private Matrix SqrtSPKF(Matrix PDash) {
// Only works with a symmetric Positive Definite matrix
// [V,D] = eig(A)
// S = V*diag(sqrt(diag(D)))*V'
//
// //PDash in
// System.out.println("PDash: ");
// PDash.print(3, 2);
// Matrix NegKeeper = Matrix.identity(9, 9);
// //Testing Forced Compliance
// for(int i = 0; i< 9; i++){
// if (PDash.get(i, i)< 0){
// NegKeeper.set(i,i,-1);
// PDash.set(i, i, -PDash.get(i, i));
// }
// }
EigenvalueDecomposition eig = PDash.eig();
Matrix V = eig.getV();
Matrix D = eig.getD();
int iDSize = D.getRowDimension();
for (int i = 0; i < iDSize; i++) {
D.set(i, i, Math.sqrt(D.get(i, i)));
}
Matrix S = V.times(D).times(V.inverse());
// S = S.times(NegKeeper);
return S;
}
public double[][] Eigenvectors(Matrix m) {
EigenvalueDecomposition decomposition = m.eig();
Matrix eigenVectorsMatrix = decomposition.getV();
double[][] eigenvectors = eigenVectorsMatrix.getArray();
// System.out.println("eigenvectors matrix");
// eigenVectorsMatrix.print(2,2);
return eigenvectors;
}
public double[] Eigenvalues(Matrix m) {
EigenvalueDecomposition decomposition = m.eig();
double[] eigenvalues = decomposition.getRealEigenvalues();
// System.out.println("eigenvalues: ");
// for(int i=0;i<eigenvalues.length;i++)
// System.out.println(""+eigenvalues[i]);
return eigenvalues;
}
public void testJacobian() throws Exception {
OdeSystem sys = new SimpleTest();
// (4,0) punto silla
Matrix jac = sys.getJacobianMatrix(new double[] {4, 0});
EigenvalueDecomposition eig = jac.eig();
double[] imag = eig.getImagEigenvalues();
double[] real = eig.getRealEigenvalues();
print(real, imag);
// TODO que devuelva numérico!
assertEquals("hyperbolic unstable saddle point ", Stability.getPointType(real, imag));
System.out.println(Stability.getPointType(real, imag));
}
@Override
public void doWork() throws OperatorException {
ExampleSet exampleSet = exampleSetInput.getData(ExampleSet.class);
// only use numeric attributes
Tools.onlyNumericalAttributes(exampleSet, "KernelPCA");
Tools.onlyNonMissingValues(exampleSet, getOperatorClassName(), this);
Attributes attributes = exampleSet.getAttributes();
int numberOfExamples = exampleSet.size();
// calculating means for later zero centering
exampleSet.recalculateAllAttributeStatistics();
double[] means = new double[exampleSet.getAttributes().size()];
int i = 0;
for (Attribute attribute : exampleSet.getAttributes()) {
means[i] = exampleSet.getStatistics(attribute, Statistics.AVERAGE);
i++;
}
// kernel
Kernel kernel = Kernel.createKernel(this);
// copying zero centered exampleValues
ArrayList<double[]> exampleValues = new ArrayList<double[]>(numberOfExamples);
i = 0;
for (Example columnExample : exampleSet) {
double[] columnValues = getAttributeValues(columnExample, attributes, means);
exampleValues.add(columnValues);
i++;
}
// filling kernel matrix
Matrix kernelMatrix = new Matrix(numberOfExamples, numberOfExamples);
for (i = 0; i < numberOfExamples; i++) {
for (int j = 0; j < numberOfExamples; j++) {
kernelMatrix.set(
i, j, kernel.calculateDistance(exampleValues.get(i), exampleValues.get(j)));
}
}
// calculating eigenVectors
EigenvalueDecomposition eig = kernelMatrix.eig();
Model model = new KernelPCAModel(exampleSet, means, eig.getV(), exampleValues, kernel);
if (exampleSetOutput.isConnected()) {
exampleSetOutput.deliver(model.apply(exampleSet));
}
originalOutput.deliver(exampleSet);
modelOutput.deliver(model);
}
public static String getEigenValues(GraphModel g) {
Matrix A = g.getWeightedAdjacencyMatrix();
EigenvalueDecomposition ed = A.eig();
double rv[] = ed.getRealEigenvalues();
double iv[] = ed.getImagEigenvalues();
String res = "";
for (int i = 0; i < rv.length; i++) {
if (iv[i] != 0) res += "" + round(rv[i], 3) + " + " + round(iv[i], 3) + "i";
else res += "" + round(rv[i], 3);
if (i != rv.length - 1) {
res += ",";
}
}
return res;
}
@Override
public final void process(final Spot spot) {
if (img.numDimensions() == 3) {
// 3D case
final SpotNeighborhood<T> neighborhood = new SpotNeighborhood<T>(spot, img);
final SpotNeighborhoodCursor<T> cursor = neighborhood.cursor();
double x, y, z;
double x2, y2, z2;
double mass, totalmass = 0;
double Ixx = 0, Iyy = 0, Izz = 0, Ixy = 0, Ixz = 0, Iyz = 0;
final double[] position = new double[img.numDimensions()];
while (cursor.hasNext()) {
cursor.fwd();
mass = cursor.get().getRealDouble();
cursor.getRelativePosition(position);
x = position[0];
y = position[1];
z = position[2];
totalmass += mass;
x2 = x * x;
y2 = y * y;
z2 = z * z;
Ixx += mass * (y2 + z2);
Iyy += mass * (x2 + z2);
Izz += mass * (x2 + y2);
Ixy -= mass * x * y;
Ixz -= mass * x * z;
Iyz -= mass * y * z;
}
final Matrix mat =
new Matrix(new double[][] {{Ixx, Ixy, Ixz}, {Ixy, Iyy, Iyz}, {Ixz, Iyz, Izz}});
mat.timesEquals(1 / totalmass);
final EigenvalueDecomposition eigdec = mat.eig();
final double[] eigenvalues = eigdec.getRealEigenvalues();
final Matrix eigenvectors = eigdec.getV();
final double I1 = eigenvalues[0];
final double I2 = eigenvalues[1];
final double I3 = eigenvalues[2];
final double a = Math.sqrt(2.5 * (I2 + I3 - I1));
final double b = Math.sqrt(2.5 * (I3 + I1 - I2));
final double c = Math.sqrt(2.5 * (I1 + I2 - I3));
final double[] semiaxes = new double[] {a, b, c};
// Sort semi-axes by ascendent order and get the sorting index
final double[] semiaxes_ordered = semiaxes.clone();
Arrays.sort(semiaxes_ordered);
final int[] order = new int[3];
for (int i = 0; i < semiaxes_ordered.length; i++)
for (int j = 0; j < semiaxes.length; j++)
if (semiaxes_ordered[i] == semiaxes[j]) order[i] = j;
// Get the sorted eigenvalues
final double[][] uvectors = new double[3][3];
for (int i = 0; i < eigenvalues.length; i++) {
uvectors[i][0] = eigenvectors.get(0, order[i]);
uvectors[i][1] = eigenvectors.get(1, order[i]);
uvectors[i][2] = eigenvectors.get(2, order[i]);
}
// Store in the Spot object
double theta, phi;
for (int i = 0; i < uvectors.length; i++) {
theta =
Math.acos(
uvectors[i][2]
/ Math.sqrt(
uvectors[i][0] * uvectors[i][0]
+ uvectors[i][1] * uvectors[i][1]
+ uvectors[i][2] * uvectors[i][2]));
phi = Math.atan2(uvectors[i][1], uvectors[i][0]);
if (phi < -Math.PI / 2) phi += Math.PI; // For an ellipsoid we care only for the
// angles in [-pi/2 , pi/2]
if (phi > Math.PI / 2) phi -= Math.PI;
// Store in descending order
spot.putFeature(featurelist_sa[i], semiaxes_ordered[i]);
spot.putFeature(featurelist_phi[i], phi);
spot.putFeature(featurelist_theta[i], theta);
}
// Store the Spot morphology (needs to be outside the above loop)
spot.putFeature(MORPHOLOGY, estimateMorphology(semiaxes_ordered));
} else if (img.numDimensions() == 2) {
// 2D case
final SpotNeighborhood<T> neighborhood = new SpotNeighborhood<T>(spot, img);
final SpotNeighborhoodCursor<T> cursor = neighborhood.cursor();
double x, y;
double x2, y2;
double mass, totalmass = 0;
double Ixx = 0, Iyy = 0, Ixy = 0;
final double[] position = new double[img.numDimensions()];
while (cursor.hasNext()) {
cursor.fwd();
mass = cursor.get().getRealDouble();
cursor.getRelativePosition(position);
x = position[0];
y = position[1];
totalmass += mass;
x2 = x * x;
y2 = y * y;
Ixx += mass * (y2);
Iyy += mass * (x2);
Ixy -= mass * x * y;
}
final Matrix mat = new Matrix(new double[][] {{Ixx, Ixy}, {Ixy, Iyy}});
mat.timesEquals(1 / totalmass);
final EigenvalueDecomposition eigdec = mat.eig();
final double[] eigenvalues = eigdec.getRealEigenvalues();
final Matrix eigenvectors = eigdec.getV();
final double I1 = eigenvalues[0];
final double I2 = eigenvalues[1];
final double a = Math.sqrt(4 * I1);
final double b = Math.sqrt(4 * I2);
final double[] semiaxes = new double[] {a, b};
// Sort semi-axes by ascendent order and get the sorting index
final double[] semiaxes_ordered = semiaxes.clone();
Arrays.sort(semiaxes_ordered);
final int[] order = new int[2];
for (int i = 0; i < semiaxes_ordered.length; i++)
for (int j = 0; j < semiaxes.length; j++)
if (semiaxes_ordered[i] == semiaxes[j]) order[i] = j;
// Get the sorted eigenvalues
final double[][] uvectors = new double[2][2];
for (int i = 0; i < eigenvalues.length; i++) {
uvectors[i][0] = eigenvectors.get(0, order[i]);
uvectors[i][1] = eigenvectors.get(1, order[i]);
}
// Store in the Spot object
double theta, phi;
for (int i = 0; i < uvectors.length; i++) {
theta = 0;
phi = Math.atan2(uvectors[i][1], uvectors[i][0]);
if (phi < -Math.PI / 2) phi += Math.PI; // For an ellipsoid we care only for the
// angles in [-pi/2 , pi/2]
if (phi > Math.PI / 2) phi -= Math.PI;
// Store in descending order
spot.putFeature(featurelist_sa[i], semiaxes_ordered[i]);
spot.putFeature(featurelist_phi[i], phi);
spot.putFeature(featurelist_theta[i], theta);
}
spot.putFeature(featurelist_sa[2], Double.valueOf(0));
spot.putFeature(featurelist_phi[2], Double.valueOf(0));
spot.putFeature(featurelist_theta[2], Double.valueOf(0));
// Store the Spot morphology (needs to be outside the above loop)
spot.putFeature(MORPHOLOGY, estimateMorphology(semiaxes_ordered));
}
}
public Object calculate(GraphModel g) {
ZagrebIndexFunctions zif = new ZagrebIndexFunctions(g);
RenderTable ret = new RenderTable();
Vector<String> titles = new Vector<>();
titles.add(" E(G) ");
titles.add(" 1.1 ");
titles.add(" 1.2 ");
titles.add(" 1.3 ");
titles.add(" 1.4 ");
titles.add(" 1.5 ");
titles.add(" 1.6 ");
titles.add(" 1.7 ");
titles.add(" Eigenvalues ");
titles.add(" 2-degree sum ");
titles.add("new query");
ret.setTitles(titles);
Matrix A = g.getWeightedAdjacencyMatrix();
EigenvalueDecomposition ed = A.eig();
double rv[] = ed.getRealEigenvalues();
double sum = 0;
double detA = Math.abs(A.det());
// positiv RV
Double[] prv = new Double[rv.length];
for (int i = 0; i < rv.length; i++) {
prv[i] = Math.abs(rv[i]);
prv[i] = (double) Math.round(prv[i] * 100000d) / 100000d;
sum += prv[i];
}
Arrays.sort(prv, Collections.reverseOrder());
double maxDeg = 0;
double maxDeg2 = 0;
double minDeg = Integer.MAX_VALUE;
ArrayList<Integer> al = AlgorithmUtils.getDegreesList(g);
Collections.sort(al);
maxDeg = al.get(al.size() - 1);
if (al.size() - 2 >= 0) maxDeg2 = al.get(al.size() - 2);
else maxDeg2 = maxDeg;
minDeg = al.get(0);
if (maxDeg2 == 0) maxDeg2 = maxDeg;
double a = 0;
double b = 0;
for (Vertex v : g) {
if (g.getDegree(v) == maxDeg) a++;
if (g.getDegree(v) == minDeg) b++;
}
if (maxDeg == minDeg) b = 0;
double m = g.getEdgesCount();
double n = g.getVerticesCount();
double M12 = zif.getSecondZagreb(1);
double M21 = zif.getFirstZagreb(1);
double M22 = zif.getSecondZagreb(2);
double Mm11 = zif.getFirstZagreb(-2);
Vector<Object> v = new Vector<>();
v.add(sum);
// 1
v.add(Math.sqrt(2 * m));
// 2
v.add(
(2 * Math.sqrt(2 * m * n) * Math.sqrt(prv[0] * prv[prv.length - 1]))
/ (prv[0] + prv[prv.length - 1]));
// 3
v.add((prv[0] * prv[prv.length - 1] * n + 2 * m) / (prv[0] + prv[prv.length - 1]));
// 4
v.add(Math.sqrt(2 * m * n - (Math.pow(n * (prv[0] - prv[prv.length - 1]), 2) / 4)));
// 5
double alpha = n * Math.floor(n / 2) * (1 - (1 / n) * Math.floor(n / 2));
v.add(Math.sqrt(2 * m * n - (Math.pow((prv[0] - prv[prv.length - 1]), 2) * alpha)));
// 6
if (detA == 0) v.add(0);
else v.add(Math.sqrt(2 * m + n * (n - 1) * Math.pow(detA, 2 / n)));
// 7
double up = n * Math.pow(prv[0] - prv[prv.length - 1], 2);
double down = 4 * (prv[0] + prv[prv.length - 1]);
v.add(Math.sqrt(2 * m * n) - (up / down));
// eigenvalues
v.add(getEigenValues(g));
// 2-degree sum
v.add(Utils.getDegreeSum(g, 1));
// new query
double eigenVal_k = prv[prv.length - 1];
int cnt = prv.length - 1;
if (eigenVal_k == 0) {
while (eigenVal_k == 0) {
eigenVal_k = prv[--cnt];
}
}
int numOfNZEigenValue = 0;
for (int i = 0; i < prv.length; i++) {
if (prv[i] != 0) numOfNZEigenValue++;
}
double alpha_k =
numOfNZEigenValue
* Math.floor(numOfNZEigenValue / 2)
* (1 - (1 / numOfNZEigenValue) * Math.floor(numOfNZEigenValue / 2));
System.out.println(alpha_k + " " + numOfNZEigenValue);
System.out.println(prv[0] + " " + eigenVal_k);
v.add(Math.sqrt(2 * m * numOfNZEigenValue - (Math.pow((prv[0] - eigenVal_k), 2) * alpha_k)));
ret.add(v);
return ret;
}
public static void main(String argv[]) {
Matrix A, B, C, Z, O, I, R, S, X, SUB, M, T, SQ, DEF, SOL;
// Uncomment this to test IO in a different locale.
// Locale.setDefault(Locale.GERMAN);
int errorCount = 0;
int warningCount = 0;
double tmp, s;
double[] columnwise = {1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12.};
double[] rowwise = {1., 4., 7., 10., 2., 5., 8., 11., 3., 6., 9., 12.};
double[][] avals = {{1., 4., 7., 10.}, {2., 5., 8., 11.}, {3., 6., 9., 12.}};
double[][] rankdef = avals;
double[][] tvals = {{1., 2., 3.}, {4., 5., 6.}, {7., 8., 9.}, {10., 11., 12.}};
double[][] subavals = {{5., 8., 11.}, {6., 9., 12.}};
double[][] rvals = {{1., 4., 7.}, {2., 5., 8., 11.}, {3., 6., 9., 12.}};
double[][] pvals = {{4., 1., 1.}, {1., 2., 3.}, {1., 3., 6.}};
double[][] ivals = {{1., 0., 0., 0.}, {0., 1., 0., 0.}, {0., 0., 1., 0.}};
double[][] evals = {
{0., 1., 0., 0.}, {1., 0., 2.e-7, 0.}, {0., -2.e-7, 0., 1.}, {0., 0., 1., 0.}
};
double[][] square = {{166., 188., 210.}, {188., 214., 240.}, {210., 240., 270.}};
double[][] sqSolution = {{13.}, {15.}};
double[][] condmat = {{1., 3.}, {7., 9.}};
double[][] badeigs = {
{0, 0, 0, 0, 0}, {0, 0, 0, 0, 1}, {0, 0, 0, 1, 0}, {1, 1, 0, 0, 1}, {1, 0, 1, 0, 1}
};
int rows = 3, cols = 4;
int invalidld = 5; /* should trigger bad shape for construction with val */
int raggedr = 0; /* (raggedr,raggedc) should be out of bounds in ragged array */
int raggedc = 4;
int validld = 3; /* leading dimension of intended test Matrices */
int nonconformld = 4; /* leading dimension which is valid, but nonconforming */
int ib = 1, ie = 2, jb = 1, je = 3; /* index ranges for sub Matrix */
int[] rowindexset = {1, 2};
int[] badrowindexset = {1, 3};
int[] columnindexset = {1, 2, 3};
int[] badcolumnindexset = {1, 2, 4};
double columnsummax = 33.;
double rowsummax = 30.;
double sumofdiagonals = 15;
double sumofsquares = 650;
/**
* Constructors and constructor-like methods: double[], int double[][] int, int int, int, double
* int, int, double[][] constructWithCopy(double[][]) random(int,int) identity(int)
*/
print("\nTesting constructors and constructor-like methods...\n");
try {
/** check that exception is thrown in packed constructor with invalid length * */
A = new Matrix(columnwise, invalidld);
errorCount =
try_failure(
errorCount,
"Catch invalid length in packed constructor... ",
"exception not thrown for invalid input");
} catch (IllegalArgumentException e) {
try_success("Catch invalid length in packed constructor... ", e.getMessage());
}
try {
/** check that exception is thrown in default constructor if input array is 'ragged' * */
A = new Matrix(rvals);
tmp = A.get(raggedr, raggedc);
} catch (IllegalArgumentException e) {
try_success("Catch ragged input to default constructor... ", e.getMessage());
} catch (java.lang.ArrayIndexOutOfBoundsException e) {
errorCount =
try_failure(
errorCount,
"Catch ragged input to constructor... ",
"exception not thrown in construction...ArrayIndexOutOfBoundsException thrown later");
}
try {
/** check that exception is thrown in constructWithCopy if input array is 'ragged' * */
A = Matrix.constructWithCopy(rvals);
tmp = A.get(raggedr, raggedc);
} catch (IllegalArgumentException e) {
try_success("Catch ragged input to constructWithCopy... ", e.getMessage());
} catch (java.lang.ArrayIndexOutOfBoundsException e) {
errorCount =
try_failure(
errorCount,
"Catch ragged input to constructWithCopy... ",
"exception not thrown in construction...ArrayIndexOutOfBoundsException thrown later");
}
A = new Matrix(columnwise, validld);
B = new Matrix(avals);
tmp = B.get(0, 0);
avals[0][0] = 0.0;
C = B.minus(A);
avals[0][0] = tmp;
B = Matrix.constructWithCopy(avals);
tmp = B.get(0, 0);
avals[0][0] = 0.0;
if ((tmp - B.get(0, 0)) != 0.0) {
/** check that constructWithCopy behaves properly * */
errorCount =
try_failure(
errorCount, "constructWithCopy... ", "copy not effected... data visible outside");
} else {
try_success("constructWithCopy... ", "");
}
avals[0][0] = columnwise[0];
I = new Matrix(ivals);
try {
check(I, Matrix.identity(3, 4));
try_success("identity... ", "");
} catch (java.lang.RuntimeException e) {
errorCount =
try_failure(errorCount, "identity... ", "identity Matrix not successfully created");
}
/**
* Access Methods: getColumnDimension() getRowDimension() getArray() getArrayCopy()
* getColumnPackedCopy() getRowPackedCopy() get(int,int) getMatrix(int,int,int,int)
* getMatrix(int,int,int[]) getMatrix(int[],int,int) getMatrix(int[],int[]) set(int,int,double)
* setMatrix(int,int,int,int,Matrix) setMatrix(int,int,int[],Matrix)
* setMatrix(int[],int,int,Matrix) setMatrix(int[],int[],Matrix)
*/
print("\nTesting access methods...\n");
/** Various get methods: */
B = new Matrix(avals);
if (B.getRowDimension() != rows) {
errorCount = try_failure(errorCount, "getRowDimension... ", "");
} else {
try_success("getRowDimension... ", "");
}
if (B.getColumnDimension() != cols) {
errorCount = try_failure(errorCount, "getColumnDimension... ", "");
} else {
try_success("getColumnDimension... ", "");
}
B = new Matrix(avals);
double[][] barray = B.getArray();
if (barray != avals) {
errorCount = try_failure(errorCount, "getArray... ", "");
} else {
try_success("getArray... ", "");
}
barray = B.getArrayCopy();
if (barray == avals) {
errorCount = try_failure(errorCount, "getArrayCopy... ", "data not (deep) copied");
}
try {
check(barray, avals);
try_success("getArrayCopy... ", "");
} catch (java.lang.RuntimeException e) {
errorCount =
try_failure(errorCount, "getArrayCopy... ", "data not successfully (deep) copied");
}
double[] bpacked = B.getColumnPackedCopy();
try {
check(bpacked, columnwise);
try_success("getColumnPackedCopy... ", "");
} catch (java.lang.RuntimeException e) {
errorCount =
try_failure(
errorCount,
"getColumnPackedCopy... ",
"data not successfully (deep) copied by columns");
}
bpacked = B.getRowPackedCopy();
try {
check(bpacked, rowwise);
try_success("getRowPackedCopy... ", "");
} catch (java.lang.RuntimeException e) {
errorCount =
try_failure(
errorCount, "getRowPackedCopy... ", "data not successfully (deep) copied by rows");
}
try {
tmp = B.get(B.getRowDimension(), B.getColumnDimension() - 1);
errorCount =
try_failure(
errorCount, "get(int,int)... ", "OutOfBoundsException expected but not thrown");
} catch (java.lang.ArrayIndexOutOfBoundsException e) {
try {
tmp = B.get(B.getRowDimension() - 1, B.getColumnDimension());
errorCount =
try_failure(
errorCount, "get(int,int)... ", "OutOfBoundsException expected but not thrown");
} catch (java.lang.ArrayIndexOutOfBoundsException e1) {
try_success("get(int,int)... OutofBoundsException... ", "");
}
} catch (java.lang.IllegalArgumentException e1) {
errorCount =
try_failure(
errorCount, "get(int,int)... ", "OutOfBoundsException expected but not thrown");
}
try {
if (B.get(B.getRowDimension() - 1, B.getColumnDimension() - 1)
!= avals[B.getRowDimension() - 1][B.getColumnDimension() - 1]) {
errorCount =
try_failure(
errorCount, "get(int,int)... ", "Matrix entry (i,j) not successfully retreived");
} else {
try_success("get(int,int)... ", "");
}
} catch (java.lang.ArrayIndexOutOfBoundsException e) {
errorCount =
try_failure(errorCount, "get(int,int)... ", "Unexpected ArrayIndexOutOfBoundsException");
}
SUB = new Matrix(subavals);
try {
M = B.getMatrix(ib, ie + B.getRowDimension() + 1, jb, je);
errorCount =
try_failure(
errorCount,
"getMatrix(int,int,int,int)... ",
"ArrayIndexOutOfBoundsException expected but not thrown");
} catch (java.lang.ArrayIndexOutOfBoundsException e) {
try {
M = B.getMatrix(ib, ie, jb, je + B.getColumnDimension() + 1);
errorCount =
try_failure(
errorCount,
"getMatrix(int,int,int,int)... ",
"ArrayIndexOutOfBoundsException expected but not thrown");
} catch (java.lang.ArrayIndexOutOfBoundsException e1) {
try_success("getMatrix(int,int,int,int)... ArrayIndexOutOfBoundsException... ", "");
}
} catch (java.lang.IllegalArgumentException e1) {
errorCount =
try_failure(
errorCount,
"getMatrix(int,int,int,int)... ",
"ArrayIndexOutOfBoundsException expected but not thrown");
}
try {
M = B.getMatrix(ib, ie, jb, je);
try {
check(SUB, M);
try_success("getMatrix(int,int,int,int)... ", "");
} catch (java.lang.RuntimeException e) {
errorCount =
try_failure(
errorCount,
"getMatrix(int,int,int,int)... ",
"submatrix not successfully retreived");
}
} catch (java.lang.ArrayIndexOutOfBoundsException e) {
errorCount =
try_failure(
errorCount,
"getMatrix(int,int,int,int)... ",
"Unexpected ArrayIndexOutOfBoundsException");
}
try {
M = B.getMatrix(ib, ie, badcolumnindexset);
errorCount =
try_failure(
errorCount,
"getMatrix(int,int,int[])... ",
"ArrayIndexOutOfBoundsException expected but not thrown");
} catch (java.lang.ArrayIndexOutOfBoundsException e) {
try {
M = B.getMatrix(ib, ie + B.getRowDimension() + 1, columnindexset);
errorCount =
try_failure(
errorCount,
"getMatrix(int,int,int[])... ",
"ArrayIndexOutOfBoundsException expected but not thrown");
} catch (java.lang.ArrayIndexOutOfBoundsException e1) {
try_success("getMatrix(int,int,int[])... ArrayIndexOutOfBoundsException... ", "");
}
} catch (java.lang.IllegalArgumentException e1) {
errorCount =
try_failure(
errorCount,
"getMatrix(int,int,int[])... ",
"ArrayIndexOutOfBoundsException expected but not thrown");
}
try {
M = B.getMatrix(ib, ie, columnindexset);
try {
check(SUB, M);
try_success("getMatrix(int,int,int[])... ", "");
} catch (java.lang.RuntimeException e) {
errorCount =
try_failure(
errorCount, "getMatrix(int,int,int[])... ", "submatrix not successfully retreived");
}
} catch (java.lang.ArrayIndexOutOfBoundsException e) {
errorCount =
try_failure(
errorCount,
"getMatrix(int,int,int[])... ",
"Unexpected ArrayIndexOutOfBoundsException");
}
try {
M = B.getMatrix(badrowindexset, jb, je);
errorCount =
try_failure(
errorCount,
"getMatrix(int[],int,int)... ",
"ArrayIndexOutOfBoundsException expected but not thrown");
} catch (java.lang.ArrayIndexOutOfBoundsException e) {
try {
M = B.getMatrix(rowindexset, jb, je + B.getColumnDimension() + 1);
errorCount =
try_failure(
errorCount,
"getMatrix(int[],int,int)... ",
"ArrayIndexOutOfBoundsException expected but not thrown");
} catch (java.lang.ArrayIndexOutOfBoundsException e1) {
try_success("getMatrix(int[],int,int)... ArrayIndexOutOfBoundsException... ", "");
}
} catch (java.lang.IllegalArgumentException e1) {
errorCount =
try_failure(
errorCount,
"getMatrix(int[],int,int)... ",
"ArrayIndexOutOfBoundsException expected but not thrown");
}
try {
M = B.getMatrix(rowindexset, jb, je);
try {
check(SUB, M);
try_success("getMatrix(int[],int,int)... ", "");
} catch (java.lang.RuntimeException e) {
errorCount =
try_failure(
errorCount, "getMatrix(int[],int,int)... ", "submatrix not successfully retreived");
}
} catch (java.lang.ArrayIndexOutOfBoundsException e) {
errorCount =
try_failure(
errorCount,
"getMatrix(int[],int,int)... ",
"Unexpected ArrayIndexOutOfBoundsException");
}
try {
M = B.getMatrix(badrowindexset, columnindexset);
errorCount =
try_failure(
errorCount,
"getMatrix(int[],int[])... ",
"ArrayIndexOutOfBoundsException expected but not thrown");
} catch (java.lang.ArrayIndexOutOfBoundsException e) {
try {
M = B.getMatrix(rowindexset, badcolumnindexset);
errorCount =
try_failure(
errorCount,
"getMatrix(int[],int[])... ",
"ArrayIndexOutOfBoundsException expected but not thrown");
} catch (java.lang.ArrayIndexOutOfBoundsException e1) {
try_success("getMatrix(int[],int[])... ArrayIndexOutOfBoundsException... ", "");
}
} catch (java.lang.IllegalArgumentException e1) {
errorCount =
try_failure(
errorCount,
"getMatrix(int[],int[])... ",
"ArrayIndexOutOfBoundsException expected but not thrown");
}
try {
M = B.getMatrix(rowindexset, columnindexset);
try {
check(SUB, M);
try_success("getMatrix(int[],int[])... ", "");
} catch (java.lang.RuntimeException e) {
errorCount =
try_failure(
errorCount, "getMatrix(int[],int[])... ", "submatrix not successfully retreived");
}
} catch (java.lang.ArrayIndexOutOfBoundsException e) {
errorCount =
try_failure(
errorCount,
"getMatrix(int[],int[])... ",
"Unexpected ArrayIndexOutOfBoundsException");
}
/** Various set methods: */
try {
B.set(B.getRowDimension(), B.getColumnDimension() - 1, 0.);
errorCount =
try_failure(
errorCount,
"set(int,int,double)... ",
"OutOfBoundsException expected but not thrown");
} catch (java.lang.ArrayIndexOutOfBoundsException e) {
try {
B.set(B.getRowDimension() - 1, B.getColumnDimension(), 0.);
errorCount =
try_failure(
errorCount,
"set(int,int,double)... ",
"OutOfBoundsException expected but not thrown");
} catch (java.lang.ArrayIndexOutOfBoundsException e1) {
try_success("set(int,int,double)... OutofBoundsException... ", "");
}
} catch (java.lang.IllegalArgumentException e1) {
errorCount =
try_failure(
errorCount,
"set(int,int,double)... ",
"OutOfBoundsException expected but not thrown");
}
try {
B.set(ib, jb, 0.);
tmp = B.get(ib, jb);
try {
check(tmp, 0.);
try_success("set(int,int,double)... ", "");
} catch (java.lang.RuntimeException e) {
errorCount =
try_failure(
errorCount, "set(int,int,double)... ", "Matrix element not successfully set");
}
} catch (java.lang.ArrayIndexOutOfBoundsException e1) {
errorCount =
try_failure(
errorCount, "set(int,int,double)... ", "Unexpected ArrayIndexOutOfBoundsException");
}
M = new Matrix(2, 3, 0.);
try {
B.setMatrix(ib, ie + B.getRowDimension() + 1, jb, je, M);
errorCount =
try_failure(
errorCount,
"setMatrix(int,int,int,int,Matrix)... ",
"ArrayIndexOutOfBoundsException expected but not thrown");
} catch (java.lang.ArrayIndexOutOfBoundsException e) {
try {
B.setMatrix(ib, ie, jb, je + B.getColumnDimension() + 1, M);
errorCount =
try_failure(
errorCount,
"setMatrix(int,int,int,int,Matrix)... ",
"ArrayIndexOutOfBoundsException expected but not thrown");
} catch (java.lang.ArrayIndexOutOfBoundsException e1) {
try_success("setMatrix(int,int,int,int,Matrix)... ArrayIndexOutOfBoundsException... ", "");
}
} catch (java.lang.IllegalArgumentException e1) {
errorCount =
try_failure(
errorCount,
"setMatrix(int,int,int,int,Matrix)... ",
"ArrayIndexOutOfBoundsException expected but not thrown");
}
try {
B.setMatrix(ib, ie, jb, je, M);
try {
check(M.minus(B.getMatrix(ib, ie, jb, je)), M);
try_success("setMatrix(int,int,int,int,Matrix)... ", "");
} catch (java.lang.RuntimeException e) {
errorCount =
try_failure(
errorCount,
"setMatrix(int,int,int,int,Matrix)... ",
"submatrix not successfully set");
}
B.setMatrix(ib, ie, jb, je, SUB);
} catch (java.lang.ArrayIndexOutOfBoundsException e1) {
errorCount =
try_failure(
errorCount,
"setMatrix(int,int,int,int,Matrix)... ",
"Unexpected ArrayIndexOutOfBoundsException");
}
try {
B.setMatrix(ib, ie + B.getRowDimension() + 1, columnindexset, M);
errorCount =
try_failure(
errorCount,
"setMatrix(int,int,int[],Matrix)... ",
"ArrayIndexOutOfBoundsException expected but not thrown");
} catch (java.lang.ArrayIndexOutOfBoundsException e) {
try {
B.setMatrix(ib, ie, badcolumnindexset, M);
errorCount =
try_failure(
errorCount,
"setMatrix(int,int,int[],Matrix)... ",
"ArrayIndexOutOfBoundsException expected but not thrown");
} catch (java.lang.ArrayIndexOutOfBoundsException e1) {
try_success("setMatrix(int,int,int[],Matrix)... ArrayIndexOutOfBoundsException... ", "");
}
} catch (java.lang.IllegalArgumentException e1) {
errorCount =
try_failure(
errorCount,
"setMatrix(int,int,int[],Matrix)... ",
"ArrayIndexOutOfBoundsException expected but not thrown");
}
try {
B.setMatrix(ib, ie, columnindexset, M);
try {
check(M.minus(B.getMatrix(ib, ie, columnindexset)), M);
try_success("setMatrix(int,int,int[],Matrix)... ", "");
} catch (java.lang.RuntimeException e) {
errorCount =
try_failure(
errorCount,
"setMatrix(int,int,int[],Matrix)... ",
"submatrix not successfully set");
}
B.setMatrix(ib, ie, jb, je, SUB);
} catch (java.lang.ArrayIndexOutOfBoundsException e1) {
errorCount =
try_failure(
errorCount,
"setMatrix(int,int,int[],Matrix)... ",
"Unexpected ArrayIndexOutOfBoundsException");
}
try {
B.setMatrix(rowindexset, jb, je + B.getColumnDimension() + 1, M);
errorCount =
try_failure(
errorCount,
"setMatrix(int[],int,int,Matrix)... ",
"ArrayIndexOutOfBoundsException expected but not thrown");
} catch (java.lang.ArrayIndexOutOfBoundsException e) {
try {
B.setMatrix(badrowindexset, jb, je, M);
errorCount =
try_failure(
errorCount,
"setMatrix(int[],int,int,Matrix)... ",
"ArrayIndexOutOfBoundsException expected but not thrown");
} catch (java.lang.ArrayIndexOutOfBoundsException e1) {
try_success("setMatrix(int[],int,int,Matrix)... ArrayIndexOutOfBoundsException... ", "");
}
} catch (java.lang.IllegalArgumentException e1) {
errorCount =
try_failure(
errorCount,
"setMatrix(int[],int,int,Matrix)... ",
"ArrayIndexOutOfBoundsException expected but not thrown");
}
try {
B.setMatrix(rowindexset, jb, je, M);
try {
check(M.minus(B.getMatrix(rowindexset, jb, je)), M);
try_success("setMatrix(int[],int,int,Matrix)... ", "");
} catch (java.lang.RuntimeException e) {
errorCount =
try_failure(
errorCount,
"setMatrix(int[],int,int,Matrix)... ",
"submatrix not successfully set");
}
B.setMatrix(ib, ie, jb, je, SUB);
} catch (java.lang.ArrayIndexOutOfBoundsException e1) {
errorCount =
try_failure(
errorCount,
"setMatrix(int[],int,int,Matrix)... ",
"Unexpected ArrayIndexOutOfBoundsException");
}
try {
B.setMatrix(rowindexset, badcolumnindexset, M);
errorCount =
try_failure(
errorCount,
"setMatrix(int[],int[],Matrix)... ",
"ArrayIndexOutOfBoundsException expected but not thrown");
} catch (java.lang.ArrayIndexOutOfBoundsException e) {
try {
B.setMatrix(badrowindexset, columnindexset, M);
errorCount =
try_failure(
errorCount,
"setMatrix(int[],int[],Matrix)... ",
"ArrayIndexOutOfBoundsException expected but not thrown");
} catch (java.lang.ArrayIndexOutOfBoundsException e1) {
try_success("setMatrix(int[],int[],Matrix)... ArrayIndexOutOfBoundsException... ", "");
}
} catch (java.lang.IllegalArgumentException e1) {
errorCount =
try_failure(
errorCount,
"setMatrix(int[],int[],Matrix)... ",
"ArrayIndexOutOfBoundsException expected but not thrown");
}
try {
B.setMatrix(rowindexset, columnindexset, M);
try {
check(M.minus(B.getMatrix(rowindexset, columnindexset)), M);
try_success("setMatrix(int[],int[],Matrix)... ", "");
} catch (java.lang.RuntimeException e) {
errorCount =
try_failure(
errorCount, "setMatrix(int[],int[],Matrix)... ", "submatrix not successfully set");
}
} catch (java.lang.ArrayIndexOutOfBoundsException e1) {
errorCount =
try_failure(
errorCount,
"setMatrix(int[],int[],Matrix)... ",
"Unexpected ArrayIndexOutOfBoundsException");
}
/**
* Array-like methods: minus minusEquals plus plusEquals arrayLeftDivide arrayLeftDivideEquals
* arrayRightDivide arrayRightDivideEquals arrayTimes arrayTimesEquals uminus
*/
print("\nTesting array-like methods...\n");
S = new Matrix(columnwise, nonconformld);
R = Matrix.random(A.getRowDimension(), A.getColumnDimension());
A = R;
try {
S = A.minus(S);
errorCount =
try_failure(errorCount, "minus conformance check... ", "nonconformance not raised");
} catch (IllegalArgumentException e) {
try_success("minus conformance check... ", "");
}
if (A.minus(R).norm1() != 0.) {
errorCount =
try_failure(
errorCount,
"minus... ",
"(difference of identical Matrices is nonzero,\nSubsequent use of minus should be suspect)");
} else {
try_success("minus... ", "");
}
A = R.copy();
A.minusEquals(R);
Z = new Matrix(A.getRowDimension(), A.getColumnDimension());
try {
A.minusEquals(S);
errorCount =
try_failure(errorCount, "minusEquals conformance check... ", "nonconformance not raised");
} catch (IllegalArgumentException e) {
try_success("minusEquals conformance check... ", "");
}
if (A.minus(Z).norm1() != 0.) {
errorCount =
try_failure(
errorCount,
"minusEquals... ",
"(difference of identical Matrices is nonzero,\nSubsequent use of minus should be suspect)");
} else {
try_success("minusEquals... ", "");
}
A = R.copy();
B = Matrix.random(A.getRowDimension(), A.getColumnDimension());
C = A.minus(B);
try {
S = A.plus(S);
errorCount =
try_failure(errorCount, "plus conformance check... ", "nonconformance not raised");
} catch (IllegalArgumentException e) {
try_success("plus conformance check... ", "");
}
try {
check(C.plus(B), A);
try_success("plus... ", "");
} catch (java.lang.RuntimeException e) {
errorCount = try_failure(errorCount, "plus... ", "(C = A - B, but C + B != A)");
}
C = A.minus(B);
C.plusEquals(B);
try {
A.plusEquals(S);
errorCount =
try_failure(errorCount, "plusEquals conformance check... ", "nonconformance not raised");
} catch (IllegalArgumentException e) {
try_success("plusEquals conformance check... ", "");
}
try {
check(C, A);
try_success("plusEquals... ", "");
} catch (java.lang.RuntimeException e) {
errorCount = try_failure(errorCount, "plusEquals... ", "(C = A - B, but C = C + B != A)");
}
A = R.uminus();
try {
check(A.plus(R), Z);
try_success("uminus... ", "");
} catch (java.lang.RuntimeException e) {
errorCount = try_failure(errorCount, "uminus... ", "(-A + A != zeros)");
}
A = R.copy();
O = new Matrix(A.getRowDimension(), A.getColumnDimension(), 1.0);
C = A.arrayLeftDivide(R);
try {
S = A.arrayLeftDivide(S);
errorCount =
try_failure(
errorCount, "arrayLeftDivide conformance check... ", "nonconformance not raised");
} catch (IllegalArgumentException e) {
try_success("arrayLeftDivide conformance check... ", "");
}
try {
check(C, O);
try_success("arrayLeftDivide... ", "");
} catch (java.lang.RuntimeException e) {
errorCount = try_failure(errorCount, "arrayLeftDivide... ", "(M.\\M != ones)");
}
try {
A.arrayLeftDivideEquals(S);
errorCount =
try_failure(
errorCount,
"arrayLeftDivideEquals conformance check... ",
"nonconformance not raised");
} catch (IllegalArgumentException e) {
try_success("arrayLeftDivideEquals conformance check... ", "");
}
A.arrayLeftDivideEquals(R);
try {
check(A, O);
try_success("arrayLeftDivideEquals... ", "");
} catch (java.lang.RuntimeException e) {
errorCount = try_failure(errorCount, "arrayLeftDivideEquals... ", "(M.\\M != ones)");
}
A = R.copy();
try {
A.arrayRightDivide(S);
errorCount =
try_failure(
errorCount, "arrayRightDivide conformance check... ", "nonconformance not raised");
} catch (IllegalArgumentException e) {
try_success("arrayRightDivide conformance check... ", "");
}
C = A.arrayRightDivide(R);
try {
check(C, O);
try_success("arrayRightDivide... ", "");
} catch (java.lang.RuntimeException e) {
errorCount = try_failure(errorCount, "arrayRightDivide... ", "(M./M != ones)");
}
try {
A.arrayRightDivideEquals(S);
errorCount =
try_failure(
errorCount,
"arrayRightDivideEquals conformance check... ",
"nonconformance not raised");
} catch (IllegalArgumentException e) {
try_success("arrayRightDivideEquals conformance check... ", "");
}
A.arrayRightDivideEquals(R);
try {
check(A, O);
try_success("arrayRightDivideEquals... ", "");
} catch (java.lang.RuntimeException e) {
errorCount = try_failure(errorCount, "arrayRightDivideEquals... ", "(M./M != ones)");
}
A = R.copy();
B = Matrix.random(A.getRowDimension(), A.getColumnDimension());
try {
S = A.arrayTimes(S);
errorCount =
try_failure(errorCount, "arrayTimes conformance check... ", "nonconformance not raised");
} catch (IllegalArgumentException e) {
try_success("arrayTimes conformance check... ", "");
}
C = A.arrayTimes(B);
try {
check(C.arrayRightDivideEquals(B), A);
try_success("arrayTimes... ", "");
} catch (java.lang.RuntimeException e) {
errorCount = try_failure(errorCount, "arrayTimes... ", "(A = R, C = A.*B, but C./B != A)");
}
try {
A.arrayTimesEquals(S);
errorCount =
try_failure(
errorCount, "arrayTimesEquals conformance check... ", "nonconformance not raised");
} catch (IllegalArgumentException e) {
try_success("arrayTimesEquals conformance check... ", "");
}
A.arrayTimesEquals(B);
try {
check(A.arrayRightDivideEquals(B), R);
try_success("arrayTimesEquals... ", "");
} catch (java.lang.RuntimeException e) {
errorCount =
try_failure(errorCount, "arrayTimesEquals... ", "(A = R, A = A.*B, but A./B != R)");
}
/** I/O methods: read print serializable: writeObject readObject */
print("\nTesting I/O methods...\n");
try {
DecimalFormat fmt = new DecimalFormat("0.0000E00");
fmt.setDecimalFormatSymbols(new DecimalFormatSymbols(Locale.US));
PrintWriter FILE = new PrintWriter(new FileOutputStream("JamaTestMatrix.out"));
A.print(FILE, fmt, 10);
FILE.close();
R = Matrix.read(new BufferedReader(new FileReader("JamaTestMatrix.out")));
if (A.minus(R).norm1() < .001) {
try_success("print()/read()...", "");
} else {
errorCount =
try_failure(
errorCount,
"print()/read()...",
"Matrix read from file does not match Matrix printed to file");
}
} catch (java.io.IOException ioe) {
warningCount =
try_warning(
warningCount,
"print()/read()...",
"unexpected I/O error, unable to run print/read test; check write permission in current directory and retry");
} catch (Exception e) {
try {
e.printStackTrace(System.out);
warningCount =
try_warning(
warningCount,
"print()/read()...",
"Formatting error... will try JDK1.1 reformulation...");
DecimalFormat fmt = new DecimalFormat("0.0000");
PrintWriter FILE = new PrintWriter(new FileOutputStream("JamaTestMatrix.out"));
A.print(FILE, fmt, 10);
FILE.close();
R = Matrix.read(new BufferedReader(new FileReader("JamaTestMatrix.out")));
if (A.minus(R).norm1() < .001) {
try_success("print()/read()...", "");
} else {
errorCount =
try_failure(
errorCount,
"print()/read() (2nd attempt) ...",
"Matrix read from file does not match Matrix printed to file");
}
} catch (java.io.IOException ioe) {
warningCount =
try_warning(
warningCount,
"print()/read()...",
"unexpected I/O error, unable to run print/read test; check write permission in current directory and retry");
}
}
R = Matrix.random(A.getRowDimension(), A.getColumnDimension());
String tmpname = "TMPMATRIX.serial";
try {
@SuppressWarnings("resource")
ObjectOutputStream out = new ObjectOutputStream(new FileOutputStream(tmpname));
out.writeObject(R);
@SuppressWarnings("resource")
ObjectInputStream sin = new ObjectInputStream(new FileInputStream(tmpname));
A = (Matrix) sin.readObject();
try {
check(A, R);
try_success("writeObject(Matrix)/readObject(Matrix)...", "");
} catch (java.lang.RuntimeException e) {
errorCount =
try_failure(
errorCount,
"writeObject(Matrix)/readObject(Matrix)...",
"Matrix not serialized correctly");
}
} catch (java.io.IOException ioe) {
warningCount =
try_warning(
warningCount,
"writeObject()/readObject()...",
"unexpected I/O error, unable to run serialization test; check write permission in current directory and retry");
} catch (Exception e) {
errorCount =
try_failure(
errorCount,
"writeObject(Matrix)/readObject(Matrix)...",
"unexpected error in serialization test");
}
/**
* LA methods: transpose times cond rank det trace norm1 norm2 normF normInf solve
* solveTranspose inverse chol eig lu qr svd
*/
print("\nTesting linear algebra methods...\n");
A = new Matrix(columnwise, 3);
T = new Matrix(tvals);
T = A.transpose();
try {
check(A.transpose(), T);
try_success("transpose...", "");
} catch (java.lang.RuntimeException e) {
errorCount = try_failure(errorCount, "transpose()...", "transpose unsuccessful");
}
A.transpose();
try {
check(A.norm1(), columnsummax);
try_success("norm1...", "");
} catch (java.lang.RuntimeException e) {
errorCount = try_failure(errorCount, "norm1()...", "incorrect norm calculation");
}
try {
check(A.normInf(), rowsummax);
try_success("normInf()...", "");
} catch (java.lang.RuntimeException e) {
errorCount = try_failure(errorCount, "normInf()...", "incorrect norm calculation");
}
try {
check(A.normF(), Math.sqrt(sumofsquares));
try_success("normF...", "");
} catch (java.lang.RuntimeException e) {
errorCount = try_failure(errorCount, "normF()...", "incorrect norm calculation");
}
try {
check(A.trace(), sumofdiagonals);
try_success("trace()...", "");
} catch (java.lang.RuntimeException e) {
errorCount = try_failure(errorCount, "trace()...", "incorrect trace calculation");
}
try {
check(A.getMatrix(0, A.getRowDimension() - 1, 0, A.getRowDimension() - 1).det(), 0.);
try_success("det()...", "");
} catch (java.lang.RuntimeException e) {
errorCount = try_failure(errorCount, "det()...", "incorrect determinant calculation");
}
SQ = new Matrix(square);
try {
check(A.times(A.transpose()), SQ);
try_success("times(Matrix)...", "");
} catch (java.lang.RuntimeException e) {
errorCount =
try_failure(
errorCount, "times(Matrix)...", "incorrect Matrix-Matrix product calculation");
}
try {
check(A.times(0.), Z);
try_success("times(double)...", "");
} catch (java.lang.RuntimeException e) {
errorCount =
try_failure(
errorCount, "times(double)...", "incorrect Matrix-scalar product calculation");
}
A = new Matrix(columnwise, 4);
QRDecomposition QR = A.qr();
R = QR.getR();
try {
check(A, QR.getQ().times(R));
try_success("QRDecomposition...", "");
} catch (java.lang.RuntimeException e) {
errorCount =
try_failure(errorCount, "QRDecomposition...", "incorrect QR decomposition calculation");
}
SingularValueDecomposition SVD = A.svd();
try {
check(A, SVD.getU().times(SVD.getS().times(SVD.getV().transpose())));
try_success("SingularValueDecomposition...", "");
} catch (java.lang.RuntimeException e) {
errorCount =
try_failure(
errorCount,
"SingularValueDecomposition...",
"incorrect singular value decomposition calculation");
}
DEF = new Matrix(rankdef);
try {
check(DEF.rank(), Math.min(DEF.getRowDimension(), DEF.getColumnDimension()) - 1);
try_success("rank()...", "");
} catch (java.lang.RuntimeException e) {
errorCount = try_failure(errorCount, "rank()...", "incorrect rank calculation");
}
B = new Matrix(condmat);
SVD = B.svd();
double[] singularvalues = SVD.getSingularValues();
try {
check(
B.cond(),
singularvalues[0]
/ singularvalues[Math.min(B.getRowDimension(), B.getColumnDimension()) - 1]);
try_success("cond()...", "");
} catch (java.lang.RuntimeException e) {
errorCount = try_failure(errorCount, "cond()...", "incorrect condition number calculation");
}
int n = A.getColumnDimension();
A = A.getMatrix(0, n - 1, 0, n - 1);
A.set(0, 0, 0.);
LUDecomposition LU = A.lu();
try {
check(A.getMatrix(LU.getPivot(), 0, n - 1), LU.getL().times(LU.getU()));
try_success("LUDecomposition...", "");
} catch (java.lang.RuntimeException e) {
errorCount =
try_failure(errorCount, "LUDecomposition...", "incorrect LU decomposition calculation");
}
X = A.inverse();
try {
check(A.times(X), Matrix.identity(3, 3));
try_success("inverse()...", "");
} catch (java.lang.RuntimeException e) {
errorCount = try_failure(errorCount, "inverse()...", "incorrect inverse calculation");
}
O = new Matrix(SUB.getRowDimension(), 1, 1.0);
SOL = new Matrix(sqSolution);
SQ = SUB.getMatrix(0, SUB.getRowDimension() - 1, 0, SUB.getRowDimension() - 1);
try {
check(SQ.solve(SOL), O);
try_success("solve()...", "");
} catch (java.lang.IllegalArgumentException e1) {
errorCount = try_failure(errorCount, "solve()...", e1.getMessage());
} catch (java.lang.RuntimeException e) {
errorCount = try_failure(errorCount, "solve()...", e.getMessage());
}
A = new Matrix(pvals);
CholeskyDecomposition Chol = A.chol();
Matrix L = Chol.getL();
try {
check(A, L.times(L.transpose()));
try_success("CholeskyDecomposition...", "");
} catch (java.lang.RuntimeException e) {
errorCount =
try_failure(
errorCount,
"CholeskyDecomposition...",
"incorrect Cholesky decomposition calculation");
}
X = Chol.solve(Matrix.identity(3, 3));
try {
check(A.times(X), Matrix.identity(3, 3));
try_success("CholeskyDecomposition solve()...", "");
} catch (java.lang.RuntimeException e) {
errorCount =
try_failure(
errorCount,
"CholeskyDecomposition solve()...",
"incorrect Choleskydecomposition solve calculation");
}
EigenvalueDecomposition Eig = A.eig();
Matrix D = Eig.getD();
Matrix V = Eig.getV();
try {
check(A.times(V), V.times(D));
try_success("EigenvalueDecomposition (symmetric)...", "");
} catch (java.lang.RuntimeException e) {
errorCount =
try_failure(
errorCount,
"EigenvalueDecomposition (symmetric)...",
"incorrect symmetric Eigenvalue decomposition calculation");
}
A = new Matrix(evals);
Eig = A.eig();
D = Eig.getD();
V = Eig.getV();
try {
check(A.times(V), V.times(D));
try_success("EigenvalueDecomposition (nonsymmetric)...", "");
} catch (java.lang.RuntimeException e) {
errorCount =
try_failure(
errorCount,
"EigenvalueDecomposition (nonsymmetric)...",
"incorrect nonsymmetric Eigenvalue decomposition calculation");
}
try {
print("\nTesting Eigenvalue; If this hangs, we've failed\n");
Matrix bA = new Matrix(badeigs);
EigenvalueDecomposition bEig = bA.eig();
try_success("EigenvalueDecomposition (hang)...", "");
} catch (java.lang.RuntimeException e) {
errorCount =
try_failure(errorCount, "EigenvalueDecomposition (hang)...", "incorrect termination");
}
print("\nTestMatrix completed.\n");
print("Total errors reported: " + Integer.toString(errorCount) + "\n");
print("Total warnings reported: " + Integer.toString(warningCount) + "\n");
}
@Override
public void run() {
amIActive = true;
String shapefile = null;
String inputFieldsString = null;
String[] fieldNames = null;
double z;
int numFields;
int progress = 0;
int lastProgress = 0;
int row;
int a, i, j;
double[] fieldAverages;
double[] fieldTotals;
boolean standardizedPCA = false;
int numberOfComponentsOutput = 0;
if (args.length <= 0) {
showFeedback("Plugin parameters have not been set.");
return;
}
// read the input parameters
inputFieldsString = args[0];
standardizedPCA = Boolean.parseBoolean(args[1]);
if (args[2].toLowerCase().contains("not")) { // not specified
numberOfComponentsOutput = 0;
} else {
numberOfComponentsOutput = Integer.parseInt(args[2]);
}
try {
// deal with the input fields
String[] inputs = inputFieldsString.split(";");
shapefile = inputs[0];
numFields = inputs.length - 1;
fieldNames = new String[numFields];
System.arraycopy(inputs, 1, fieldNames, 0, numFields);
// read the appropriate field from the dbf file into an array
AttributeTable table = new AttributeTable(shapefile.replace(".shp", ".dbf"));
int numRecs = table.getNumberOfRecords();
DBFField[] fields = table.getAllFields();
ArrayList<Integer> PCAFields = new ArrayList<>();
for (j = 0; j < fieldNames.length; j++) {
for (i = 0; i < fields.length; i++) {
if (fields[i].getName().equals(fieldNames[j])
&& (fields[i].getDataType() == DBFField.DBFDataType.NUMERIC
|| fields[i].getDataType() == DBFField.DBFDataType.FLOAT)) {
PCAFields.add(i);
}
}
}
if (numFields != PCAFields.size()) {
showFeedback(
"Not all of the specified database fields were found in the file or "
+ "a field of a non-numerical type was selected.");
return;
}
double[][] fieldArray = new double[numRecs][numFields];
Object[] rec;
for (i = 0; i < numRecs; i++) {
rec = table.getRecord(i);
for (j = 0; j < numFields; j++) {
fieldArray[i][j] = (Double) (rec[PCAFields.get(j)]);
}
if (cancelOp) {
cancelOperation();
return;
}
progress = (int) (100f * i / (numRecs - 1));
if (progress != lastProgress) {
updateProgress("Reading data:", progress);
}
lastProgress = progress;
}
fieldAverages = new double[numFields];
fieldTotals = new double[numFields];
// Calculate the means
for (row = 0; row < numRecs; row++) {
for (i = 0; i < numFields; i++) {
fieldTotals[i] += fieldArray[row][i];
}
}
for (i = 0; i < numFields; i++) {
fieldAverages[i] = fieldTotals[i] / numRecs;
}
// Calculate the covariance matrix and total deviations
double[] fieldTotalDeviation = new double[numFields];
double[][] covariances = new double[numFields][numFields];
double[][] correlationMatrix = new double[numFields][numFields];
for (row = 0; row < numRecs; row++) {
for (i = 0; i < numFields; i++) {
fieldTotalDeviation[i] +=
(fieldArray[row][i] - fieldAverages[i]) * (fieldArray[row][i] - fieldAverages[i]);
for (a = 0; a < numFields; a++) {
covariances[i][a] +=
(fieldArray[row][i] - fieldAverages[i]) * (fieldArray[row][a] - fieldAverages[a]);
}
}
if (cancelOp) {
cancelOperation();
return;
}
progress = (int) (100f * row / (numRecs - 1));
if (progress != lastProgress) {
updateProgress("Calculating covariances:", progress);
}
lastProgress = progress;
}
for (i = 0; i < numFields; i++) {
for (a = 0; a < numFields; a++) {
correlationMatrix[i][a] =
covariances[i][a] / (Math.sqrt(fieldTotalDeviation[i] * fieldTotalDeviation[a]));
}
}
for (i = 0; i < numFields; i++) {
for (a = 0; a < numFields; a++) {
covariances[i][a] = covariances[i][a] / (numRecs - 1);
}
}
// Calculate the eigenvalues and eigenvectors
Matrix cov = null;
if (!standardizedPCA) {
cov = new Matrix(covariances);
} else {
cov = new Matrix(correlationMatrix);
}
EigenvalueDecomposition eigen = cov.eig();
double[] eigenvalues;
Matrix eigenvectors;
SortedSet<PrincipalComponent> principalComponents;
eigenvalues = eigen.getRealEigenvalues();
eigenvectors = eigen.getV();
double[][] vecs = eigenvectors.getArray();
int numComponents = eigenvectors.getColumnDimension(); // same as num rows.
principalComponents = new TreeSet<PrincipalComponent>();
for (i = 0; i < numComponents; i++) {
double[] eigenvector = new double[numComponents];
for (j = 0; j < numComponents; j++) {
eigenvector[j] = vecs[j][i];
}
principalComponents.add(new PrincipalComponent(eigenvalues[i], eigenvector));
}
double totalEigenvalue = 0;
for (i = 0; i < numComponents; i++) {
totalEigenvalue += eigenvalues[i];
}
double[][] explainedVarianceArray = new double[numComponents][2]; // percent and cum. percent
j = 0;
for (PrincipalComponent pc : principalComponents) {
explainedVarianceArray[j][0] = pc.eigenValue / totalEigenvalue * 100.0;
if (j == 0) {
explainedVarianceArray[j][1] = explainedVarianceArray[j][0];
} else {
explainedVarianceArray[j][1] =
explainedVarianceArray[j][0] + explainedVarianceArray[j - 1][1];
}
j++;
}
DecimalFormat df1 = new DecimalFormat("0.00");
DecimalFormat df2 = new DecimalFormat("0.0000");
DecimalFormat df3 = new DecimalFormat("0.000000");
DecimalFormat df4 = new DecimalFormat("0.000");
String ret = "Principal Component Analysis Report:\n\n";
ret += "Component\tExplained Var.\tCum. %\tEigenvalue\tEigenvector\n";
j = 0;
for (PrincipalComponent pc : principalComponents) {
String explainedVariance = df1.format(explainedVarianceArray[j][0]);
String explainedCumVariance = df1.format(explainedVarianceArray[j][1]);
double[] eigenvector = pc.eigenVector.clone();
ret +=
(j + 1)
+ "\t"
+ explainedVariance
+ "\t"
+ explainedCumVariance
+ "\t"
+ df2.format(pc.eigenValue)
+ "\t";
String eigenvec = "[";
for (i = 0; i < numComponents; i++) {
if (i < numComponents - 1) {
eigenvec += df3.format(eigenvector[i]) + ", ";
} else {
eigenvec += df3.format(eigenvector[i]);
}
}
eigenvec += "]";
ret += eigenvec + "\n";
if (j < numberOfComponentsOutput) {
DBFField field = new DBFField();
field = new DBFField();
field.setName("COMP" + (j + 1));
field.setDataType(DBFField.DBFDataType.NUMERIC);
field.setFieldLength(10);
field.setDecimalCount(4);
table.addField(field);
for (row = 0; row < numRecs; row++) {
z = 0;
for (i = 0; i < numFields; i++) {
z += fieldArray[row][i] * eigenvector[i];
}
Object[] recData = table.getRecord(row);
recData[recData.length - 1] = new Double(z);
table.updateRecord(row, recData);
if (cancelOp) {
cancelOperation();
return;
}
progress = (int) (100f * row / (numRecs - 1));
if (progress != lastProgress) {
updateProgress("Outputing Component " + (j + 1) + ":", progress);
}
lastProgress = progress;
}
}
j++;
}
// calculate the factor loadings.
ret += "\nFactor Loadings:\n";
ret += "\t\tComponent\n\t";
for (i = 0; i < numComponents; i++) {
ret += (i + 1) + "\t";
}
ret += "\n";
double loading = 0;
if (!standardizedPCA) {
for (i = 0; i < numFields; i++) {
ret += "field " + (i + 1) + "\t";
for (PrincipalComponent pc : principalComponents) {
double[] eigenvector = pc.eigenVector.clone();
double ev = pc.eigenValue;
loading = (eigenvector[i] * Math.sqrt(ev)) / Math.sqrt(covariances[i][i]);
ret += df4.format(loading) + "\t";
}
ret += "\n";
}
} else {
for (i = 0; i < numFields; i++) {
ret += "field " + (i + 1) + "\t";
for (PrincipalComponent pc : principalComponents) {
double[] eigenvector = pc.eigenVector.clone();
double ev = pc.eigenValue;
loading = (eigenvector[i] * Math.sqrt(ev));
ret += df4.format(loading) + "\t";
}
ret += "\n";
}
}
ret += "\n";
for (i = 0; i < numFields; i++) {
ret += "field " + (i + 1) + "\t" + fieldNames[i] + "\n";
}
returnData(ret);
if (numberOfComponentsOutput > 0) {
returnData(table.getFileName());
}
ScreePlot plot = new ScreePlot(explainedVarianceArray);
returnData(plot);
} catch (OutOfMemoryError oe) {
myHost.showFeedback("An out-of-memory error has occurred during operation.");
} catch (Exception e) {
myHost.showFeedback("An error has occurred during operation. See log file for details.");
myHost.logException("Error in " + getDescriptiveName(), e);
} finally {
updateProgress("Progress: ", 0);
// tells the main application that this process is completed.
amIActive = false;
myHost.pluginComplete();
}
}
/**
* Calculates the 3 MI's, 3 ration and the R_gyr value.
*
* <p>The molecule should have hydrogens
*
* @param container Parameter is the atom container.
* @return An ArrayList containing 7 elements in the order described above
*/
@TestMethod("testCalculate_IAtomContainer")
public DescriptorValue calculate(IAtomContainer container) {
if (!GeometryTools.has3DCoordinates(container))
return getDummyDescriptorValue(new CDKException("Molecule must have 3D coordinates"));
IAtomContainer clone;
IsotopeFactory factory;
try {
clone = (IAtomContainer) container.clone();
factory = IsotopeFactory.getInstance(container.getBuilder());
factory.configureAtoms(clone);
} catch (Exception e) {
logger.debug(e);
return getDummyDescriptorValue(e);
}
DoubleArrayResult retval = new DoubleArrayResult(7);
double ccf = 1.000138;
double eps = 1e-5;
double[][] imat = new double[3][3];
Point3d centerOfMass = GeometryTools.get3DCentreOfMass(clone);
double xdif;
double ydif;
double zdif;
double xsq;
double ysq;
double zsq;
for (int i = 0; i < clone.getAtomCount(); i++) {
IAtom currentAtom = clone.getAtom(i);
double mass = factory.getMajorIsotope(currentAtom.getSymbol()).getExactMass();
xdif = currentAtom.getPoint3d().x - centerOfMass.x;
ydif = currentAtom.getPoint3d().y - centerOfMass.y;
zdif = currentAtom.getPoint3d().z - centerOfMass.z;
xsq = xdif * xdif;
ysq = ydif * ydif;
zsq = zdif * zdif;
imat[0][0] += mass * (ysq + zsq);
imat[1][1] += mass * (xsq + zsq);
imat[2][2] += mass * (xsq + ysq);
imat[1][0] += -1 * mass * ydif * xdif;
imat[0][1] = imat[1][0];
imat[2][0] += -1 * mass * xdif * zdif;
imat[0][2] = imat[2][0];
imat[2][1] += -1 * mass * ydif * zdif;
imat[1][2] = imat[2][1];
}
// diagonalize the MI tensor
Matrix tmp = new Matrix(imat);
EigenvalueDecomposition eigenDecomp = tmp.eig();
double[] eval = eigenDecomp.getRealEigenvalues();
retval.add(eval[2]);
retval.add(eval[1]);
retval.add(eval[0]);
double etmp = eval[0];
eval[0] = eval[2];
eval[2] = etmp;
if (Math.abs(eval[1]) > 1e-3) retval.add(eval[0] / eval[1]);
else retval.add(1000);
if (Math.abs(eval[2]) > 1e-3) {
retval.add(eval[0] / eval[2]);
retval.add(eval[1] / eval[2]);
} else {
retval.add(1000);
retval.add(1000);
}
// finally get the radius of gyration
double pri;
IMolecularFormula formula = MolecularFormulaManipulator.getMolecularFormula(clone);
if (Math.abs(eval[2]) > eps) pri = Math.pow(eval[0] * eval[1] * eval[2], 1.0 / 3.0);
else pri = Math.sqrt(eval[0] * ccf / MolecularFormulaManipulator.getTotalExactMass(formula));
retval.add(
Math.sqrt(
Math.PI * 2 * pri * ccf / MolecularFormulaManipulator.getTotalExactMass(formula)));
return new DescriptorValue(
getSpecification(), getParameterNames(), getParameters(), retval, getDescriptorNames());
}
private double generalizedCorrelationRatio(SampleIterator it, int inputDim, int out) {
Map<Double, Integer> n_y = new HashMap<>();
Map<Double, MultivariateSummaryStatistics> stat_y = new HashMap<>();
List<RealMatrix> x = new ArrayList<>();
MultivariateSummaryStatistics stat = new MultivariateSummaryStatistics(inputDim, unbiased);
for (int i = 0; i < maxSamples && it.hasNext(); i++) {
Sample sample = it.next();
double[] input = sample.getEncodedInput().toArray();
double output = sample.getEncodedOutput().getEntry(out);
if (!n_y.containsKey(output)) {
n_y.put(output, 0);
stat_y.put(output, new MultivariateSummaryStatistics(inputDim, unbiased));
}
injectNoise(input);
n_y.put(output, n_y.get(output) + 1);
stat_y.get(output).addValue(input);
x.add(new Array2DRowRealMatrix(input));
stat.addValue(input);
}
RealMatrix x_sum = new Array2DRowRealMatrix(stat.getSum());
Map<Double, RealMatrix> x_y_sum = new HashMap<>();
for (Entry<Double, MultivariateSummaryStatistics> entry : stat_y.entrySet()) {
x_y_sum.put(entry.getKey(), new Array2DRowRealMatrix(entry.getValue().getSum()));
}
RealMatrix H = new Array2DRowRealMatrix(inputDim, inputDim);
RealMatrix temp = new Array2DRowRealMatrix(inputDim, inputDim);
for (double key : n_y.keySet()) {
temp =
temp.add(
x_y_sum
.get(key)
.multiply(x_y_sum.get(key).transpose())
.scalarMultiply(1.0 / n_y.get(key)));
}
H = temp.subtract(x_sum.multiply(x_sum.transpose()).scalarMultiply(1.0 / x.size()));
RealMatrix E = new Array2DRowRealMatrix(inputDim, inputDim);
for (RealMatrix m : x) {
E = E.add(m.multiply(m.transpose()));
}
E = E.subtract(temp);
List<Integer> zeroColumns = findZeroColumns(E);
E = removeZeroColumns(E, zeroColumns);
H = removeZeroColumns(H, zeroColumns);
Matrix JE = new Matrix(E.getData());
Matrix JH = new Matrix(H.getData());
if (JE.rank() < JE.getRowDimension()) {
Log.write(this, "Some error occurred (E matrix is singular)");
return -1;
} else {
double lambda;
if (useEigenvalues) {
Matrix L = JE.inverse().times(JH);
double[] eigs = L.eig().getRealEigenvalues();
Arrays.sort(eigs);
lambda = 1;
int nonNullEigs = n_y.keySet().size() - 1;
for (int i = eigs.length - nonNullEigs; i < eigs.length; i++) {
if (Math.abs(eigs[i]) < zeroThreshold) {
Log.write(this, "Some error occurred (E matrix has too many null eigenvalues)");
return -1;
}
lambda *= 1.0 / (1.0 + eigs[i]);
}
} else {
Matrix sum = JE.plus(JH);
if (sum.rank() < sum.getRowDimension()) {
Log.write(this, "Some error occourred (E+H is singular");
return -1;
}
lambda = JE.det() / sum.det();
}
return Math.sqrt(1 - lambda);
}
}
public boolean calcNearPD(Matrix x) {
int n = x.getRowDimension();
Matrix X;
// Init local variables
double[] diagX0 = new double[n];
double[] d = new double[n];
Matrix D_S = new Matrix(n, n);
Matrix R = new Matrix(n, n);
Matrix Y = new Matrix(n, n);
EigenvalueDecomposition eig;
Matrix D_plus = new Matrix(n, n);
Matrix Q = new Matrix(n, n);
if (keepDiag) {
for (int i = 0; i < n; i++) {
diagX0[i] = x.get(i, i);
}
}
X = x.copy();
// Set iteration, convergence criteria
int iter = 0;
boolean converged = false;
double conv = Double.POSITIVE_INFINITY;
// Loop
while ((iter < maxit) & !converged) {
Y = X.copy();
// Dykstra correction
if (doDykstra) {
R = Y.minus(D_S);
}
// project onto PSD matrices X_k = P_S (R_k)
if (doDykstra) {
eig = R.eig();
} else {
eig = Y.eig();
}
d = eig.getRealEigenvalues();
Q = eig.getV();
// Get the maximum eigenvalue
double eigMax = Double.NEGATIVE_INFINITY;
for (int i = 0; i < n; i++) {
if (d[i] > eigMax) eigMax = d[i];
}
// compute the D_plus diagonal matricies
for (int i = 0; i < n; i++) {
double d_plus = Math.max(d[i], eigTol * eigMax);
D_plus.set(i, i, d_plus);
}
X = (Q.times(D_plus)).times(Q.transpose());
// Update Dykstra correction
if (doDykstra) D_S = X.minus(R);
// project onto symmetric and possibly 'given diag' matrices:
if (keepDiag) {
for (int i = 0; i < n; i++) {
X.set(i, i, diagX0[i]);
}
}
// update convergence and iteration values
conv = (Y.minus(X)).normInf() / Y.normInf();
iter = iter + 1;
// check convergence criteria
if (conv <= convTol) converged = true;
}
// Set solution local variables as globals
this.X = X;
this.conv = conv;
this.normF = (x.minus(X)).normF();
this.iter = iter;
this.eigVals = d;
return converged;
}
/**
* This method tests the NearPD class for a approx correlation matrix. This test has been
* implemented in Matlab for verification
*
* @param args
*/
public static void main(String[] args) {
// int n=11;
int n = 10;
// int n=17;
double[][] rho = new double[n][n];
// String rhoString=
// "1.0000 0.9459 0.8018 0.6580 0.5526 0.4665 0.3909
// 0.3200 0.2486 0.1682 -0.0946 -0.1157 -0.1355 0.9652 0.2520
// 0.8299 0.5164 " +
// "0.9459 1.0000 0.7642 0.6108 0.5027 0.4173 0.3446
// 0.2785 0.2139 0.1432 -0.1256 -0.1358 -0.1252 0.9446 0.3705
// 0.8809 0.5569 " +
// "0.8018 0.7642 1.0000 0.8139 0.6787 0.5695 0.4745
// 0.3865 0.2989 0.2014 -0.1337 -0.1508 -0.1513 0.8722 0.2323
// 0.9336 0.4744 " +
// "0.6580 0.6108 0.8139 1.0000 0.8398 0.7086 0.5931
// 0.4850 0.3764 0.2543 -0.1279 -0.1470 -0.1526 0.7762 0.2969
// 0.9358 0.4703 " +
// "0.5526 0.5027 0.6787 0.8398 1.0000 0.8465 0.7105
// 0.5823 0.4529 0.3066 -0.1182 -0.1372 -0.1452 0.6840 0.4004
// 0.8400 0.4664 " +
// "0.4665 0.4173 0.5695 0.7086 0.8465 1.0000 0.8409
// 0.6903 0.5376 0.3643 -0.1069 -0.1249 -0.1337 0.5948 0.4537
// 0.7401 0.4421 " +
// "0.3909 0.3446 0.4745 0.5931 0.7105 0.8409 1.0000
// 0.8219 0.6407 0.4346 -0.0944 -0.1109 -0.1197 0.5070 0.4544
// 0.6354 0.4050 " +
// "0.3200 0.2785 0.3865 0.4850 0.5823 0.6903 0.8219
// 1.0000 0.7802 0.5296 -0.0807 -0.0951 -0.1033 0.4188 0.4166
// 0.5261 0.3618 " +
// "0.2486 0.2139 0.2989 0.3764 0.4529 0.5376 0.6407
// 0.7802 1.0000 0.6792 -0.0650 -0.0768 -0.0839 0.3265 0.3494
// 0.4097 0.2997 " +
// "0.1682 0.1432 0.2014 0.2543 0.3066 0.3643 0.4346
// 0.5296 0.6792 1.0000 -0.0453 -0.0537 -0.0589 0.2207 0.2495
// 0.2760 0.2086 " +
// "-0.0946 -0.1256 -0.1337 -0.1279 -0.1182 -0.1069 -0.0944
// -0.0807 -0.0650 -0.0453 1.0000 0.7538 0.5695 -0.0437 0.7922
// -0.0830 0.4212 " +
// "-0.1157 -0.1358 -0.1508 -0.1470 -0.1372 -0.1249 -0.1109
// -0.0951 -0.0768 -0.0537 0.7538 1.0000 0.7648 -0.0076 0.8310
// -0.0258 0.5696 " +
// "-0.1355 -0.1252 -0.1513 -0.1526 -0.1452 -0.1337 -0.1197
// -0.1033 -0.0839 -0.0589 0.5695 0.7648 1.0000 0.0581 0.8930
// 0.0665 0.6871 " +
// "0.9652 0.9446 0.8722 0.7762 0.6840 0.5948 0.5070
// 0.4188 0.3265 0.2207 -0.0437 -0.0076 0.0581 1.0000 0.2931
// 0.8956 0.5861 " +
// "0.2520 0.3705 0.2323 0.2969 0.4004 0.4537 0.4544
// 0.4166 0.3494 0.2495 0.7922 0.8310 0.8930 0.2931 1.0000
// 0.3191 0.6704 " +
// "0.8299 0.8809 0.9336 0.9358 0.8400 0.7401 0.6354
// 0.5261 0.4097 0.2760 -0.0830 -0.0258 0.0665 0.8956 0.3191
// 1.0000 0.5372 " +
// "0.5164 0.5569 0.4744 0.4703 0.4664 0.4421 0.4050
// 0.3618 0.2997 0.2086 0.4212 0.5696 0.6871 0.5861 0.6704
// 0.5372 1.0000";
String rhoString =
"1.0000 0.8646 0.5412 -0.0070 -0.4291 -0.2727 0.5448 0.4981 -0.4579 -0.4481 "
+ "0.8646 1.0000 0.5343 -0.0141 -0.4832 -0.4705 0.4392 0.4716 -0.3771 -0.3660 "
+ "0.5412 0.5343 1.0000 0.0427 -0.7999 -0.8240 0.2005 0.1926 -0.2985 -0.2684 "
+ "-0.0070 -0.0141 0.0427 1.0000 -0.4071 -0.8087 -0.1396 -0.1775 -0.0304 -0.0340 "
+ "-0.4291 -0.4832 -0.7999 -0.4071 1.0000 0.0594 -0.1627 -0.2604 0.2474 0.2541 "
+ "-0.2727 -0.4705 -0.8240 -0.8087 0.0594 1.0000 0.2672 0.0173 0.3205 0.3749 "
+ "0.5448 0.4392 0.2005 -0.1396 -0.1627 0.2672 1.0000 0.2581 -0.3109 -0.3051 "
+ "0.4981 0.4716 0.1926 -0.1775 -0.2604 0.0173 0.2581 1.0000 0.2289 0.2408 "
+ "-0.4579 -0.3771 -0.2985 -0.0304 0.2474 0.3205 -0.3109 0.2289 1.0000 0.8425 "
+ "-0.4481 -0.3660 -0.2684 -0.0340 0.2541 0.3749 -0.3051 0.2408 0.8425 1.0000";
// String rhoString=" 1.000 0.399 0.426 0.484 0.585 0.713 0.822 0.887 0.918 0.929 0.897 " +
// "0.399 1.000 0.911 0.791 0.675 0.563 0.458 0.360 0.272 0.195 0.129 " +
// "0.426 0.911 1.000 0.878 0.759 0.643 0.532 0.428 0.332 0.247 0.173 " +
// "0.484 0.791 0.878 1.000 0.878 0.759 0.643 0.532 0.428 0.332 0.247 " +
// "0.585 0.675 0.759 0.878 1.000 0.878 0.759 0.643 0.532 0.428 0.332 " +
// "0.713 0.563 0.643 0.759 0.878 1.000 0.878 0.759 0.643 0.532 0.428 " +
// "0.822 0.458 0.532 0.643 0.759 0.878 1.000 0.878 0.759 0.643 0.532 " +
// "0.887 0.360 0.428 0.532 0.643 0.759 0.878 1.000 0.878 0.759 0.643 " +
// "0.918 0.272 0.332 0.428 0.532 0.643 0.759 0.878 1.000 0.878 0.759 " +
// "0.929 0.195 0.247 0.332 0.428 0.532 0.643 0.759 0.878 1.000 0.878 " +
// "0.897 0.129 0.173 0.247 0.332 0.428 0.532 0.643 0.759 0.878 1.000";
// Create string tokenizer
StringTokenizer st = new StringTokenizer(rhoString);
for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) {
rho[i][j] = Double.parseDouble(st.nextToken().trim());
}
}
Matrix rho_matrix = new Matrix(rho);
// CholeskyDecomposition chol = rho_matrix.chol();
CholeskyDecomposition chol = new CholeskyDecomposition(rho_matrix);
if (!chol.isSPD()) System.out.println("The rho matrix is not SPD");
// Get the eigen values for this matrix
EigenvalueDecomposition eig = rho_matrix.eig();
String eigValsString = "";
double[] eigVals = eig.getRealEigenvalues();
for (int i = 0; i < n; i++)
eigValsString = eigValsString + " " + Math.round(eigVals[i] * 1000) / 1000.;
System.out.println("The eigVals are: " + eigValsString);
NearPD nearPd = new NearPD();
nearPd.setKeepDiag(true);
nearPd.calcNearPD(rho_matrix);
Matrix rho_PDmatrix = nearPd.getX();
String rhoPDString_Matlab =
"1.0000 0.3944 0.4262 0.4865 0.5863 0.7108 0.8179 0.8836 0.9155 0.9246 0.8882 "
+ "0.3944 1.0000 0.9110 0.7905 0.6747 0.5635 0.4589 0.3607 0.2725 0.1959 0.1308 "
+ "0.4262 0.9110 1.0000 0.8780 0.7590 0.6430 0.5320 0.4280 0.3320 0.2470 0.1729 "
+ "0.4865 0.7905 0.8780 1.0000 0.8782 0.7588 0.6425 0.5316 0.4277 0.3315 0.2460 "
+ "0.5863 0.6747 0.7590 0.8782 1.0000 0.8779 0.7587 0.6428 0.5318 0.4277 0.3315 "
+ "0.7108 0.5635 0.6430 0.7588 0.8779 1.0000 0.8784 0.7593 0.6432 0.5324 0.4289 "
+ "0.8179 0.4589 0.5320 0.6425 0.7587 0.8784 1.0000 0.8787 0.7595 0.6438 0.5337 "
+ "0.8836 0.3607 0.4280 0.5316 0.6428 0.7593 0.8787 1.0000 0.8784 0.7597 0.6444 "
+ "0.9155 0.2725 0.3320 0.4277 0.5318 0.6432 0.7595 0.8784 1.0000 0.8785 0.7600 "
+ "0.9246 0.1959 0.2470 0.3315 0.4277 0.5324 0.6438 0.7597 0.8785 1.0000 0.8798 "
+ "0.8882 0.1308 0.1729 0.2460 0.3315 0.4289 0.5337 0.6444 0.7600 0.8798 1.0000";
// Output the PD matrix and the convergence parameters
System.out.println("Testing NearPD: nearest PD matrix \n " + "Rho_PD = \n");
rho_PDmatrix.print(12, 8);
CholeskyDecomposition cholDecompPD = new CholeskyDecomposition(rho_PDmatrix);
if (!cholDecompPD.isSPD()) {
throw new RuntimeException("Error: Even after NearPD the matrix is not PD");
}
}