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Some additional output during Pareto curve generation. 

git-svn-id: https://www.prismmodelchecker.org/svn/prism/prism/trunk@10893 bbc10eb1-c90d-0410-af57-cb519fbb1720
master
Dave Parker 10 years ago
parent
b46a0c31be
commit
90a79ce2d0
1 changed files with 9 additions and 17 deletions
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  1. 26
      prism/src/prism/MultiObjModelChecker.java

26
prism/src/prism/MultiObjModelChecker.java
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@ -834,13 +834,13 @@ public class MultiObjModelChecker extends PrismComponent
PrismNative.setExportAdv(Prism.EXPORT_ADV_NONE);
for (int i = 0; i < dimProb; i++) {
mainLog.println("Computing maximum values for probability objective " + (i + 1) + "/" + dimProb);
double[] result = null;
// Optimise in direction of probability objective i
Point direction = new Point(dimProb + dimReward);
direction.setCoord(i, 1);
try {
mainLog.println("Optimising weighted sum for probability objective " + (i + 1) + "/" + dimProb + ": weights " + direction);
if (useGS) {
result = PrismSparse.NondetMultiObjGS(modelProduct.getODD(), modelProduct.getAllDDRowVars(), modelProduct.getAllDDColVars(),
modelProduct.getAllDDNondetVars(), false, st, adversary, trans_matrix, probDoubleVectors, rewSparseMatrices, direction.getCoords());
@ -857,7 +857,8 @@ public class MultiObjModelChecker extends PrismComponent
for (int j = 0; j < dimProb + dimReward; j++) {
direction.setCoord(j, j == i ? large : 1);
}
direction.normalize();
direction = direction.normalize();
mainLog.println("Optimising weighted sum for probability objective " + (i + 1) + "/" + dimProb + ": weights " + direction);
if (useGS) {
result = PrismSparse.NondetMultiObjGS(modelProduct.getODD(), modelProduct.getAllDDRowVars(), modelProduct.getAllDDColVars(),
modelProduct.getAllDDNondetVars(), false, st, adversary, trans_matrix, probDoubleVectors, rewSparseMatrices, direction.getCoords());
@ -879,16 +880,13 @@ public class MultiObjModelChecker extends PrismComponent
}
for (int i = 0; i < dimReward; i++) {
mainLog.println("Computing maximum values for reward objective " + (i + 1) + "/" + dimReward);
if (verbose) {
mainLog.println("Getting an upper bound on maximizing objective " + i);
}
double[] result = null;
// Optimise in direction of reward objective i
Point direction = new Point(dimProb + dimReward);
direction.setCoord(dimProb + i, 1);
try {
mainLog.println("Optimising weighted sum for reward objective " + (i + 1) + "/" + dimReward + ": weights " + direction);
if (useGS) {
result = PrismSparse.NondetMultiObjGS(modelProduct.getODD(), modelProduct.getAllDDRowVars(), modelProduct.getAllDDColVars(),
modelProduct.getAllDDNondetVars(), false, st, adversary, trans_matrix, probDoubleVectors, rewSparseMatrices, direction.getCoords());
@ -905,7 +903,8 @@ public class MultiObjModelChecker extends PrismComponent
for (int j = 0; j < dimProb + dimReward; j++) {
direction.setCoord(j, j == dimProb + i ? large : 1);
}
direction.normalize();
direction = direction.normalize();
mainLog.println("Optimising weighted sum for reward objective " + (i + 1) + "/" + dimReward + ": weights " + direction);
if (useGS) {
result = PrismSparse.NondetMultiObjGS(modelProduct.getODD(), modelProduct.getAllDDRowVars(), modelProduct.getAllDDColVars(),
modelProduct.getAllDDNondetVars(), false, st, adversary, trans_matrix, probDoubleVectors, rewSparseMatrices, direction.getCoords());
@ -946,28 +945,21 @@ public class MultiObjModelChecker extends PrismComponent
while (iters < maxIters) {
iters++;
//create the weights array
double[] weights = new double[dimProb + dimReward];
for (int i = 0; i < dimProb + dimReward; i++) {
weights[i] = direction.getCoord(i);
}
// If adversary generation is enabled, we amend the filename so that multiple adversaries can be exported
String advFileName = settings.getString(PrismSettings.PRISM_EXPORT_ADV_FILENAME);
if (settings.getChoice(PrismSettings.PRISM_EXPORT_ADV) != Prism.EXPORT_ADV_NONE) {
PrismNative.setExportAdvFilename(PrismUtils.addCounterSuffixToFilename(advFileName, iters));
}
mainLog.println("Computing weighted sum of objectives for weights " + Arrays.toString(weights));
mainLog.println("Optimising weighted sum of objectives: weights " + direction);
double[] result;
if (useGS) {
result = PrismSparse.NondetMultiObjGS(modelProduct.getODD(), modelProduct.getAllDDRowVars(), modelProduct.getAllDDColVars(),
modelProduct.getAllDDNondetVars(), false, st, adversary, trans_matrix, probDoubleVectors, rewSparseMatrices, weights);
modelProduct.getAllDDNondetVars(), false, st, adversary, trans_matrix, probDoubleVectors, rewSparseMatrices, direction.getCoords());
} else {
result = PrismSparse.NondetMultiObj(modelProduct.getODD(), modelProduct.getAllDDRowVars(), modelProduct.getAllDDColVars(),
modelProduct.getAllDDNondetVars(), false, st, adversary, trans_matrix, modelProduct.getSynchs(), probDoubleVectors, probStepBounds,
rewSparseMatrices, weights, rewardStepBounds);
rewSparseMatrices, direction.getCoords(), rewardStepBounds);
}
/* //Minimizing operators are negated, and for Pareto we need to maximize.

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