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

Example 11 with DenseVector

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

the class RegressionTreeTest method test2.

private static void test2() {
    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 12 with DenseVector

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

the class RegressionTreeTest method test5.

private static void test5() {
    RegressionTree tree = RegressionTree.newStump(10, 0.5, -1.2, 3);
    System.out.println(tree);
    Vector vector = new DenseVector(100);
    vector.set(10, 0.6);
    System.out.println(tree.predict(vector));
}

Example 13 with DenseVector

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

the class IntervalSplitterTest method test12.

static void test12() {
    RegTreeConfig regTreeConfig = new RegTreeConfig().setNumSplitIntervals(2);
    Vector vector = new DenseVector(4);
    vector.set(0, Double.NaN);
    vector.set(1, Double.NaN);
    vector.set(2, Double.NaN);
    vector.set(3, 3);
    double[] probs = { 1, 0.5, 1, 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 14 with DenseVector

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

the class IntervalSplitterTest method test9.

static void test9() {
    RegTreeConfig regTreeConfig = new RegTreeConfig().setNumSplitIntervals(2);
    Vector vector = new DenseVector(4);
    vector.set(0, 0);
    vector.set(1, 1);
    vector.set(2, Double.NaN);
    vector.set(3, 3);
    double[] probs = { 1, 0.5, 1, 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));
    System.out.println(1.5 / (1.5 + 0.6));
    System.out.println(1 + 1 + 3 * 1.5 / (1.5 + 0.6));
}

Example 15 with DenseVector

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

the class IntervalSplitterTest method test8.

static void test8() {
    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 = { 1, 0.5, 0, 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));
}