Examples with AndersonDarlingTest edu.cmu.tetrad.data.AndersonDarlingTest used on opensource projects

Example 6 with AndersonDarlingTest

use of edu.cmu.tetrad.data.AndersonDarlingTest in project tetrad by cmu-phil.

the class Lofs method pValue.

private double pValue(Node node, List<Node> parents) {
    List<Double> _residuals = new ArrayList<>();
    Node _target = node;
    List<Node> _regressors = parents;
    Node target = getVariable(variables, _target.getName());
    List<Node> regressors = new ArrayList<>();
    for (Node _regressor : _regressors) {
        Node variable = getVariable(variables, _regressor.getName());
        regressors.add(variable);
    }
    DATASET: for (int m = 0; m < dataSets.size(); m++) {
        RegressionResult result = regressions.get(m).regress(target, regressors);
        TetradVector residualsSingleDataset = result.getResiduals();
        for (int h = 0; h < residualsSingleDataset.size(); h++) {
            if (Double.isNaN(residualsSingleDataset.get(h))) {
                continue DATASET;
            }
        }
        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);
            if (isMeanCenterResiduals()) {
                _residualsSingleDataset.set(i2, (_residualsSingleDataset.get(i2) - mean));
            }
        // _residualsSingleDataset.set(i2, (_residualsSingleDataset.get(i2)));
        }
        for (int k = 0; k < _residualsSingleDataset.size(); k++) {
            _residuals.add(_residualsSingleDataset.get(k));
        }
    }
    double[] _f = new double[_residuals.size()];
    for (int k = 0; k < _residuals.size(); k++) {
        _f[k] = _residuals.get(k);
    }
    return new AndersonDarlingTest(_f).getP();
}

Example 7 with AndersonDarlingTest

use of edu.cmu.tetrad.data.AndersonDarlingTest in project tetrad by cmu-phil.

the class LingamPattern2 method getScore.

// Return the average score.
private Score getScore(Graph dag, List<TetradMatrix> data, List<Node> variables) {
    // System.out.println("Scoring DAG: " + dag);
    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));
            }
            DoubleArrayList f = new DoubleArrayList(_residualsSingleDataset.elements());
            for (int k = 0; k < f.size(); k++) {
                f.set(k, Math.abs(f.get(k)));
            }
            double _mean = Descriptive.mean(f);
            double diff = _mean - Math.sqrt(2.0 / Math.PI);
            score += diff * diff;
        // score += andersonDarlingPASquareStar(target, dag.getParents(target));
        }
        for (int k = 0; k < _residuals.size(); k++) {
            residuals.set(k, j, _residuals.get(k));
        }
    }
    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 8 with AndersonDarlingTest

use of edu.cmu.tetrad.data.AndersonDarlingTest in project tetrad by cmu-phil.

the class LingamPattern2 method andersonDarlingPASquareStar.

private double andersonDarlingPASquareStar(Node node, List<Node> parents) {
    List<Double> _residuals = new ArrayList<>();
    Node _target = node;
    List<Node> _regressors = parents;
    Node target = getVariable(variables, _target.getName());
    List<Node> regressors = new ArrayList<>();
    for (Node _regressor : _regressors) {
        Node variable = getVariable(variables, _regressor.getName());
        regressors.add(variable);
    }
    DATASET: for (int m = 0; m < dataSets.size(); m++) {
        RegressionResult result = regressions.get(m).regress(target, regressors);
        TetradVector residualsSingleDataset = result.getResiduals();
        for (int h = 0; h < residualsSingleDataset.size(); h++) {
            if (Double.isNaN(residualsSingleDataset.get(h))) {
                continue DATASET;
            }
        }
        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);
            _residualsSingleDataset.set(i2, (_residualsSingleDataset.get(i2) - mean));
        // _residualsSingleDataset.set(i2, (_residualsSingleDataset.get(i2)));
        }
        for (int k = 0; k < _residualsSingleDataset.size(); k++) {
            _residuals.add(_residualsSingleDataset.get(k));
        }
    }
    double[] _f = new double[_residuals.size()];
    for (int k = 0; k < _residuals.size(); k++) {
        _f[k] = _residuals.get(k);
    }
    double p = new AndersonDarlingTest(_f).getASquaredStar();
    System.out.println("Anderson Darling p for " + node + " given " + parents + " = " + p);
    return p;
}

Example 9 with AndersonDarlingTest

use of edu.cmu.tetrad.data.AndersonDarlingTest 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 10 with AndersonDarlingTest

use of edu.cmu.tetrad.data.AndersonDarlingTest in project tetrad by cmu-phil.

the class TestAndersonDarling method test2.

@Test
public void test2() {
    RandomUtil.getInstance().setSeed(4838582394L);
    double[] x = rand1(100);
    double aa = new AndersonDarlingTest(x).getASquared();
    assertEquals(1.93, aa, 0.1);
}