/*
* Matrix mult but with min-plus, and iterative. Each min-plus operation
* that changes the path inserts it into a new queue
*/
public SparseMatrix apsp() {
SparseMatrix shortestPaths = new SparseMatrix(this);
SparseMatrix currentPairs = new SparseMatrix(this.rowDim, this.colDim);
SparseMatrix newPairs = new SparseMatrix(this.rowDim, this.colDim);
newPairs = new SparseMatrix(this);
for (int d = 0; d < this.rowDim; d++) {
shortestPaths.set(d, d, 0.0);
}
for (int d = 0; d < this.rowDim; d++) {
newPairs.set(d, d, 0.0);
}
while (!newPairs.isEmpty()) {
currentPairs = new SparseMatrix(newPairs);
newPairs = new SparseMatrix(this.rowDim, this.colDim);
for (int r : currentPairs.rows) {
Counter row = currentPairs.getRow(r);
for (int c : row.keySet()) {
Counter oRow = this.getRow(c);
for (int oc : oRow.keySet()) {
double pathLength = currentPairs.get(r, c) + oRow.get(oc);
if (pathLength < shortestPaths.getPath(r, oc)) {
newPairs.set(r, oc, pathLength);
shortestPaths.set(r, oc, pathLength);
}
}
}
}
}
return shortestPaths;
}
public SparseMatrix makeLaplacian() {
SparseMatrix laplacian = new SparseMatrix(this.rowDim, this.colDim);
for (int r : this.getRows()) {
Counter row = this.getRow(r);
laplacian.set(r, r, row.sum());
for (int c : row.keySet()) {
laplacian.set(r, c, -1 * row.get(c));
}
}
return laplacian;
}
public SparseMatrix transpose() {
SparseMatrix transp = new SparseMatrix(this.rowDim, this.colDim);
for (int r : rows) {
Counter row = this.getRow(r);
for (int c : row.keySet()) {
double v = row.get(c);
transp.set(c, r, v);
}
}
return transp;
}
public SparseMatrix stochasticizeRows() {
SparseMatrix stochasticMat = new SparseMatrix(this.rowDim, this.colDim);
double[] rowSums = new double[this.rowDim];
for (int r : this.rows) {
Counter row = this.getRow(r);
for (int c : row.keySet()) {
rowSums[r] += row.get(c);
}
}
for (int r : this.rows) {
Counter row = this.getRow(r);
for (int c : row.keySet()) {
double value = 0;
if (rowSums[r] != 0) {
// if(true){
value = this.get(r, c) / rowSums[r];
}
stochasticMat.set(r, c, value);
}
}
return stochasticMat;
}
