Package org.apache.commons.math3.analysis.differentiation

Examples of org.apache.commons.math3.analysis.differentiation.DerivativeStructure.multiply()

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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 col = 0; col < dim; col++) {
                tmpMatrix.multiplyEntry(row, col, factor);
            }
        }

        samplingMatrix = covMatEigenvectors.multiply(tmpMatrix);
    }

    /**
     * Gets the mean vector.
     *
 
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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 col = 0; col < dim; col++) {
                tmpMatrix.multiplyEntry(row, col, factor);
            }
        }

        samplingMatrix = covMatEigenvectors.multiply(tmpMatrix);
    }

    /**
     * Gets the mean vector.
     *
 
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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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    protected RealVector calculateBeta() {
        RealMatrix OI = getOmegaInverse();
        RealMatrix XT = getX().transpose();
        RealMatrix XTOIX = XT.multiply(OI).multiply(getX());
        RealMatrix inverse = new LUDecomposition(XTOIX).getSolver().getInverse();
        return inverse.multiply(XT).multiply(OI).operate(getY());
    }

    /**
     * Calculates the variance on the beta.
     * <pre>
 
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            // Sort by fitness and compute weighted mean into xmean
            final int[] arindex = sortedIndices(fitness);
            // Calculate new xmean, this is selection and recombination
            final RealMatrix xold = xmean; // for speed up of Eq. (2) and (3)
            final RealMatrix bestArx = selectColumns(arx, MathArrays.copyOf(arindex, mu));
            xmean = bestArx.multiply(weights);
            final RealMatrix bestArz = selectColumns(arz, MathArrays.copyOf(arindex, mu));
            final RealMatrix zmean = bestArz.multiply(weights);
            final boolean hsig = updateEvolutionPaths(zmean, xold);
            if (diagonalOnly <= 0) {
                updateCovariance(hsig, bestArx, arz, arindex, xold);
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            // Sort by fitness and compute weighted mean into xmean
            final int[] arindex = sortedIndices(fitness);
            // Calculate new xmean, this is selection and recombination
            final RealMatrix xold = xmean; // for speed up of Eq. (2) and (3)
            final RealMatrix bestArx = selectColumns(arx, MathArrays.copyOf(arindex, mu));
            xmean = bestArx.multiply(weights);
            final RealMatrix bestArz = selectColumns(arz, MathArrays.copyOf(arindex, mu));
            final RealMatrix zmean = bestArz.multiply(weights);
            final boolean hsig = updateEvolutionPaths(zmean, xold);
            if (diagonalOnly <= 0) {
                updateCovariance(hsig, bestArx, arz, arindex, xold);
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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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