Examples with DoubleArrayList cern.colt.list.DoubleArrayList used on opensource projects

Example 76 with DoubleArrayList

use of cern.colt.list.DoubleArrayList in project tetrad by cmu-phil.

the class LingamPattern2 method getScore2.

private Score getScore2(Graph dag, List<TetradMatrix> data, List<Node> variables) {
    // System.out.println("Scoring DAG: " + dag);
    List<Regression> regressions = new ArrayList<>();
    for (TetradMatrix _data : data) {
        regressions.add(new RegressionDataset(_data, variables));
    }
    int totalSampleSize = 0;
    for (TetradMatrix _data : data) {
        totalSampleSize += _data.rows();
    }
    int numCols = data.get(0).columns();
    List<Node> nodes = dag.getNodes();
    double score = 0.0;
    double[] pValues = new double[nodes.size()];
    TetradMatrix residuals = new TetradMatrix(totalSampleSize, numCols);
    for (int j = 0; j < nodes.size(); j++) {
        List<Double> _residuals = new ArrayList<>();
        Node _target = nodes.get(j);
        List<Node> _regressors = dag.getParents(_target);
        Node target = getVariable(variables, _target.getName());
        List<Node> regressors = new ArrayList<>();
        for (Node _regressor : _regressors) {
            Node variable = getVariable(variables, _regressor.getName());
            regressors.add(variable);
        }
        for (int m = 0; m < data.size(); m++) {
            RegressionResult result = regressions.get(m).regress(target, regressors);
            TetradVector residualsSingleDataset = result.getResiduals();
            DoubleArrayList _residualsSingleDataset = new DoubleArrayList(residualsSingleDataset.toArray());
            double mean = Descriptive.mean(_residualsSingleDataset);
            double std = Descriptive.standardDeviation(Descriptive.variance(_residualsSingleDataset.size(), Descriptive.sum(_residualsSingleDataset), Descriptive.sumOfSquares(_residualsSingleDataset)));
            for (int i2 = 0; i2 < _residualsSingleDataset.size(); i2++) {
                _residualsSingleDataset.set(i2, (_residualsSingleDataset.get(i2) - mean) / std);
            }
            for (int k = 0; k < _residualsSingleDataset.size(); k++) {
                _residuals.add(_residualsSingleDataset.get(k));
            }
        }
        for (int k = 0; k < _residuals.size(); k++) {
            residuals.set(k, j, _residuals.get(k));
        }
    }
    for (int i = 0; i < nodes.size(); i++) {
        DoubleArrayList f = new DoubleArrayList(residuals.getColumn(i).toArray());
        for (int j = 0; j < f.size(); j++) {
            f.set(j, Math.abs(f.get(j)));
        }
        double _mean = Descriptive.mean(f);
        double diff = _mean - Math.sqrt(2.0 / Math.PI);
        score += diff * diff;
    }
    for (int j = 0; j < residuals.columns(); j++) {
        double[] x = residuals.getColumn(j).toArray();
        double p = new AndersonDarlingTest(x).getP();
        pValues[j] = p;
    }
    return new Score(score, pValues);
}

Example 77 with DoubleArrayList

use of cern.colt.list.DoubleArrayList in project tetrad by cmu-phil.

the class SemEstimator method setMeans.

/**
 * Sets the means of variables in the SEM IM based on the given data set.
 */
private void setMeans(SemIm semIm, DataSet dataSet) {
    if (dataSet != null) {
        int numColumns = dataSet.getNumColumns();
        for (int j = 0; j < numColumns; j++) {
            double[] column = dataSet.getDoubleData().getColumn(j).toArray();
            DoubleArrayList list = new DoubleArrayList(column);
            double mean = Descriptive.mean(list);
            Node node = dataSet.getVariable(j);
            Node variableNode = semIm.getVariableNode(node.getName());
            semIm.setMean(variableNode, mean);
            double standardDeviation = Descriptive.standardDeviation(Descriptive.variance(list.size(), Descriptive.sum(list), Descriptive.sumOfSquares(list)));
            semIm.setMeanStandardDeviation(variableNode, standardDeviation);
        }
    } else if (getCovMatrix() != null) {
        List<Node> variables = getCovMatrix().getVariables();
        for (Node node : variables) {
            Node variableNode = semIm.getVariableNode(node.getName());
            semIm.setMean(variableNode, 0.0);
        }
    }
}

Example 78 with DoubleArrayList

use of cern.colt.list.DoubleArrayList in project tetrad by cmu-phil.

the class Whitener method scale.

private DoubleMatrix2D scale(DoubleMatrix2D x, boolean scale) {
    for (int j = 0; j < x.columns(); j++) {
        DoubleArrayList u = new DoubleArrayList(x.viewColumn(j).toArray());
        double mean = Descriptive.mean(u);
        for (int i = 0; i < x.rows(); i++) {
            x.set(i, j, x.get(i, j) - mean);
        }
        if (scale) {
            double rms = rms(x.viewColumn(j));
            for (int i = 0; i < x.rows(); i++) {
                x.set(i, j, x.get(i, j) / rms);
            }
        }
    }
    return x;
}

Example 79 with DoubleArrayList

use of cern.colt.list.DoubleArrayList in project tetrad by cmu-phil.

the class DataUtils method standardizeData.

public static DoubleArrayList standardizeData(DoubleArrayList data) {
    DoubleArrayList data2 = new DoubleArrayList(data.size());
    double sum = 0.0;
    for (int i = 0; i < data.size(); i++) {
        sum += data.get(i);
    }
    double mean = sum / data.size();
    for (int i = 0; i < data.size(); i++) {
        data2.add(data.get(i) - mean);
    }
    double norm = 0.0;
    for (int i = 0; i < data2.size(); i++) {
        double v = data2.get(i);
        norm += v * v;
    }
    norm = Math.sqrt(norm / (data2.size() - 1));
    for (int i = 0; i < data2.size(); i++) {
        data2.set(i, data2.get(i) / norm);
    }
    return data2;
}

Example 80 with DoubleArrayList

use of cern.colt.list.DoubleArrayList in project tetrad by cmu-phil.

the class TestSemIm method testCholesky.

/**
 * The Cholesky decomposition of a symmetric, positive definite matrix
 * multiplied by the transpose of the Cholesky decomposition should be equal
 * to the original matrix itself.
 */
@Test
public void testCholesky() {
    Graph graph = constructGraph2();
    SemPm semPm = new SemPm(graph);
    SemIm semIm = new SemIm(semPm);
    DataSet dataSet = semIm.simulateData(500, false);
    TetradMatrix data = dataSet.getDoubleData();
    double[][] a = new double[data.columns()][data.columns()];
    for (int i = 0; i < data.columns(); i++) {
        for (int j = 0; j < data.columns(); j++) {
            DoubleArrayList icol = new DoubleArrayList(data.getColumn(i).toArray());
            DoubleArrayList jcol = new DoubleArrayList(data.getColumn(j).toArray());
            a[i][j] = Descriptive.covariance(icol, jcol);
        }
    }
    double[][] l = MatrixUtils.cholesky(a);
    double[][] lT = MatrixUtils.transpose(l);
    double[][] product = MatrixUtils.product(l, lT);
    assertTrue(MatrixUtils.equals(a, product, 1.e-10));
}