Examples with TetradMatrix edu.cmu.tetrad.util.TetradMatrix used on opensource projects

Example 26 with TetradMatrix

use of edu.cmu.tetrad.util.TetradMatrix in project tetrad by cmu-phil.

the class FactorAnalysisJoe method successiveResidualHelper.

/*
     * Helper method for the basic structure successive factor method above.
     * Takes a residual matrix and a approximation vector, and finds both
     * the factor loading vector and the "d value" which is used to determine
     * the amount of total variance accounted for so far.
     */
private void successiveResidualHelper(TetradMatrix residual, TetradMatrix approximationVector) {
    TetradMatrix uVector = matrixMult(residual, approximationVector);
    TetradMatrix lVector = matrixMult(transpose(approximationVector), uVector);
    double dScalar = Math.sqrt(lVector.get(0, 0));
    TetradMatrix aVector = matrixDiv(dScalar, uVector);
    Vector aVectors = new Vector();
    Vector uVectors = new Vector();
    Vector dScalars = new Vector();
    aVectors.add(aVector);
    uVectors.add(uVector);
    dScalars.add(dScalar);
    for (int i = 0; i < 100; i++) {
        approximationVector = (TetradMatrix) aVectors.lastElement();
        uVector = matrixMult(residual, approximationVector);
        lVector = matrixMult(transpose(approximationVector), uVector);
        dScalar = Math.sqrt(lVector.get(0, 0));
        aVector = matrixDiv(dScalar, uVector);
        aVectors.add(aVector);
        uVectors.add(uVector);
        dScalars.add(dScalar);
        // 
        // TetradMatrix oldFactorLoading = (TetradMatrix)factorLoadings.lastElement();
        // //            TetradMatrix newUVector = matrixMult((TetradMatrix)correlationMatrix.getMatrix(),
        // //                                                        (TetradMatrix)factorLoadings.lastElement());
        // TetradMatrix newUVector = matrixMult((TetradMatrix)uVectors.lastElement(),
        // (TetradMatrix)factorLoadings.lastElement());
        // uVectors.add(newUVector);
        // TetradMatrix newLScalar = matrixMult(transpose(oldFactorLoading), newUVector);
        // if (lVeco.get(0, 0) < 0) throw new IllegalArgumentException();
        // double newDScalar = Math.sqrt(lVector.get(0, 0));
        // dScalars.add(newDScalar);
        // TetradMatrix _aVector = matrixDiv(newDScalar, uVector);
        // aVectors.add(_aVector);
        System.out.println("New D Scalar: " + dScalar);
        if (Math.sqrt((dScalar / (Double) dScalars.get(dScalars.size() - 2)) - 1) < .00001) {
            System.out.println("Stopped on the " + i + "th iteration.");
            break;
        }
    }
    System.out.println("Resultant factor loading matrix: ");
    System.out.println(aVectors.lastElement());
    this.dValues.add((Double) dScalars.lastElement());
    this.factorLoadingVectors.add((TetradMatrix) aVectors.lastElement());
}

Example 27 with TetradMatrix

use of edu.cmu.tetrad.util.TetradMatrix in project tetrad by cmu-phil.

the class FactorAnalysisJoe method matrixMult.

/*
     * Calculates (a * b)
     */
private static TetradMatrix matrixMult(TetradMatrix a, TetradMatrix b) {
    if (a.columns() != b.rows()) {
        throw new IllegalArgumentException();
    }
    TetradMatrix result = new TetradMatrix(a.rows(), b.columns());
    for (int i = 0; i < a.rows(); i++) {
        for (int j = 0; j < b.columns(); j++) {
            double value = 0;
            for (int k = 0; k < b.rows(); k++) {
                value += a.get(i, k) * b.get(k, j);
            }
            result.set(i, j, value);
        }
    }
    // System.out.println(result);
    return result;
}

Example 28 with TetradMatrix

use of edu.cmu.tetrad.util.TetradMatrix in project tetrad by cmu-phil.

the class FactorAnalysisJoe method largestNonDiagonalMagnitude.

/*
     * Largest nondiagonal magnitude method.
     *
     * Estimates the communality (diagonal of the residual correlation
     * matrix) as the largest nondiagonal absolute value present in each column.
     *
     * Tends to produce smaller numbers of factors than the unity method.
     */
public void largestNonDiagonalMagnitude(CorrelationMatrix r) {
    TetradMatrix residual = r.getMatrix();
    // System.out.println(residual.toString());
    for (int i = 0; i < residual.columns(); i++) {
        double max = 0;
        for (int j = 0; j < residual.columns(); j++) {
            if (i == j)
                continue;
            double temp = Math.abs(residual.get(j, i));
            if (temp > max)
                max = temp;
        }
        residual.set(i, i, max);
    }
// System.out.println(residual.toString());
}

Example 29 with TetradMatrix

use of edu.cmu.tetrad.util.TetradMatrix in project tetrad by cmu-phil.

the class SemOptimizerRicf method optimize.

// ==============================PUBLIC METHODS========================//
/**
 * Optimizes the fitting function of the given Sem using the Powell method
 * from Numerical Recipes by adjusting the freeParameters of the Sem.
 */
public void optimize(SemIm semIm) {
    if (numRestarts < 1)
        numRestarts = 1;
    if (numRestarts != 1) {
        throw new IllegalArgumentException("Number of restarts must be 1 for this method.");
    }
    TetradMatrix sampleCovar = semIm.getSampleCovar();
    if (sampleCovar == null) {
        throw new NullPointerException("Sample covar has not been set.");
    }
    if (DataUtils.containsMissingValue(sampleCovar)) {
        throw new IllegalArgumentException("Please remove or impute missing values.");
    }
    if (DataUtils.containsMissingValue(sampleCovar)) {
        throw new IllegalArgumentException("Please remove or impute missing values.");
    }
    TetradLogger.getInstance().log("info", "Trying EM...");
    // new SemOptimizerEm().optimize(semIm);
    CovarianceMatrix cov = new CovarianceMatrix(semIm.getMeasuredNodes(), sampleCovar, semIm.getSampleSize());
    SemGraph graph = semIm.getSemPm().getGraph();
    Ricf.RicfResult result = new Ricf().ricf(graph, cov, 0.001);
    // Ricf.RicfResult result = null;
    // 
    // for (int t = 0; t < 10; t++) {
    // Graph graph = semIm.getSemPm().getGraph();
    // result = new Ricf().ricf(graph, cov, 0.001);
    // 
    // TetradMatrix bHat = result.getBhat();
    // TetradMatrix lHat = result.getLhat();
    // TetradMatrix oHat = result.getOhat();
    // TetradMatrix sHat = result.getShat();
    // 
    // for (Parameter param : semIm.getFreeParameters()) {
    // if (param.getType() == ParamType.COEF) {
    // int i = semIm.getSemPm().getVariableNodes().indexOf(param.getNodeA());
    // int j = semIm.getSemPm().getVariableNodes().indexOf(param.getNodeB());
    // semIm.setEdgeCoef(param.getNodeA(), param.getNodeB(), -bHat.get(j, i));
    // }
    // 
    // if (param.getType() == ParamType.VAR) {
    // int i = semIm.getSemPm().getVariableNodes().indexOf(param.getNodeA());
    // if (lHat.get(i, i) != 0) {
    // semIm.setErrVar(param.getNodeA(), lHat.get(i, i));
    // } else if (oHat.get(i, i) != 0) {
    // semIm.setErrVar(param.getNodeA(), oHat.get(i, i));
    // }
    // }
    // }
    // 
    // if (t < 9) {
    // for (Parameter param : semIm.getFreeParameters()) {
    // double value = semIm.getParamValue(param);
    // double max = Double.NEGATIVE_INFINITY;
    // double d;
    // 
    // for (d = value - .5; d <= value + 0.5; d += 0.001) {
    // semIm.setParamValue(param, d);
    // double fml = semIm.getFml();
    // if (fml > max) max = fml;
    // }
    // 
    // semIm.setParamValue(param, d);
    // }
    // }
    // }
    TetradMatrix bHat = new TetradMatrix(result.getBhat().toArray());
    TetradMatrix lHat = new TetradMatrix(result.getLhat().toArray());
    TetradMatrix oHat = new TetradMatrix(result.getOhat().toArray());
    for (Parameter param : semIm.getFreeParameters()) {
        if (param.getType() == ParamType.COEF) {
            int i = semIm.getSemPm().getVariableNodes().indexOf(param.getNodeA());
            int j = semIm.getSemPm().getVariableNodes().indexOf(param.getNodeB());
            semIm.setEdgeCoef(param.getNodeA(), param.getNodeB(), -bHat.get(j, i));
        }
        if (param.getType() == ParamType.VAR) {
            int i = semIm.getSemPm().getVariableNodes().indexOf(param.getNodeA());
            if (lHat.get(i, i) != 0) {
                semIm.setErrVar(param.getNodeA(), lHat.get(i, i));
            } else if (oHat.get(i, i) != 0) {
                semIm.setErrVar(param.getNodeA(), oHat.get(i, i));
            }
        }
        if (param.getType() == ParamType.COVAR) {
            int i = semIm.getSemPm().getVariableNodes().indexOf(param.getNodeA());
            int j = semIm.getSemPm().getVariableNodes().indexOf(param.getNodeB());
            if (lHat.get(i, i) != 0) {
                semIm.setErrCovar(param.getNodeA(), param.getNodeB(), lHat.get(j, i));
            } else if (oHat.get(i, i) != 0) {
                semIm.setErrCovar(param.getNodeA(), param.getNodeB(), oHat.get(j, i));
            }
        }
    }
    System.out.println(result);
    System.out.println(semIm);
}

Example 30 with TetradMatrix

use of edu.cmu.tetrad.util.TetradMatrix in project tetrad by cmu-phil.

the class CovMatrixDifferenceWrapper method calcDifference.

// public CovMatrixDifferenceWrapper(SemEstimatorWrapper wrapper1, DataWrapper wrapper2, Parameters params) {
// if (wrapper1 == null || wrapper2 == null) {
// throw new NullPointerException("The data must not be null");
// }
// 
// DataModel model2 = wrapper2.getSelectedDataModel();
// 
// if (!(model2 instanceof ICovarianceMatrix)) {
// throw new IllegalArgumentException("Expecting corrariance matrices.");
// }
// 
// TetradMatrix corr1 = wrapper1.getEstimatedSemIm().getImplCovarMeas();
// TetradMatrix corr2 = ((ICovarianceMatrix) model2).getMatrix();
// 
// TetradMatrix corr3 = calcDifference(corr1, corr2);
// 
// ICovarianceMatrix corrWrapper = new CovarianceMatrix(model2.getVariable(), corr3,
// ((ICovarianceMatrix) model2).getSampleSize());
// 
// setDataModel(corrWrapper);
// setSourceGraph(wrapper2.getSourceGraph());
// LogDataUtils.logDataModelList("Difference of matrices.", getDataModelList());
// 
// }
// public CovMatrixDifferenceWrapper(SemImWrapper wrapper1, DataWrapper wrapper2, Parameters params) {
// try {
// if (wrapper1 == null || wrapper2 == null) {
// throw new NullPointerException("The data must not be null");
// }
// 
// DataModel model2 = wrapper2.getSelectedDataModel();
// 
// if (!(model2 instanceof ICovarianceMatrix)) {
// throw new IllegalArgumentException("Expecting corrariance matrices.");
// }
// 
// TetradMatrix corr1 = wrapper1.getSemIm().getImplCovarMeas();
// TetradMatrix corr2 = ((ICovarianceMatrix) model2).getMatrix();
// 
// TetradMatrix corr3 = calcDifference(corr1, corr2);
// 
// ICovarianceMatrix corrWrapper = new CovarianceMatrix(model2.getVariable(), corr3,
// ((ICovarianceMatrix) model2).getSampleSize());
// 
// setDataModel(corrWrapper);
// setSourceGraph(wrapper2.getSourceGraph());
// LogDataUtils.logDataModelList("Difference of matrices.", getDataModelList());
// } catch (Exception e) {
// e.printStackTrace();
// throw new RuntimeException(e);
// }
// 
// }
private TetradMatrix calcDifference(TetradMatrix corr1, TetradMatrix corr2) {
    if (corr1.rows() != corr2.rows()) {
        throw new IllegalArgumentException("Covariance matrices must be the same size.");
    }
    TetradMatrix corr3 = new TetradMatrix(corr2.rows(), corr2.rows());
    for (int i = 0; i < corr3.rows(); i++) {
        for (int j = 0; j < corr3.rows(); j++) {
            double v = corr1.get(i, j) - corr2.get(i, j);
            corr3.set(i, j, v);
        // corr3.set(j, i, v);
        }
    }
    for (int i = 0; i < corr3.rows(); i++) {
        corr3.set(i, i, Math.abs(corr3.get(i, i)));
    }
    return corr3;
}