Package org.apache.commons.math3.linear

Examples of org.apache.commons.math3.linear.RealVector.toArray()

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        // action
        Line reverted = line.revert();

        // verify
        Assert.assertArrayEquals(expected.toArray(), reverted.getDirection().toArray(), 0);

    }

}
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        // action
        Line reverted = line.revert();

        // verify
        Assert.assertArrayEquals(expected.toArray(), reverted.getDirection().toArray(), 0);

    }

}
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            g2.setPaint(Color.black);
            g2.drawRect(0, 0, w - 1, h - 1);
           
            for (Vector2D point : points) {
                Vector2D p = transform(point, w, h);
                double[] arr = p.toArray();
                g2.draw(new Rectangle2D.Double(arr[0] - 1, arr[1] - 1, 2, 2));
            }
        }       
       
        @Override
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                .weight(new DiagonalMatrix(new double[]{16, 4}))
                .model(new MultivariateJacobianFunction() {
                    public Pair<RealVector, RealMatrix> value(RealVector actualPoint) {
                        //verify correct values passed in
                        Assert.assertArrayEquals(
                                point.toArray(), actualPoint.toArray(), Precision.EPSILON);
                        //return values
                        return new Pair<RealVector, RealMatrix>(
                                new ArrayRealVector(new double[]{3, 4}),
                                MatrixUtils.createRealMatrix(new double[][]{{5, 6}, {7, 8}})
                        );
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        //action
        RealVector residuals = evaluation.getResiduals();
        RealMatrix jacobian = evaluation.getJacobian();

        //verify
        Assert.assertArrayEquals(evaluation.getPoint().toArray(), point.toArray(), 0);
        Assert.assertArrayEquals(new double[]{-12, -8}, residuals.toArray(), Precision.EPSILON);
        TestUtils.assertEquals(
                "jacobian",
                jacobian,
                MatrixUtils.createRealMatrix(new double[][]{{20, 24},{14, 16}}),
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                .weight(new DiagonalMatrix(new double[]{16, 4}))
                .model(new MultivariateJacobianFunction() {
                    public Pair<RealVector, RealMatrix> value(RealVector actualPoint) {
                        //verify correct values passed in
                        Assert.assertArrayEquals(
                                point.toArray(), actualPoint.toArray(), Precision.EPSILON);
                        //return values
                        return new Pair<RealVector, RealMatrix>(
                                new ArrayRealVector(new double[]{3, 4}),
                                MatrixUtils.createRealMatrix(new double[][]{{5, 6}, {7, 8}})
                        );
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        //action
        RealVector residuals = evaluation.getResiduals();
        RealMatrix jacobian = evaluation.getJacobian();

        //verify
        Assert.assertArrayEquals(evaluation.getPoint().toArray(), point.toArray(), 0);
        Assert.assertArrayEquals(new double[]{-12, -8}, residuals.toArray(), Precision.EPSILON);
        TestUtils.assertEquals(
                "jacobian",
                jacobian,
                MatrixUtils.createRealMatrix(new double[][]{{20, 24},{14, 16}}),
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      }
     
      QRDecomposition qr = new QRDecomposition(matrix);
      RealVector solution = qr.getSolver().solve(vector);
       
      double[] coeffs = solution.toArray();
     
      for (double coeff : coeffs) {
        if (coeff > 10e3) {
          continue calculatePolynomials;
        }
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        }

        int zIndex = (getNumObjectiveFunctions() == 1) ? 0 : 1;
        matrix.setEntry(zIndex, zIndex, maximize ? 1 : -1);
        RealVector objectiveCoefficients = maximize ? f.getCoefficients().mapMultiply(-1) : f.getCoefficients();
        copyArray(objectiveCoefficients.toArray(), matrix.getDataRef()[zIndex]);
        matrix.setEntry(zIndex, width - 1, maximize ? f.getConstantTerm() : -1 * f.getConstantTerm());

        if (!restrictToNonNegative) {
            matrix.setEntry(zIndex, getSlackVariableOffset() - 1,
                            getInvertedCoefficientSum(objectiveCoefficients));
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    /**
     * {@inheritDoc}
     */
    public double[] estimateRegressionParameters() {
        RealVector b = calculateBeta();
        return b.toArray();
    }

    /**
     * {@inheritDoc}
     */
 
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