =
new SparseRowMatrix(
eigenVectors.size(),
eigenVectors.get(0).size(),
eigenVectors.toArray(new Vector[eigenVectors.size()]),
true,
true);
} else {
eigensToVerify = eigens;
}
}
private void saveCleanEigens(
Configuration conf, Collection<Map.Entry<MatrixSlice, EigenStatus>> prunedEigenMeta)
throws IOException {
Path path = new Path(outPath, CLEAN_EIGENVECTORS);
FileSystem fs = FileSystem.get(path.toUri(), conf);
SequenceFile.Writer seqWriter =
new SequenceFile.Writer(fs, conf, path, IntWritable.class, VectorWritable.class);
try {
IntWritable iw = new IntWritable();
int numEigensWritten = 0;
int index = 0;
for (Map.Entry<MatrixSlice, EigenStatus> pruneSlice : prunedEigenMeta) {
MatrixSlice s = pruneSlice.getKey();
EigenStatus meta = pruneSlice.getValue();
EigenVector ev =
new EigenVector(
s.vector(), meta.getEigenValue(), Math.abs(1 - meta.getCosAngle()), s.index());
// log.info("appending {} to {}", ev, path);
Writable vw = new VectorWritable(ev);
iw.set(index++);
seqWriter.append(iw, vw);
// increment the number of eigenvectors written and see if we've
// reached our specified limit, or if we wish to write all eigenvectors
// (latter is built-in, since numEigensWritten will always be > 0
numEigensWritten++;
if (numEigensWritten == maxEigensToKeep) {
log.info(
"{} of the {} total eigens have been written",
maxEigensToKeep,
prunedEigenMeta.size());
break;
}
}
} finally {
Closeables.close(seqWriter, false);
}
cleanedEigensPath = path;
}
@Test
public void testSequential() {
int validld = 3;
Matrix A = new DenseMatrix(validld, validld);
double[] columnwise = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0};
int i = 0;
for (MatrixSlice row : A) {
for (Vector.Element element : row.vector()) {
element.set(columnwise[i++]);
}
}
EigenDecomposition Eig = new EigenDecomposition(A);
Matrix D = Eig.getD();
Matrix V = Eig.getV();
check("EigenvalueDecomposition (nonsymmetric)...", A.times(V), V.times(D));
A = A.transpose().times(A);
Eig = new EigenDecomposition(A);
D = Eig.getD();
V = Eig.getV();
check("EigenvalueDecomposition (symmetric)...", A.times(V), V.times(D));
}
