/** Computes the next step of the Power Method. */
public void step() throws IOException {
double[] oldRank = rank, newRank = previousRank;
DoubleArrays.fill(newRank, 0.0);
// for each node, calculate its outdegree and redistribute its rank among pointed nodes
double accum = 0.0;
progressLogger.expectedUpdates = numNodes;
progressLogger.start("Iteration " + (++iterationNumber) + "...");
final ArcLabelledNodeIterator nodeIterator = g.nodeIterator();
int i, outdegree, j, n = numNodes;
int[] succ;
Label[] lab;
while (n-- != 0) {
i = nodeIterator.nextInt();
outdegree = nodeIterator.outdegree();
if (outdegree == 0 || buckets != null && buckets.get(i)) accum += oldRank[i];
else {
j = outdegree;
succ = nodeIterator.successorArray();
lab = nodeIterator.labelArray();
while (j-- != 0) {
newRank[succ[j]] += (oldRank[i] * lab[j].getFloat()) / sumoutweight[i];
}
}
progressLogger.update();
}
progressLogger.done();
final double accumOverNumNodes = accum / numNodes;
final double oneOverNumNodes = 1.0 / numNodes;
if (preference != null)
if (preferentialAdjustment == null)
for (i = numNodes; i-- != 0; )
newRank[i] =
alpha * newRank[i]
+ (1 - alpha) * preference.getDouble(i)
+ alpha * accumOverNumNodes;
else
for (i = numNodes; i-- != 0; )
newRank[i] =
alpha * newRank[i]
+ (1 - alpha) * preference.getDouble(i)
+ alpha * accum * preferentialAdjustment.getDouble(i);
else if (preferentialAdjustment == null)
for (i = numNodes; i-- != 0; )
newRank[i] = alpha * newRank[i] + (1 - alpha) * oneOverNumNodes + alpha * accumOverNumNodes;
else
for (i = numNodes; i-- != 0; )
newRank[i] =
alpha * newRank[i]
+ (1 - alpha) * oneOverNumNodes
+ alpha * accum * preferentialAdjustment.getDouble(i);
// make the rank just computed the new rank
rank = newRank;
previousRank = oldRank;
// Compute derivatives.
n = iterationNumber;
if (subset == null) {
for (i = 0; i < order.length; i++) {
final int k = order[i];
final double alphak = Math.pow(alpha, k);
final double nFallingK = Util.falling(n, k);
for (j = 0; j < numNodes; j++)
derivative[i][j] += nFallingK * (rank[j] - previousRank[j]) / alphak;
}
} else {
for (i = 0; i < order.length; i++) {
final int k = order[i];
final double alphak = Math.pow(alpha, k);
final double nFallingK = Util.falling(n, k);
for (int t : subset) derivative[i][t] += nFallingK * (rank[t] - previousRank[t]) / alphak;
}
}
// Compute coefficients, if required.
if (coeffBasename != null) {
final DataOutputStream coefficients =
new DataOutputStream(
new FastBufferedOutputStream(
new FileOutputStream(coeffBasename + "-" + (iterationNumber))));
final double alphaN = Math.pow(alpha, n);
for (i = 0; i < numNodes; i++) coefficients.writeDouble((rank[i] - previousRank[i]) / alphaN);
coefficients.close();
}
}
/**
* For a specific sub-set of blocks (child nodes), find a 'base' subset of parents for which the
* block's logLikelihood is not -Infinity
*
* @param candidateParentsPerNode
* @param chosenArcsPerNode
* @param setOfBlocks
* @return
*/
protected double getOutOfMinusInfinity(
Int2ObjectOpenHashMap<IntOpenHashSet> candidateParentsPerNode,
Int2ObjectOpenHashMap<ObjectOpenHashSet<Arc>> chosenArcsPerNode,
IntOpenHashSet setOfBlocks,
TIntDoubleHashMap logLPerNode) {
double totalLogL = 0;
ProgressLogger pl = new ProgressLogger(LOGGER, ProgressLogger.TEN_SECONDS, "blocks");
pl.start("Begin initializing, to avoid zero likelihood, using set-cover heuristic");
pl.expectedUpdates = setOfBlocks.size();
int nArcs = 0;
for (int v : setOfBlocks) {
pl.update();
IntOpenHashSet vParents = candidateParentsPerNode.get(v);
Int2ObjectOpenHashMap<IntOpenHashSet> parentActions =
new Int2ObjectOpenHashMap<IntOpenHashSet>();
Int2ObjectOpenHashMap<IntArrayList> cPlusV = auxiliary.getCplusOnline(v);
Int2ObjectOpenHashMap<IntArrayList> cMinusV = auxiliary.getCminusOnline(v);
if (cPlusV != null) {
IntSet actions = cPlusV.keySet();
// Heuristic: first add the parents that participate in A+ for
// most actions
for (int action : actions) {
for (int u : cPlusV.get(action)) {
if (!parentActions.containsKey(u)) {
parentActions.put(u, new IntOpenHashSet());
}
parentActions.get(u).add(action);
}
}
}
KeepMaximum km = new KeepMaximum();
km.addAllKey2Listsize(parentActions);
IntOpenHashSet baseSetOfParents = new IntOpenHashSet();
double logL = Double.NEGATIVE_INFINITY;
while (logL == Double.NEGATIVE_INFINITY && (km.getMaximumKey() != -1)) {
int u = km.getMaximumKey();
if (baseSetOfParents.contains(u)) {
throw new IllegalStateException("Attempted to add twice the same parent");
}
baseSetOfParents.add(u);
logL = blockLogLikelihood(v, cPlusV, cMinusV, baseSetOfParents);
IntOpenHashSet uActions = parentActions.get(u);
for (int parent : vParents) {
parentActions.get(parent).removeAll(uActions);
}
vParents.remove(u);
parentActions.remove(u);
km.reset();
km.addAllKey2Listsize(parentActions);
}
// keep track of the likelihood
totalLogL += logL;
if (logLPerNode != null) {
logLPerNode.put(v, logL);
}
chosenArcsPerNode.put(v, new ObjectOpenHashSet<Arc>());
for (int u : baseSetOfParents) {
nArcs++;
chosenArcsPerNode.get(v).add(new Arc(u, v));
}
}
pl.stop("Done initialization. Added " + nArcs + " arcs, logLikelihood=" + totalLogL);
return totalLogL;
}
