Package org.apache.commons.math3.linear

Examples of org.apache.commons.math3.linear.Array2DRowRealMatrix.transpose()

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            for (int i = 0; i < n; i++) {
                for (int j = 0; j < k; j++) {
                    final RealMatrix vec
                        = new Array2DRowRealMatrix(MathArrays.ebeSubtract(data[i], newMeans[j]));
                    final RealMatrix dataCov
                        = vec.multiply(vec.transpose()).scalarMultiply(gamma[i][j]);
                    newCovMats[j] = newCovMats[j].add(dataCov);
                }
            }

            // Converting to arrays for use by fitted model
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        for (int v = 0; v < dim; v++) {
            final double[] evec = covMatDec.getEigenvector(v).toArray();
            covMatEigenvectors.setColumn(v, evec);
        }

        final RealMatrix tmpMatrix = covMatEigenvectors.transpose();

        // Scale each eigenvector by the square root of its eigenvalue.
        for (int row = 0; row < dim; row++) {
            final double factor = FastMath.sqrt(covMatEigenvalues[row]);
            for (int col = 0; col < dim; col++) {
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            for (int i = 0; i < n; i++) {
                for (int j = 0; j < k; j++) {
                    final RealMatrix vec
                        = new Array2DRowRealMatrix(MathArrays.ebeSubtract(data[i], newMeans[j]));
                    final RealMatrix dataCov
                        = vec.multiply(vec.transpose()).scalarMultiply(gamma[i][j]);
                    newCovMats[j] = newCovMats[j].add(dataCov);
                }
            }

            // Converting to arrays for use by fitted model
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        for (int v = 0; v < dim; v++) {
            final double[] evec = covMatDec.getEigenvector(v).toArray();
            covMatEigenvectors.setColumn(v, evec);
        }

        final RealMatrix tmpMatrix = covMatEigenvectors.transpose();

        // Scale each eigenvector by the square root of its eigenvalue.
        for (int row = 0; row < dim; row++) {
            final double factor = FastMath.sqrt(covMatEigenvalues[row]);
            for (int col = 0; col < dim; col++) {
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            for (int i = 0; i < n; i++) {
                for (int j = 0; j < k; j++) {
                    final RealMatrix vec
                        = new Array2DRowRealMatrix(MathArrays.ebeSubtract(data[i], newMeans[j]));
                    final RealMatrix dataCov
                        = vec.multiply(vec.transpose()).scalarMultiply(gamma[i][j]);
                    newCovMats[j] = newCovMats[j].add(dataCov);
                }
            }

            // Converting to arrays for use by fitted model
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        for (int v = 0; v < dim; v++) {
            final double[] evec = covMatDec.getEigenvector(v).toArray();
            covMatEigenvectors.setColumn(v, evec);
        }

        final RealMatrix tmpMatrix = covMatEigenvectors.transpose();

        // Scale each eigenvector by the square root of its eigenvalue.
        for (int row = 0; row < dim; row++) {
            final double factor = FastMath.sqrt(covMatEigenvalues[row]);
            for (int col = 0; col < dim; col++) {
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        for (int v = 0; v < dim; v++) {
            final double[] evec = covMatDec.getEigenvector(v).toArray();
            covMatEigenvectors.setColumn(v, evec);
        }

        final RealMatrix tmpMatrix = covMatEigenvectors.transpose();

        // Scale each eigenvector by the square root of its eigenvalue.
        for (int row = 0; row < dim; row++) {
            final double factor = FastMath.sqrt(covMatEigenvalues[row]);
            for (int col = 0; col < dim; col++) {
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            }
        }

        // Compute and return Hat matrix
        // No DME advertised - args valid if we get here
        return Q.multiply(augI).multiply(Q.transpose());
    }

    /**
     * <p>Returns the sum of squared deviations of Y from its mean.</p>
     *
 
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    @Override
    protected RealMatrix calculateBetaVariance() {
        int p = getX().getColumnDimension();
        RealMatrix Raug = qr.getR().getSubMatrix(0, p - 1 , 0, p - 1);
        RealMatrix Rinv = new LUDecomposition(Raug).getSolver().getInverse();
        return Rinv.multiply(Rinv.transpose());
    }

}
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                }
            }
        }

        // Compute and return Hat matrix
        return Q.multiply(augI).multiply(Q.transpose());
    }

    /**
     * <p>Returns the sum of squared deviations of Y from its mean.</p>
     *
 
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