Examples with UnionFind chapter1.section5.UnionFind used on opensource projects

Example 6 with UnionFind

use of chapter1.section5.UnionFind in project algorithms-sedgewick-wayne by reneargento.

the class Exercise52_NegativeWeightsI method main.

// Parameters example: -1 1 6 0.5 9 5 100
// -1 1 6 0.5 16 40 100
public static void main(String[] args) {
    Exercise52_NegativeWeightsI negativeWeightsI = new Exercise52_NegativeWeightsI();
    List<Double> weightsToRescale = new ArrayList<>();
    weightsToRescale.add(0.9);
    weightsToRescale.add(0.99);
    weightsToRescale.add(0.35);
    weightsToRescale.add(-0.2);
    weightsToRescale.add(-0.3);
    double minWeightInScaleTest = -1;
    double maxWeightInScaleTest = 1;
    StdOut.print("Rescaled weights: ");
    List<Double> rescaledWeights = negativeWeightsI.rescaleWeights(weightsToRescale, minWeightInScaleTest, maxWeightInScaleTest);
    for (double rescaledWeight : rescaledWeights) {
        StdOut.printf("%.2f ", rescaledWeight);
    }
    StdOut.println("\nExpected: 0.86 1.00 0.01 -0.84 -1.00 ");
    double minWeightInScale = Double.parseDouble(args[0]);
    double maxWeightInScale = Double.parseDouble(args[1]);
    int verticesInEuclideanDigraph = Integer.parseInt(args[2]);
    double radius = Double.parseDouble(args[3]);
    int verticesInGridDigraph = Integer.parseInt(args[4]);
    int extraEdgesInGridDigraph = Integer.parseInt(args[5]);
    int numberOfDigraphs = Integer.parseInt(args[6]);
    if (minWeightInScale < -1 || minWeightInScale > 1) {
        throw new IllegalArgumentException("Min weight in scale must be between -1 and 1");
    }
    if (maxWeightInScale < -1 || maxWeightInScale > 1) {
        throw new IllegalArgumentException("Max weight in scale must be between -1 and 1");
    }
    if (minWeightInScale > maxWeightInScale) {
        throw new IllegalArgumentException("Min weight in scale cannot be higher than max weight in scale");
    }
    // 1- Random sparse edge-weighted digraphs
    RandomSparseEdgeWeightedDigraphsGenerator randomSparseEdgeWeightedDigraphsGenerator = negativeWeightsI.new RandomSparseEdgeWeightedDigraphsGenerator();
    List<EdgeWeightedDigraphInterface> randomSparseEdgeWeightedDigraphsUniform = randomSparseEdgeWeightedDigraphsGenerator.randomSparseEdgeWeightedDigraphsGenerator(numberOfDigraphs, true, minWeightInScale, maxWeightInScale);
    EdgeWeightedDigraphInterface firstRandomSparseEdgeWeightedDigraphUniform = randomSparseEdgeWeightedDigraphsUniform.get(0);
    StdOut.println("\nRandom sparse-edge-weighted-digraph with uniform weights distribution");
    StdOut.println(firstRandomSparseEdgeWeightedDigraphUniform);
    List<EdgeWeightedDigraphInterface> randomSparseEdgeWeightedDigraphsGaussian = randomSparseEdgeWeightedDigraphsGenerator.randomSparseEdgeWeightedDigraphsGenerator(numberOfDigraphs, false, minWeightInScale, maxWeightInScale);
    EdgeWeightedDigraphInterface firstRandomSparseEdgeWeightedDigraphGaussian = randomSparseEdgeWeightedDigraphsGaussian.get(0);
    StdOut.println("Random sparse-edge-weighted-digraph with gaussian weights distribution:");
    StdOut.println(firstRandomSparseEdgeWeightedDigraphGaussian);
    // 2- Random Euclidean edge-weighted digraphs
    StdOut.println("Random Euclidean edge-weighted digraph:");
    List<EdgeWeightedDigraphInterface> randomEuclideanEdgeWeightedDigraphs = negativeWeightsI.new RandomEuclideanEdgeWeightedDigraphsGenerator().generateRandomEuclideanEdgeWeightedDigraphs(numberOfDigraphs, verticesInEuclideanDigraph, radius, minWeightInScale, maxWeightInScale);
    EdgeWeightedDigraphInterface firstEuclideanEdgeWeightedDigraph = randomEuclideanEdgeWeightedDigraphs.get(0);
    StdOut.println(firstEuclideanEdgeWeightedDigraph);
    UnionFind unionFind = new UnionFind(verticesInEuclideanDigraph);
    for (int vertex = 0; vertex < verticesInEuclideanDigraph; vertex++) {
        for (DirectedEdge edge : firstEuclideanEdgeWeightedDigraph.adjacent(vertex)) {
            int neighbor = edge.to();
            unionFind.union(vertex, neighbor);
        }
    }
    // Check theory that if "radius" is larger than threshold value SQRT(ln(V) / (Math.PI * V)) then the graph is
    // almost certainly connected. Otherwise, it is almost certainly disconnected.
    double thresholdValue = Math.sqrt(Math.log(verticesInEuclideanDigraph) / (Math.PI * verticesInEuclideanDigraph));
    StdOut.println("Euclidean digraph expected to be connected (if it were an undirected graph): " + (radius > thresholdValue));
    StdOut.println("Euclidean digraph would be connected (if it were an undirected graph): " + (unionFind.count() == 1));
    // 3- Random grid edge-weighted digraphs
    StdOut.println("\nRandom grid edge-weighted digraph:");
    List<EdgeWeightedDigraphInterface> randomGridEdgeWeightedDigraphs = negativeWeightsI.new RandomGridEdgeWeightedDigraphsGenerator().generateRandomGridEdgeWeightedDigraphs(numberOfDigraphs, verticesInGridDigraph, extraEdgesInGridDigraph, minWeightInScale, maxWeightInScale);
    EdgeWeightedDigraphInterface firstRandomGridEdgeWeightedDigraph = randomGridEdgeWeightedDigraphs.get(0);
    StdOut.println(firstRandomGridEdgeWeightedDigraph);
}

Example 7 with UnionFind

use of chapter1.section5.UnionFind in project algorithms-sedgewick-wayne by reneargento.

the class Exercise53_NegativeWeightsII method main.

// Parameters example: 50 6 0.5 9 5 100
// 50 6 0.5 16 40 100
public static void main(String[] args) {
    Exercise53_NegativeWeightsII negativeWeightsII = new Exercise53_NegativeWeightsII();
    List<Double> weights = new ArrayList<>();
    weights.add(0.9);
    weights.add(0.99);
    weights.add(0.35);
    weights.add(0.2);
    int percentageToNegateTest = 50;
    StdOut.print("Updated weights: ");
    List<Double> updatedWeights = negativeWeightsII.negateRandomWeights(weights, percentageToNegateTest);
    for (double updatedWeight : updatedWeights) {
        StdOut.printf("%.2f ", updatedWeight);
    }
    StdOut.println("\nExpected: 2 out of 4 edges with negative weights");
    int percentageToNegate = Integer.parseInt(args[0]);
    int verticesInEuclideanDigraph = Integer.parseInt(args[1]);
    double radius = Double.parseDouble(args[2]);
    int verticesInGridDigraph = Integer.parseInt(args[3]);
    int extraEdgesInGridDigraph = Integer.parseInt(args[4]);
    int numberOfDigraphs = Integer.parseInt(args[5]);
    if (percentageToNegate < 0 || percentageToNegate > 100) {
        throw new IllegalArgumentException("Percentage to negate must be between 0 and 100");
    }
    // 1- Random sparse edge-weighted digraphs
    RandomSparseEdgeWeightedDigraphsGenerator randomSparseEdgeWeightedDigraphsGenerator = negativeWeightsII.new RandomSparseEdgeWeightedDigraphsGenerator();
    List<EdgeWeightedDigraphInterface> randomSparseEdgeWeightedDigraphsUniform = randomSparseEdgeWeightedDigraphsGenerator.randomSparseEdgeWeightedDigraphsGenerator(numberOfDigraphs, true, percentageToNegate);
    EdgeWeightedDigraphInterface firstRandomSparseEdgeWeightedDigraphUniform = randomSparseEdgeWeightedDigraphsUniform.get(0);
    StdOut.println("\nRandom sparse-edge-weighted-digraph with uniform weights distribution");
    StdOut.println(firstRandomSparseEdgeWeightedDigraphUniform);
    List<EdgeWeightedDigraphInterface> randomSparseEdgeWeightedDigraphsGaussian = randomSparseEdgeWeightedDigraphsGenerator.randomSparseEdgeWeightedDigraphsGenerator(numberOfDigraphs, false, percentageToNegate);
    EdgeWeightedDigraphInterface firstRandomSparseEdgeWeightedDigraphGaussian = randomSparseEdgeWeightedDigraphsGaussian.get(0);
    StdOut.println("Random sparse-edge-weighted-digraph with gaussian weights distribution:");
    StdOut.println(firstRandomSparseEdgeWeightedDigraphGaussian);
    // 2- Random Euclidean edge-weighted digraphs
    StdOut.println("Random Euclidean edge-weighted digraph:");
    List<EdgeWeightedDigraphInterface> randomEuclideanEdgeWeightedDigraphs = negativeWeightsII.new RandomEuclideanEdgeWeightedDigraphsGenerator().generateRandomEuclideanEdgeWeightedDigraphs(numberOfDigraphs, verticesInEuclideanDigraph, radius, percentageToNegate);
    EdgeWeightedDigraphInterface firstEuclideanEdgeWeightedDigraph = randomEuclideanEdgeWeightedDigraphs.get(0);
    StdOut.println(firstEuclideanEdgeWeightedDigraph);
    UnionFind unionFind = new UnionFind(verticesInEuclideanDigraph);
    for (int vertex = 0; vertex < verticesInEuclideanDigraph; vertex++) {
        for (DirectedEdge edge : firstEuclideanEdgeWeightedDigraph.adjacent(vertex)) {
            int neighbor = edge.to();
            unionFind.union(vertex, neighbor);
        }
    }
    // Check theory that if "radius" is larger than threshold value SQRT(ln(V) / (Math.PI * V)) then the graph is
    // almost certainly connected. Otherwise, it is almost certainly disconnected.
    double thresholdValue = Math.sqrt(Math.log(verticesInEuclideanDigraph) / (Math.PI * verticesInEuclideanDigraph));
    StdOut.println("Euclidean digraph expected to be connected (if it were an undirected graph): " + (radius > thresholdValue));
    StdOut.println("Euclidean digraph would be connected (if it were an undirected graph): " + (unionFind.count() == 1));
    // 3- Random grid edge-weighted digraphs
    StdOut.println("\nRandom grid edge-weighted digraph:");
    List<EdgeWeightedDigraphInterface> randomGridEdgeWeightedDigraphs = negativeWeightsII.new RandomGridEdgeWeightedDigraphsGenerator().generateRandomGridEdgeWeightedDigraphs(numberOfDigraphs, verticesInGridDigraph, extraEdgesInGridDigraph, percentageToNegate);
    EdgeWeightedDigraphInterface firstRandomGridEdgeWeightedDigraph = randomGridEdgeWeightedDigraphs.get(0);
    StdOut.println(firstRandomGridEdgeWeightedDigraph);
}

Example 8 with UnionFind

use of chapter1.section5.UnionFind in project algorithms-sedgewick-wayne by reneargento.

the class Exercise48_RandomEuclideanDigraphs method main.

// Parameters example: 6 0.5 100
public static void main(String[] args) {
    int vertices = Integer.parseInt(args[0]);
    double radius = Double.parseDouble(args[1]);
    int numberOfDigraphs = Integer.parseInt(args[2]);
    List<Exercise38_EuclideanDigraphs.EuclideanDigraph> randomEuclideanDigraphs = new Exercise48_RandomEuclideanDigraphs().generateRandomEuclideanDigraphs(numberOfDigraphs, vertices, radius);
    Exercise38_EuclideanDigraphs.EuclideanDigraph firstEuclideanDigraph = randomEuclideanDigraphs.get(0);
    firstEuclideanDigraph.show(-0.1, 1.1, -0.1, 1.1, 0.03, 0.01, 0.03);
    UnionFind unionFind = new UnionFind(vertices);
    for (int vertex = 0; vertex < vertices; vertex++) {
        for (int neighbor : firstEuclideanDigraph.adjacent(vertex)) {
            unionFind.union(vertex, neighbor);
        }
    }
    // Check theory that if "radius" is larger than threshold value SQRT(ln(V) / (Math.PI * V)) then the graph is
    // almost certainly connected. Otherwise, it is almost certainly disconnected.
    double thresholdValue = Math.sqrt(Math.log(vertices) / (Math.PI * vertices));
    StdOut.println("Expected to be connected (if it were an undirected graph): " + (radius > thresholdValue));
    StdOut.println("Would be connected (if it were an undirected graph): " + (unionFind.count() == 1));
}