Java Code Examples for it.unimi.dsi.Util#identity()

/** Creates a new instance using a specific initial permutation and specified number of threads.
 *
 * <p>If <code>exact</code> is true, the final permutation is
 * <em>exactly</em> the same as if you first permute the graph with <code>startPerm</code> and
 * then apply LLP with an {@code null} starting permutation.
 *
 * @param symGraph a symmetric, loopless graph.
 * @param startPerm an initial permutation of the graph, or {@code null} for no permutation.
 * @param numberOfThreads the number of threads to be used (0 for automatic sizing).
 * @param seed a random seed.
 * @param exact a boolean flag that forces the algorithm to run exactly.
 */
public LayeredLabelPropagation(final ImmutableGraph symGraph, final int[] startPerm, final int numberOfThreads, final long seed, final boolean exact) throws IOException {
	this.symGraph = symGraph;
	this.n = symGraph.numNodes();
	this.startPerm = startPerm;
	this.seed = seed;
	this.r = new XoRoShiRo128PlusRandom(seed);
	this.exact = exact;
	this.label = new AtomicIntegerArray(n);
	this.volume = new AtomicIntegerArray(n);
	cumulativeOutdegrees = new EliasFanoCumulativeOutdegreeList(symGraph, symGraph.numArcs(), 1);

	this.gapCost = new MutableDouble();
	this.updateList = Util.identity(n);
	simpleUncaughtExceptionHandler = new SimpleUncaughtExceptionHandler();
	labelling = File.createTempFile(this.getClass().getName(), "labelling");
	labelling.deleteOnExit();

	this.numberOfThreads = numberOfThreads != 0 ? numberOfThreads : Runtime.getRuntime().availableProcessors();
	this.canChange = new boolean[n];
	this.modified = new AtomicInteger(0);
	this.objectiveFunction = new double[this.numberOfThreads];
}
 

@Test
public void testBlocking() throws InterruptedException {
	for(final int size: new int[] { 10, 100, 128, 256 }) {
		for(final int d: new int[] { 1, 2, 3, 4 }) {
			final ReorderingBlockingQueue<Integer> q = new ReorderingBlockingQueue<>(size / d);
			final int[] perm = Util.identity(size);
			IntArrays.shuffle(perm, new XoRoShiRo128PlusRandom());
			for(int i = perm.length; i-- != 0;) {
				final int t = perm[i];
				new Thread() {
					@Override
					public void run() {
						try {
							q.put(Integer.valueOf(t), t);
						}
						catch (final InterruptedException e) {
							throw new RuntimeException(e.getMessage(), e);
						}
					}
				}.start();
			}
			for(int i = 0; i < perm.length; i++) assertEquals(i, q.take().intValue());
			assertEquals(0, q.size());
		}
	}
}
 

private void update(final double gamma) {
	final int n = this.n;
	final int[] updateList = this.updateList;
	modified.set(0);
	nextArcs = nextNode = 0;

	if (exact) {
		if (startPerm == null) Util.identity(updateList);
		else Util.invertPermutation(startPerm, updateList);
	}

	// Local shuffle
	for(int i = 0; i < n;) IntArrays.shuffle(updateList, i, Math.min(i += SHUFFLE_GRANULARITY, n), r);

	final ProgressLogger pl = new ProgressLogger(LOGGER);
	pl.expectedUpdates = n;
	pl.logInterval = ProgressLogger.TEN_SECONDS;
	pl.itemsName = "nodes";
	pl.start("Starting update " + update + "...");

	final Thread[] thread = new Thread[numberOfThreads];

	nextArcs = nextNode =  0;
	for (int i = 0; i < numberOfThreads; i++) {
		thread[i] = new IterationThread(symGraph.copy(), gamma, i, pl);
		thread[i].setUncaughtExceptionHandler(simpleUncaughtExceptionHandler);
		thread[i].start();
	}

	for (int i = 0; i < numberOfThreads; i++)
		try {
			thread[i].join();
		}
		catch (final InterruptedException e) {
			throw new RuntimeException(e);
		}

	if (threadException != null) throw new RuntimeException(threadException);
	pl.done();
}
 

@Test
public void testNoBlocking() throws InterruptedException {
	for(final int size: new int[] { 1, 10, 100, 128, 256 }) {
		final ReorderingBlockingQueue<Integer> q = new ReorderingBlockingQueue<>(size);
		final int[] perm = Util.identity(size);
		IntArrays.shuffle(perm, new XoRoShiRo128PlusRandom());
		for(int i = perm.length; i-- != 0;) q.put(Integer.valueOf(perm[i]), perm[i]);
		for(int i = 0; i < perm.length; i++) assertEquals(i, q.take().intValue());
		assertEquals(0, q.size());
	}
}
 

/** Generates <code>np</code> call graphs. Each call graph is obtained using {@link #preferentialAttachmentDAG(int, int, IntegerDistribution, RandomGenerator)} (with
 *  specified initial graph size (<code>initialGraphSizeDistribution</code>), graph size (<code>graphSizeDistribution</code>), outdegree distribution (<code>outdegreeDistribution</code>).
 *  Then a dependency DAG is generated between the call graphs, once more using {@link #preferentialAttachmentDAG(int, int, IntegerDistribution, RandomGenerator)} (this
 *  time the initial graph size is 1, whereas the outdegree distribution is <code>outdegreeDistribution</code>).
 *  Then to each node of each call graph a new set of outgoing arcs is generated (their number is chosen using <code>externalOutdegreeDistribution</code>): the target
 *  call graph is generated using the indegree distribution of the dependency DAG; the target node is chosen according to the reverse indegree distribution within the revision call graph.
 *
 * @param np number of revision call graphs to be generated.
 * @param graphSizeDistribution the distribution of the graph sizes (number of functions per call graph).
 * @param initialGraphSizeDistribution the distribution of the initial graph sizes (the initial independent set from which the preferential attachment starts).
 * @param outdegreeDistribution the distribution of internal outdegrees (number of internal calls per function).
 * @param externalOutdegreeDistribution the distribution of external outdegrees (number of external calls per function).
 * @param depExponent exponent of the Zipf distribution used to establish the dependencies between call graphs.
 * @param random the random object used for the generation.
 */
public void generate(final int np, final IntegerDistribution graphSizeDistribution, final IntegerDistribution initialGraphSizeDistribution,
		final IntegerDistribution outdegreeDistribution, final IntegerDistribution externalOutdegreeDistribution, final IntegerDistribution dependencyOutdegreeDistribution, final RandomGenerator random) {
	rcgs = new ArrayListMutableGraph[np];
	nodePermutation = new int[np][];
	final FenwickTree[] td = new FenwickTree[np];
	deps = new IntOpenHashSet[np];
	source2Targets = new ObjectOpenCustomHashSet[np];

	// Generate rcg of the np revisions, and the corresponding reverse preferential distribution; cumsize[i] is the sum of all nodes in packages <i
	for ( int i = 0; i < np; i++) {
		deps[i] = new IntOpenHashSet();
		final int n = graphSizeDistribution.sample();
		final int n0 = Math.min(initialGraphSizeDistribution.sample(), n);
		rcgs[i] = preferentialAttachmentDAG(n, n0, outdegreeDistribution, random);
		td[i] = getPreferentialDistribution(rcgs[i].immutableView(), true);
		nodePermutation[i] = Util.identity(n);
		Collections.shuffle(IntArrayList.wrap(nodePermutation[i]), new Random(random.nextLong()));
	}

	// Generate the dependency DAG between revisions using preferential attachment starting from 1 node
	final ArrayListMutableGraph depDAG = preferentialAttachmentDAG(np, 1, dependencyOutdegreeDistribution, random);

	// For each source package, generate function calls so to cover all dependencies
	for (int sourcePackage = 0; sourcePackage < np; sourcePackage++) {
		source2Targets[sourcePackage] = new ObjectOpenCustomHashSet<>(IntArrays.HASH_STRATEGY);
		final int outdegree = depDAG.outdegree(sourcePackage);
		if (outdegree == 0) continue; // No calls needed (I'm kinda busy)

		final int numFuncs = rcgs[sourcePackage].numNodes();
		final int[] externalArcs = new int[numFuncs];
		int allExternalArcs = 0;
		// We decide how many calls to dispatch from each function
		for (int sourceNode = 0; sourceNode < numFuncs; sourceNode++) allExternalArcs += (externalArcs[sourceNode] = externalOutdegreeDistribution.sample());
		// We create a global list of external successors by shuffling
		final int[] targetPackage = new int[allExternalArcs];
		final int[] succ = depDAG.successorArray(sourcePackage);
		for(int i = 0; i < outdegree; i++) deps[sourcePackage].add(succ[i]);
		for(int i = 0; i < allExternalArcs; i++) targetPackage[i] = succ[i % outdegree];
		MathArrays.shuffle(targetPackage, random);

		for (int sourceNode = allExternalArcs = 0; sourceNode < numFuncs; sourceNode++) {
			final int externalOutdegree = externalArcs[sourceNode];
			for (int t = 0; t < externalOutdegree; t++) {
				final int targetNode = td[targetPackage[allExternalArcs + t]].sample(random) - 1;
				source2Targets[sourcePackage].add(new int[] { sourceNode, targetPackage[allExternalArcs + t], targetNode });
			}
			allExternalArcs += externalOutdegree;
		}
	}
}