Java Code Examples for org.apache.commons.math3.linear.RealVector#toArray()

@Override
public SlopeCoefficients estimateCoefficients(final DerivationEquation eq)
    throws EstimationException {
  final double[][] sourceTriangleMatrix = eq.getCovarianceLowerTriangularMatrix();
  // Copy matrix and enhance it to a full matrix as expected by CholeskyDecomposition
  // FIXME: Avoid copy job to speed-up the solving process e.g. by extending the CholeskyDecomposition constructor
  final int length = sourceTriangleMatrix.length;
  final double[][] matrix = new double[length][];
  for (int i = 0; i < length; i++) {
    matrix[i] = new double[length];
    final double[] s = sourceTriangleMatrix[i];
    final double[] t = matrix[i];
    for (int j = 0; j <= i; j++) {
      t[j] = s[j];
    }
    for (int j = i + 1; j < length; j++) {
      t[j] = sourceTriangleMatrix[j][i];
    }
  }
  final RealMatrix coefficients =
      new Array2DRowRealMatrix(matrix, false);
  try {
    final DecompositionSolver solver = new CholeskyDecomposition(coefficients).getSolver();
    final RealVector constants = new ArrayRealVector(eq.getConstraints(), true);
    final RealVector solution = solver.solve(constants);
    return new DefaultSlopeCoefficients(solution.toArray());
  } catch (final NonPositiveDefiniteMatrixException e) {
    throw new EstimationException("Matrix inversion error due to data is linearly dependent", e);
  }
}
 

/**
 * Get the -1 times the sum of all coefficients in the given array.
 * @param coefficients coefficients to sum
 * @return the -1 times the sum of all coefficients in the given array.
 */
protected static double getInvertedCoefficientSum(final RealVector coefficients) {
    double sum = 0;
    for (double coefficient : coefficients.toArray()) {
        sum -= coefficient;
    }
    return sum;
}
 

/**
 * Get the -1 times the sum of all coefficients in the given array.
 * @param coefficients coefficients to sum
 * @return the -1 times the sum of all coefficients in the given array.
 */
protected static double getInvertedCoefficientSum(final RealVector coefficients) {
    double sum = 0;
    for (double coefficient : coefficients.toArray()) {
        sum -= coefficient;
    }
    return sum;
}
 

/**
 * Get the -1 times the sum of all coefficients in the given array.
 * @param coefficients coefficients to sum
 * @return the -1 times the sum of all coefficients in the given array.
 */
protected static double getInvertedCoefficientSum(final RealVector coefficients) {
    double sum = 0;
    for (double coefficient : coefficients.toArray()) {
        sum -= coefficient;
    }
    return sum;
}
 

/**
 * Get the -1 times the sum of all coefficients in the given array.
 * @param coefficients coefficients to sum
 * @return the -1 times the sum of all coefficients in the given array.
 */
protected static double getInvertedCoefficientSum(final RealVector coefficients) {
    double sum = 0;
    for (double coefficient : coefficients.toArray()) {
        sum -= coefficient;
    }
    return sum;
}
 

/**
 * Get the -1 times the sum of all coefficients in the given array.
 * @param coefficients coefficients to sum
 * @return the -1 times the sum of all coefficients in the given array.
 */
protected static double getInvertedCoefficientSum(final RealVector coefficients) {
    double sum = 0;
    for (double coefficient : coefficients.toArray()) {
        sum -= coefficient;
    }
    return sum;
}
 

public MultivariateNormal(RealVector mean, RealMatrix sigma) {
    double[][] arrayOfMatrix = new double[sigma.getColumnDimension()][sigma.getRowDimension()];
    for (int i = 0; i < sigma.getColumnDimension(); i++) {
        arrayOfMatrix[i] = sigma.getRow(i);
    }
    distribution = new MultivariateNormalDistribution(mean.toArray(), arrayOfMatrix);
}
 

/** {@inheritDoc} */
public Pair<RealVector, RealMatrix> value(final RealVector point) {
    //TODO get array from RealVector without copying?
    final double[] p = point.toArray();

    // Evaluate.
    return new Pair<RealVector, RealMatrix>(computeValue(p),
                                            computeJacobian(p));
}
 

/**
 * Get the -1 times the sum of all coefficients in the given array.
 * @param coefficients coefficients to sum
 * @return the -1 times the sum of all coefficients in the given array.
 */
protected static double getInvertedCoefficientSum(final RealVector coefficients) {
    double sum = 0;
    for (double coefficient : coefficients.toArray()) {
        sum -= coefficient;
    }
    return sum;
}
 

/**
 * Get the -1 times the sum of all coefficients in the given array.
 * @param coefficients coefficients to sum
 * @return the -1 times the sum of all coefficients in the given array.
 */
protected static double getInvertedCoefficientSum(final RealVector coefficients) {
    double sum = 0;
    for (double coefficient : coefficients.toArray()) {
        sum -= coefficient;
    }
    return sum;
}
 

/**
 * Get the -1 times the sum of all coefficients in the given array.
 * @param coefficients coefficients to sum
 * @return the -1 times the sum of all coefficients in the given array.
 */
protected static double getInvertedCoefficientSum(final RealVector coefficients) {
    double sum = 0;
    for (double coefficient : coefficients.toArray()) {
        sum -= coefficient;
    }
    return sum;
}
 

/**
 * Calculates the parameters of a bivariate quadratic equation.
 * 
 * @param elevationValues the window of points to use.
 * @return the parameters of the bivariate quadratic equation as [a, b, c, d, e, f]
 */
private static double[] calculateParameters( final double[][] elevationValues ) {
    int rows = elevationValues.length;
    int cols = elevationValues[0].length;
    int pointsNum = rows * cols;

    final double[][] xyMatrix = new double[pointsNum][6];
    final double[] valueArray = new double[pointsNum];

    // TODO check on resolution
    int index = 0;
    for( int y = 0; y < rows; y++ ) {
        for( int x = 0; x < cols; x++ ) {
            xyMatrix[index][0] = x * x; // x^2
            xyMatrix[index][1] = y * y; // y^2
            xyMatrix[index][2] = x * y; // xy
            xyMatrix[index][3] = x; // x
            xyMatrix[index][4] = y; // y
            xyMatrix[index][5] = 1;
            valueArray[index] = elevationValues[y][x];
            index++;
        }
    }

    RealMatrix A = MatrixUtils.createRealMatrix(xyMatrix);
    RealVector z = MatrixUtils.createRealVector(valueArray);

    DecompositionSolver solver = new RRQRDecomposition(A).getSolver();
    RealVector solution = solver.solve(z);

    // start values for a, b, c, d, e, f, all set to 0.0
    final double[] parameters = solution.toArray();
    return parameters;
}
 

/**
 * Get the -1 times the sum of all coefficients in the given array.
 * @param coefficients coefficients to sum
 * @return the -1 times the sum of all coefficients in the given array.
 */
protected static double getInvertedCoefficientSum(final RealVector coefficients) {
    double sum = 0;
    for (double coefficient : coefficients.toArray()) {
        sum -= coefficient;
    }
    return sum;
}
 

/**
 * Get the -1 times the sum of all coefficients in the given array.
 * @param coefficients coefficients to sum
 * @return the -1 times the sum of all coefficients in the given array.
 */
protected static double getInvertedCoefficientSum(final RealVector coefficients) {
    double sum = 0;
    for (double coefficient : coefficients.toArray()) {
        sum -= coefficient;
    }
    return sum;
}
 

/**
 * {@inheritDoc}
 */
public double[] estimateRegressionParameters() {
    RealVector b = calculateBeta();
    return b.toArray();
}
 

/**
 * {@inheritDoc}
 */
public double[] estimateResiduals() {
    RealVector b = calculateBeta();
    RealVector e = yVector.subtract(xMatrix.operate(b));
    return e.toArray();
}
 

/**
 * {@inheritDoc}
 */
public double[] estimateResiduals() {
    RealVector b = calculateBeta();
    RealVector e = yVector.subtract(xMatrix.operate(b));
    return e.toArray();
}
 

/**
 * {@inheritDoc}
 */
public double[] estimateRegressionParameters() {
    RealVector b = calculateBeta();
    return b.toArray();
}
 

/**
 * {@inheritDoc}
 */
public double[] estimateRegressionParameters() {
    RealVector b = calculateBeta();
    return b.toArray();
}
 

/**
 * {@inheritDoc}
 */
public double[] estimateResiduals() {
    RealVector b = calculateBeta();
    RealVector e = yVector.subtract(xMatrix.operate(b));
    return e.toArray();
}