Examples of Jama.Matrix

Jama.Matrix
Jama = Java Matrix class.
The Java Matrix Class provides the fundamental operations of numerical linear algebra. Various constructors create Matrices from two dimensional arrays of double precision floating point numbers. Various "gets" and "sets" provide access to submatrices and matrix elements. Several methods implement basic matrix arithmetic, including matrix addition and multiplication, matrix norms, and element-by-element array operations. Methods for reading and printing matrices are also included. All the operations in this version of the Matrix Class involve real matrices. Complex matrices may be handled in a future version.
Five fundamental matrix decompositions, which consist of pairs or triples of matrices, permutation vectors, and the like, produce results in five decomposition classes. These decompositions are accessed by the Matrix class to compute solutions of simultaneous linear equations, determinants, inverses and other matrix functions. The five decompositions are:
- Cholesky Decomposition of symmetric, positive definite matrices.
- LU Decomposition of rectangular matrices.
- QR Decomposition of rectangular matrices.
- Singular Value Decomposition of rectangular matrices.
- Eigenvalue Decomposition of both symmetric and nonsymmetric square matrices.
Example of use:
Solve a linear system A x = b and compute the residual norm, ||b - A x||.
```
 double[][] vals = {{1.,2.,3},{4.,5.,6.},{7.,8.,10.}}; Matrix A = new Matrix(vals); Matrix b = Matrix.random(3,1); Matrix x = A.solve(b); Matrix r = A.times(x).minus(b); double rnorm = r.normInf(); 
```
@author The MathWorks, Inc. and the National Institute of Standards and Technology. @version 5 August 1998

                                           ColorScience.CAMethod caMethod) {
        return whiteBalance(image, source, REF_T, tint, lightness, 1, null, caMethod);
    }


    static public float[][] whiteBalanceMatrix(float source, float REF_T, float mult, float cameraRGB[][], ColorScience.CAMethod caMethod) {
        Matrix B = new Matrix(ColorScience.chromaticAdaptation(REF_T, source, caMethod));
        Matrix combo = XYZtoRGB.times(B.times(RGBtoZYX));


        Matrix m = combo.times(new Matrix(new double[][]{{1},{1},{1}}));


        double max = m.get(1, 0); // Math.max(m.get(1, 0), Math.max(m.get(1, 0), m.get(2, 0)));
        if (max != 1)
            combo = combo.times(new Matrix(new double[][]{{1/max, 0, 0},{0, 1/max, 0},{0, 0, 1/max}}));


        if (cameraRGB != null)
            combo = combo.times(new Matrix(cameraRGB));


        if (mult != 1)
            combo = combo.times(mult);


        return combo.getArrayFloat();

View Full Code Here

    public static float[][] RGBtoZYX() {
        double mdata[][] = new double[3][3];
        for (int i = 0; i < 3; i++)
            for (int j = 0; j < 3; j++)
                mdata[i][j] = rgbXYZ[i][j];
        Matrix XYZRGB = new Matrix(mdata);


        float[][] S = new Matrix(new double[][] {{wtptXYZ[0], wtptXYZ[1], wtptXYZ[2]}}).times(XYZRGB.inverse()).getArrayFloat();


        return new float[][] {
            {S[0][0] * rgbXYZ[0][0], S[0][0] * rgbXYZ[0][1], S[0][0] * rgbXYZ[0][2]},
            {S[0][1] * rgbXYZ[1][0], S[0][1] * rgbXYZ[1][1], S[0][1] * rgbXYZ[1][2]},
            {S[0][2] * rgbXYZ[2][0], S[0][2] * rgbXYZ[2][1], S[0][2] * rgbXYZ[2][2]}

View Full Code Here

        float G[] = {Cxy[1][0], Cxy[1][1], 1 - Cxy[1][0] - Cxy[1][1]};
        float B[] = {Cxy[2][0], Cxy[2][1], 1 - Cxy[2][0] - Cxy[2][1]};
        float W[] = {DT[0], DT[1], 1 - DT[0] - DT[1]};


        // Compute luminance weights for the primaries using the white point
        Matrix RGB = vec(R, G, B);
        Matrix WGB = vec(W, G, B);
        Matrix RWB = vec(R, W, B);
        Matrix RGW = vec(R, G, W);


        float rgbDet = (float) RGB.det();


        return new float[] {(R[1] * (float) WGB.det()) / (W[1] * rgbDet),
                            (G[1] * (float) RWB.det()) / (W[1] * rgbDet),
                            (B[1] * (float) RGW.det()) / (W[1] * rgbDet)};
    }

View Full Code Here

            };
        }
    }


    static Matrix vec(float[] A, float[] B, float[] C) {
        Matrix ABC = new Matrix(3, 3);
        for (int i = 0; i < 3; i++)
            ABC.set(i, 0, A[i]);
        for (int i = 0; i < 3; i++)
            ABC.set(i, 1, B[i]);
        for (int i = 0; i < 3; i++)
            ABC.set(i, 2, C[i]);
        return ABC;
    }

View Full Code Here

            default:
                method = XYZScaling;
                break;
        }


        Matrix B = new Matrix(method);


        // source illuminant tristimulus in cone response domain
        float[] sXYZ = xy2XYZ(D(source));
        sXYZ = new Matrix(new float[][] {{sXYZ[0], sXYZ[1], sXYZ[2]}}).times(B).getArrayFloat()[0];


        // target illuminant tristimulus in cone response domain
        float[] tXYZ = xy2XYZ(D(target));
        tXYZ = new Matrix(new float[][] {{tXYZ[0], tXYZ[1], tXYZ[2]}}).times(B).getArrayFloat()[0];


        // scaling matrix for the colors
        float[][] diag = new float[][]{
            {sXYZ[0] / tXYZ[0], 0, 0},
            {0, sXYZ[1] / tXYZ[1], 0},
            {0, 0, sXYZ[2] / tXYZ[2]}
        };


        // total tansform
        return B.times(new Matrix(diag)).times(B.inverse()).getArrayFloat();
    }

View Full Code Here

        float sat = Float.MAX_VALUE;
        float minT = 0;
        double wbr = 0, wbg = 0, wbb = 0;
        float tint = 0;


        Matrix color = new Matrix(new double[][]{{rgb[0]}, {rgb[1]}, {rgb[2]}});


        for (int t = 1000; t < 40000; t+= (0.001 * t)) {
            Matrix B = new Matrix(chromaticAdaptation(t, refT, caMethod));
            Matrix combo = XYZtoRGB.times(B.times(RGBtoZYX));


            Matrix adapdedColor = combo.times(color);


            double r = clip(adapdedColor.get(0, 0));
            double g = clip(adapdedColor.get(1, 0));
            double b = clip(adapdedColor.get(2, 0));


            float tSat = (float) saturation(r, g, b);


            if (tSat < sat) {
                sat = tSat;

View Full Code Here

        float minT = 0;


        float[] xyzRef = JAIContext.linearColorSpace.toCIEXYZ(rgb);
        float[] labRef = JAIContext.labColorSpace.fromCIEXYZ(xyzRef);        


        Matrix gray = new Matrix(new double[][]{{0.18}, {0.18}, {0.18}});


        for (int t = 1000; t < 40000; t+= (0.001 * t)) {
            Matrix B = new Matrix(chromaticAdaptation(t, refT, caMethod));
            Matrix combo = XYZtoRGB.times(B.times(RGBtoZYX));


            gray = combo.times(gray);


            double r = clip(gray.get(0, 0));
            double g = clip(gray.get(1, 0));
            double b = clip(gray.get(2, 0));

View Full Code Here

            // wtptXYZ -> wtptxy
            final float[] wtptxy = XYZ2xy(wtptXYZ);
            whitePointTemperature = CCTX(wtptxy[0]);
            W = W(whitePointTemperature, Cxy);


            RGBToXYZMat = new Matrix (new float[][] {
                mul(W[0] / Zr[1], Zr),
                mul(W[1] / Zg[1], Zg),
                mul(W[2] / Zb[1], Zb)
            }).transpose().getArrayFloat();


            XYZToRGBMat = new Matrix(RGBToXYZMat).inverse().getArrayFloat();
        }

View Full Code Here

            return strip(t);
        }


        public double[][] toRGB(int dataType) {
            double[][] t = scaleTransform(getTransform(), dataType);
            return strip(new Matrix(t).inverse().getArray());
        }

View Full Code Here


        /* compute eigen vectors for 3*3 matrix */


        double[] m = {xx, xy, xz, xy, yy, yz, xz, yz, zz};


        Matrix matrix = new Matrix(m, 3);


        EigenvalueDecomposition ed = matrix.eig();


        double x1 = ed.getV().get(0, 2);
        double y1 = ed.getV().get(1, 2);
        double z1 = ed.getV().get(2, 2);

View Full Code Here

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