Examples with DenseVector org.apache.mahout.math.DenseVector used on opensource projects

Example 66 with DenseVector

use of org.apache.mahout.math.DenseVector in project pyramid by cheng-li.

the class IntervalSplitterTest method test7.

static void test7() {
    RegTreeConfig regTreeConfig = new RegTreeConfig().setNumSplitIntervals(4);
    Vector vector = new DenseVector(4);
    vector.set(0, 0);
    vector.set(1, 1);
    vector.set(2, 2);
    vector.set(3, 3);
    double[] probs = { 0, 0.5, 0.2, 0.6 };
    double[] labels = { 1, 2, 3, 4 };
    Splitter.GlobalStats globalStats = new Splitter.GlobalStats(labels, probs);
    List<Interval> intervals = IntervalSplitter.generateIntervals(regTreeConfig, vector, probs, labels, globalStats);
    System.out.println(intervals);
    System.out.println(IntervalSplitter.compress(intervals));
}

Example 67 with DenseVector

use of org.apache.mahout.math.DenseVector in project pyramid by cheng-li.

the class RegressionTreeTest method test1.

private static void test1() {
    Node a = new Node();
    a.setFeatureIndex(0);
    a.setThreshold(0.0);
    a.setLeftProb(0.3);
    a.setRightProb(0.7);
    Node b = new Node();
    b.setFeatureIndex(1);
    b.setThreshold(0.1);
    b.setLeftProb(0.8);
    b.setRightProb(0.2);
    Node c = new Node();
    c.setFeatureIndex(2);
    c.setThreshold(0.2);
    c.setLeftProb(0.1);
    c.setRightProb(0.9);
    Node d = new Node();
    d.setLeaf(true);
    d.setValue(1);
    Node e = new Node();
    e.setLeaf(true);
    e.setValue(2);
    Node f = new Node();
    f.setLeaf(true);
    f.setValue(3);
    Node g = new Node();
    g.setLeaf(true);
    g.setValue(4);
    a.setLeftChild(b);
    a.setRightChild(c);
    b.setLeftChild(d);
    b.setRightChild(e);
    c.setLeftChild(f);
    c.setRightChild(g);
    RegressionTree tree = new RegressionTree();
    tree.root = a;
    tree.leaves.add(d);
    tree.leaves.add(e);
    tree.leaves.add(f);
    tree.leaves.add(g);
    Vector vector1 = new DenseVector(3);
    vector1.set(0, Double.NaN);
    vector1.set(1, Double.NaN);
    vector1.set(2, Double.NaN);
    System.out.println(tree.probability(vector1, a));
    System.out.println(tree.probability(vector1, b));
    System.out.println(tree.probability(vector1, c));
    System.out.println(tree.probability(vector1, d));
    System.out.println(tree.probability(vector1, e));
    System.out.println(tree.probability(vector1, f));
    System.out.println(tree.probability(vector1, g));
    System.out.println(tree.predict(vector1));
    System.out.println(0.24 + 0.06 * 2 + 0.07 * 3 + 0.63 * 4);
}

Example 68 with DenseVector

use of org.apache.mahout.math.DenseVector in project pyramid by cheng-li.

the class RegressionTreeTest method test3.

private static void test3() {
    Node a = new Node();
    a.setFeatureIndex(0);
    a.setThreshold(0.0);
    a.setLeftProb(0.3);
    a.setRightProb(0.7);
    Node b = new Node();
    b.setFeatureIndex(1);
    b.setThreshold(0.1);
    b.setLeftProb(0.8);
    b.setRightProb(0.2);
    Node c = new Node();
    c.setFeatureIndex(2);
    c.setThreshold(0.2);
    c.setLeftProb(0.1);
    c.setRightProb(0.9);
    Node d = new Node();
    d.setLeaf(true);
    d.setValue(1);
    Node e = new Node();
    e.setLeaf(true);
    e.setValue(2);
    Node f = new Node();
    f.setLeaf(true);
    f.setValue(3);
    Node g = new Node();
    g.setLeaf(true);
    g.setValue(4);
    a.setLeftChild(b);
    a.setRightChild(c);
    b.setLeftChild(d);
    b.setRightChild(e);
    c.setLeftChild(f);
    c.setRightChild(g);
    RegressionTree tree = new RegressionTree();
    tree.root = a;
    tree.leaves.add(d);
    tree.leaves.add(e);
    tree.leaves.add(f);
    tree.leaves.add(g);
    Vector vector1 = new DenseVector(3);
    vector1.set(0, -1);
    vector1.set(1, 0.2);
    vector1.set(2, Double.NaN);
    System.out.println(tree.probability(vector1, a));
    System.out.println(tree.probability(vector1, b));
    System.out.println(tree.probability(vector1, c));
    System.out.println(tree.probability(vector1, d));
    System.out.println(tree.probability(vector1, e));
    System.out.println(tree.probability(vector1, f));
    System.out.println(tree.probability(vector1, g));
    System.out.println(tree.predict(vector1));
}

Example 69 with DenseVector

use of org.apache.mahout.math.DenseVector in project pyramid by cheng-li.

the class RegressionTreeTest method test4.

private static void test4() {
    Node a = new Node();
    a.setFeatureIndex(0);
    a.setThreshold(0.0);
    a.setLeftProb(0.3);
    a.setRightProb(0.7);
    Node b = new Node();
    b.setFeatureIndex(1);
    b.setThreshold(0.1);
    b.setLeftProb(0.8);
    b.setRightProb(0.2);
    Node c = new Node();
    c.setFeatureIndex(2);
    c.setThreshold(0.2);
    c.setLeftProb(0.1);
    c.setRightProb(0.9);
    Node d = new Node();
    d.setLeaf(true);
    d.setValue(1);
    Node e = new Node();
    e.setLeaf(true);
    e.setValue(2);
    Node f = new Node();
    f.setLeaf(true);
    f.setValue(3);
    Node g = new Node();
    g.setLeaf(true);
    g.setValue(4);
    a.setLeftChild(b);
    a.setRightChild(c);
    b.setLeftChild(d);
    b.setRightChild(e);
    c.setLeftChild(f);
    c.setRightChild(g);
    RegressionTree tree = new RegressionTree();
    tree.root = a;
    tree.leaves.add(d);
    tree.leaves.add(e);
    tree.leaves.add(f);
    tree.leaves.add(g);
    Vector vector1 = new DenseVector(3);
    vector1.set(0, 1);
    vector1.set(1, Double.NaN);
    vector1.set(2, Double.NaN);
    System.out.println(tree.probability(vector1, a));
    System.out.println(tree.probability(vector1, b));
    System.out.println(tree.probability(vector1, c));
    System.out.println(tree.probability(vector1, d));
    System.out.println(tree.probability(vector1, e));
    System.out.println(tree.probability(vector1, f));
    System.out.println(tree.probability(vector1, g));
    System.out.println(tree.predict(vector1));
}

Example 70 with DenseVector

use of org.apache.mahout.math.DenseVector in project pyramid by cheng-li.

the class FusedKolmogorovFilterTest method test2.

private static void test2() {
    Vector vector = new DenseVector(10);
    vector.set(0, 0.1);
    vector.set(1, 0.2);
    vector.set(2, 0.15);
    vector.set(3, 0.4);
    vector.set(4, 0.7);
    vector.set(8, 0.9);
    vector.set(9, 0.8);
    int[] labels = new int[10];
    labels[0] = 0;
    labels[1] = 1;
    labels[2] = 2;
    labels[3] = 1;
    labels[9] = 2;
    FusedKolmogorovFilter filter = new FusedKolmogorovFilter();
    filter.setNumBins(10);
    List<List<Double>> inputsEachClass = filter.generateInputsEachClass(vector, labels, 3);
    System.out.println(inputsEachClass);
    List<EmpiricalCDF> empiricalCDFs = filter.generateCDFs(vector, inputsEachClass);
    System.out.println(empiricalCDFs);
    System.out.println(filter.maxDistance(empiricalCDFs));
    System.out.println(EmpiricalCDF.distance(empiricalCDFs.get(0), empiricalCDFs.get(1)));
    System.out.println(EmpiricalCDF.distance(empiricalCDFs.get(0), empiricalCDFs.get(2)));
    System.out.println(EmpiricalCDF.distance(empiricalCDFs.get(1), empiricalCDFs.get(2)));
}