@Override
public void addMultiple(AMatrix m, double factor) {
checkRowCount(m.rowCount());
int cc = checkColumnCount(m.columnCount());
for (int i = 0; i < cc; i++) {
getColumnView(i).addMultiple(m.getColumn(i), factor);
}
}
@Test
public void testOuterProducts() {
AVector v = Vectorz.createUniformRandomVector(5);
INDArray a = v.outerProduct(v);
assertTrue(a instanceof AMatrix);
AMatrix m = (AMatrix) a;
AVector v2 = v.clone();
v2.square();
assertEquals(v2, m.getLeadingDiagonal());
}
/** Check results against symmetric matrices that are randomly generated */
public void checkRandomSymmetric() {
for (int N = 1; N <= 15; N++) {
for (int i = 0; i < 20; i++) {
// DenseMatrix64F A = RandomMatrices.createSymmetric(N,-1,1,rand);
AMatrix z = Matrixx.createRandomMatrix(3, 3);
AMatrix A = z.innerProduct(z.getTranspose());
SymmetricQRAlgorithmDecomposition alg = createDecomposition();
assertNotNull(alg.decompose(A));
performStandardTests(alg, A.toMatrix(), -1);
}
}
}
@Override
public AMatrix innerProduct(AMatrix a) {
if (a instanceof ADiagonalMatrix) {
return innerProduct((ADiagonalMatrix) a);
} else if (a instanceof Matrix) {
return innerProduct((Matrix) a);
}
if (!(dimensions == a.rowCount()))
throw new IllegalArgumentException(ErrorMessages.incompatibleShapes(this, a));
int acc = a.columnCount();
Matrix m = Matrix.create(dimensions, acc);
for (int i = 0; i < dimensions; i++) {
double dv = unsafeGetDiagonalValue(i);
for (int j = 0; j < acc; j++) {
m.unsafeSet(i, j, dv * a.unsafeGet(i, j));
}
}
return m;
}
private double diffNormF(AMatrix tempA, AMatrix tempB) {
AMatrix temp = tempA.copy();
temp.sub(tempB);
double total = temp.elementSquaredSum();
temp.abs();
double scale = temp.elementMax();
return Math.abs(scale - 0) > 1e-12 ? total / scale : 0;
}
/** Sees if the pair of eigenvalue and eigenvector was found in the decomposition. */
public void testForEigenpair(
SymmetricQRAlgorithmDecomposition alg, double valueReal, double valueImg, double... vector) {
int N = alg.getNumberOfEigenvalues();
int numMatched = 0;
for (int i = 0; i < N; i++) {
Vector2 c = alg.getEigenvalue(i);
if (Math.abs(c.x - valueReal) < 1e-4 && Math.abs(c.y - valueImg) < 1e-4) {
// if( c.isReal() ) {
if (Math.abs(c.y - 0) < 1e-8)
if (vector.length > 0) {
AVector v = alg.getEigenVector(i);
AMatrix e = Matrix.createFromRows(vector);
e = e.getTranspose();
Matrix t = Matrix.create(v.length(), 1);
t.setColumn(0, v);
double error = diffNormF(e, t);
// CommonOps.changeSign(e);
e.multiply(-1);
double error2 = diffNormF(e, t);
if (error < 1e-3 || error2 < 1e-3) numMatched++;
} else {
numMatched++;
}
else if (Math.abs(c.y - 0) > 1e-8) {
numMatched++;
}
}
}
assertEquals(1, numMatched);
}
public CholeskyResult(AMatrix L) {
this(L, IdentityMatrix.create(L.rowCount()), L.getTranspose());
}
@Override
public ZeroVector innerProduct(AMatrix m) {
if (m.rowCount() != length)
throw new IllegalArgumentException("Incompatible vector*matrix sizes");
return ZeroVector.create(m.columnCount());
}
private static double normPInf(AMatrix A) {
return A.absCopy().elementMax();
}
@Override
public boolean isSameShape(AMatrix m) {
return (dimensions == m.rowCount()) && (dimensions == m.columnCount());
}