public SparseMatrix getRedundant() {
SparseMatrix redMat = new SparseMatrix(this).multiply(this);
int oldCard = 0;
int card = redMat.cardinality();
int i = 0;
// for(int r = 0; r < rowDim; r++){
// if(true){//transMat.hasRow(r)){
// transMat.add(r, r, 1.0);
// }
// if(true){//tcMat.hasRow(r)){
// tcMat.add(r, r, 1.0);
// }
// }
while (card > oldCard) {
// System.out.println("iter: "+i);
i++;
oldCard = card;
redMat = redMat.add(redMat.multiply(this));
card = redMat.cardinality();
// System.out.println(tcMat.toString());
}
return redMat;
}
public SparseMatrix transitiveClosure() {
SparseMatrix tcMat = new SparseMatrix(this);
SparseMatrix transMat = new SparseMatrix(this);
int oldCard = 0;
int card = tcMat.cardinality();
int i = 0;
// for(int r = 0; r < rowDim; r++){
// if(true){//transMat.hasRow(r)){
// transMat.add(r, r, 1.0);
// }
// if(true){//tcMat.hasRow(r)){
// tcMat.add(r, r, 1.0);
// }
// }
while (card > oldCard) {
// System.out.println("iter: "+i);
i++;
oldCard = card;
tcMat = tcMat.add(tcMat.multiply(transMat));
card = tcMat.cardinality();
// System.out.println(tcMat.toString());
}
return tcMat;
}
public static void main(String[] args) {
long start;
long end;
int card;
double seconds;
double megaTEPs;
System.out.println("Working");
Random rand = new Random();
int dim = 16384;
int num = dim * 10;
SparseMatrix testMat = new SparseMatrix(dim, dim);
for (int i = 0; i < num; i++) {
if ((i % (1024 * 32 * 20) == 0)) System.out.println(i + " of " + num);
int r = rand.nextInt(dim);
int c = rand.nextInt(dim);
// int r = (i+2659)*3571%dim;
// int c = (i+2659)*2081%dim;
testMat.add(r, c, 1.0);
testMat.add(c, r, 1.0);
// testMat.add(r, r, 1.0);
// testMat.add(c, c, 1.0);
// testMat.set(r, c, 1.0);
// testMat.set(c, r, 1.0);
}
boolean isSymm = true;
int sum = 0;
for (int r = 0; r < dim; r++) {
for (int c = 0; c < dim; c++) {
double val = testMat.get(r, c);
sum += val;
if (val > 0.0) {
if (testMat.get(c, r) == 0) {
isSymm = false;
}
}
}
}
System.out.println(sum + " " + testMat.cardinality() + " " + isSymm);
// SparseMatrix testMat2 = new SparseMatrix(dim, dim);
// for (int i = 0; i < num; i++) {
// int r = rand.nextInt(dim);
// int c = rand.nextInt(dim);testMat
// testMat2.add(r, c, 1.0);
// }
// start = System.currentTimeMillis();
// SparseMatrix testMat3 = testMat.multiply(testMat2);
// end = System.currentTimeMillis();
// System.out.println("Time: "+(end-start));
// System.out.println(testMat.cardinality()+" "+testMat.add(testMat2).cardinality()+"
// "+testMat3.cardinality());
// start = System.currentTimeMillis();
// SparseMatrix apspRes = new SparseMatrix(testMat.rowDim, testMat.colDim);
// int T = 1;testMat
// for (int iter = 0; iter < T; iter++) {
// apspRes = testMat.apsp();
// }
// end = System.currentTimeMillis();
// System.out.println("Time: " + (end - start));
// card = testMat.cardinality();
// seconds = (double) (end - start) / 1000.0;
// megaTEPs = card / seconds * dim / 1000000.0;
// System.out.println("megaTEPs: " + megaTEPs);
// System.out.println(testMat.toString());
// System.out.println(apspRes.toString());
// start = System.currentTimeMillis();
// Counter bfsRes = new Counter();
// int iterMax = dim;
// for(int iter = 0; iter < iterMax; iter++){
// int init = iter;
// bfsRes = testMat.bfs(init);
// // System.out.println(bfsRes.toString());
// }
// end = System.currentTimeMillis();
// System.out.println("Time: "+(end-start));
// card = testMat.cardinality();
// seconds = (double)(end-start)/1000.0;
// megaTEPs = card/seconds*iterMax/1000000.0;
// System.out.println("megaTEPs: "+megaTEPs);
// System.out.println(init);
// System.out.println(testMat.toString());
// System.out.println(bfsRes.toString());
// SparseMatrix testMatStoch = testMat.stochasticizeRows();
// double beta = 0.85;
// ArrayList<Double> vector = new ArrayList<Double>();
// for(int i = 0; i < dim; i++){
// vector.add(0.0);
// if(i == 0) vector.set(i,1.0);
// }
// ArrayList<Double> seedVector = new ArrayList<Double>(vector);
// double svCard = 0;
// for(int i = 0; i < dim; i++){
// svCard += seedVector.get(i);
// }
// ArrayList<Double> oldVector = new ArrayList<Double>(vector);
// vector = (ArrayList<Double>) testMatStoch.multiply(vector);
// double diff = 1;
// int t = 0;
// start = System.currentTimeMillis();
// while(diff > Math.pow(10.0, -10.0) && (diff < 3.99999 || diff > 4.00001) && t < 200){
// t += 1;
// vector = (ArrayList<Double>) testMatStoch.multiply(vector);
// double norm = 0;
// for(int i = 0; i < dim; i++){
//// norm += vector.get(i)*vector.get(i);
// norm += Math.abs(vector.get(i));
// }
//// norm = Math.sqrt(norm);
// for(int i = 0; i < dim; i++){r
//// vector.set(i,
// beta*vector.get(i)/norm+(1-beta)*seedVector.get(i));///Math.max(norm,1.0));
// vector.set(i, beta*vector.get(i)+(1-beta)*seedVector.get(i));///Math.max(norm,1.0));
// }
// diff = 0;
// for(int i = 0; i < dim; i++){
// diff +=(oldVector.get(i)-vector.get(i))*(oldVector.get(i)-vector.get(i));
// }
// System.out.println(diff+" "+norm);
//// System.out.println(vector.toString());
// // System.out.println(oldVector.toString());
// oldVector = new ArrayList<Double>(vector);
// }
//// System.out.println(testMatStoch.toStringValues());
// end = System.currentTimeMillis();
// System.out.println("Time: "+(end-start)+" iterations: "+t);
// Counter sv = new Counter();
// sv.set(rand.nextInt(dim),1.0);
// sv.set(0, 1.0);
// SparseMatrix.PersonalizedPageRank(sv, testMat);
// SparseMatrix laplacian = testMat.makeLaplacian();
// SparseMatrix normedLap = laplacian.stochasticizeRows();
// for(int i = 0; i < testMat.rowDim; i++){
// testMat.add(i,i,100.0);
// }
// SparseMatrix normedTest = testMat.stochasticizeRows();
// testMat = testMat.symmetric();
SparseMatrix orthogMat = new SparseMatrix(0, testMat.colDim);
Counter topEig = SparseMatrix.ConstrainedEig(testMat, orthogMat);
// Counter topEig = SparseMatrix.TopEig(testMat);
// for(int i = 0; i < orthogMat.colDim; i++){
// orthogMat.set(0, i, 1.0);
// }
int topK = 10;
for (int j = 0; j < topK; j++) {
orthogMat.addRow(j, new Counter(topEig));
// double norm = testMat.multiply(topEig).norm();
// System.out.println("norm: "+norm);
topEig = SparseMatrix.ConstrainedEig(testMat, orthogMat);
}
// orthogMat.addRow(topK, new Counter(topEig));
// for(int i: orthogMat.rows){
// Counter eig = orthogMat.getRow(i);
// for(int j: orthogMat.rows){
// Counter eigOther = orthogMat.getRow(j);
// double sim = eig.dot(eigOther);
//// int sim = (int)(eig.dot(eigOther)+0.9999);
// System.out.print(sim+" ");
// }
// System.out.println();
// }
// SparseMatrix tcMin = testMat.minimize().transitiveClosure();
// SparseMatrix tcMat = testMat.transitiveClosure();
// System.out.println(tcMin.cardinality()+" "+tcMat.cardinality());
// testMat.add(0,0,1.0);
// testMat.add(0,1,1.0);
// testMat.add(1,1,1.0);
// testMat.add(1,2,1.0);
// testMat.add(2,2,1.0);
// System.out.println(testMat.cardinality());
// for(int t = 0; t < 1000; t++){
// testMat = testMat.multiply(testMat);
// System.out.println(testMat.cardinality());
// }
// System.out.println(testMat.toString());
}