Java 类org.apache.commons.math3.linear.Array2DRowRealMatrix 实例源码

项目：Machine-Learning-End-to-Endguide-for-Java-developers 文件：MathExamples.java

public void apacheCommonsExample() {
    double[][] A = {
        {0.1950, 0.0311},
        {0.3588, 0.2203},
        {0.1716, 0.5931},
        {0.2105, 0.3242}};

    double[][] B = {
        {0.0502, 0.9823, 0.9472},
        {0.5732, 0.2694, 0.916}};

    RealMatrix aRealMatrix = new Array2DRowRealMatrix(A);
    RealMatrix bRealMatrix = new Array2DRowRealMatrix(B);

    RealMatrix cRealMatrix = aRealMatrix.multiply(bRealMatrix);
    System.out.println();
    for (int i = 0; i < cRealMatrix.getRowDimension(); i++) {
        System.out.println(cRealMatrix.getRowVector(i));
    }
}

项目：FSensor 文件：RotationMeasurementModel.java

public RotationMeasurementModel()
{
    super();

    // H = measurementMatrix
    measurementMatrix = new Array2DRowRealMatrix(new double[][]
    {
    { 1, 0, 0, 0 },
    { 0, 1, 0, 0 },
    { 0, 0, 1, 0 },
    { 0, 0, 0, 1 } });

    // R = measurementNoise
    measurementNoise = new Array2DRowRealMatrix(new double[][]
    {
    { noiseCoefficient, 0, 0, 0 },
    { 0, noiseCoefficient, 0, 0 },
    { 0, 0, noiseCoefficient, 0 },
    { 0, 0, 0, noiseCoefficient } });
}

项目：Java-Data-Science-Made-Easy 文件：MathExamples.java

public void apacheCommonsExample() {
    double[][] A = {
        {0.1950, 0.0311},
        {0.3588, 0.2203},
        {0.1716, 0.5931},
        {0.2105, 0.3242}};

    double[][] B = {
        {0.0502, 0.9823, 0.9472},
        {0.5732, 0.2694, 0.916}};

    RealMatrix aRealMatrix = new Array2DRowRealMatrix(A);
    RealMatrix bRealMatrix = new Array2DRowRealMatrix(B);

    RealMatrix cRealMatrix = aRealMatrix.multiply(bRealMatrix);
    System.out.println();
    for (int i = 0; i < cRealMatrix.getRowDimension(); i++) {
        System.out.println(cRealMatrix.getRowVector(i));
    }
}

项目：Java-for-Data-Science 文件：MathExamples.java

public void apacheCommonsExample() {
    double[][] A = {
        {0.1950, 0.0311},
        {0.3588, 0.2203},
        {0.1716, 0.5931},
        {0.2105, 0.3242}};

    double[][] B = {
        {0.0502, 0.9823, 0.9472},
        {0.5732, 0.2694, 0.916}};

    RealMatrix aRealMatrix = new Array2DRowRealMatrix(A);
    RealMatrix bRealMatrix = new Array2DRowRealMatrix(B);

    RealMatrix cRealMatrix = aRealMatrix.multiply(bRealMatrix);
    System.out.println();
    for (int i = 0; i < cRealMatrix.getRowDimension(); i++) {
        System.out.println(cRealMatrix.getRowVector(i));
    }
}

项目：BNPMix.java 文件：Generator.java

public RealMatrix nextWishart(double df, Cholesky invscale) {
  int d = invscale.getL().getColumnDimension();
  Array2DRowRealMatrix A = new Array2DRowRealMatrix(d,d);
  ArrayRealVector v = new ArrayRealVector(d);
  for (int i=0; i<d; i++) {
    v.setEntry(i, sqrt(nextChiSquared(df-i)));
    for (int j=0; j<i; j++) {
      v.setEntry(j, 0.0);
    }
    for (int j=i+1; j<d; j++) {
      v.setEntry(j, nextGaussian());
    }
    A.setColumnVector(i, invscale.solveLT(v));
  }
  return A.multiply(A.transpose());
}

项目：BNPMix.java 文件：Generator.java

public static void main(String[] args) {
  // test wishart
  double[][] s = {{2.0,1.0,0.0},{1.0,2.0,1.0},{0.0,1.0,2.0}};
  RealMatrix S = new Array2DRowRealMatrix(s);
  Cholesky C = new Cholesky(S);
  double df = 2.4;
  RealMatrix sum = new Array2DRowRealMatrix(3,3);
  for (int i=0; i<100000; i++) {
    RealMatrix sample = generator.nextWishart(df, C);
    sum = sum.add(sample);
  }
  sum = sum.scalarMultiply(1.0/100000.0);
  System.out.println(sum.getRowVector(0));
  System.out.println(sum.getRowVector(1));
  System.out.println(sum.getRowVector(2));
}

项目：oryx2 文件：LinearSystemSolver.java

/**
 * @param data dense matrix represented in row-major form
 * @return solver for the system Ax = b
 */
static Solver getSolver(double[][] data) {
  if (data == null) {
    return null;
  }
  RealMatrix M = new Array2DRowRealMatrix(data, false);
  double infNorm = M.getNorm();
  double singularityThreshold = infNorm * SINGULARITY_THRESHOLD_RATIO;
  RRQRDecomposition decomposition = new RRQRDecomposition(M, singularityThreshold);
  DecompositionSolver solver = decomposition.getSolver();
  if (solver.isNonSingular()) {
    return new Solver(solver);
  }
  // Otherwise try to report apparent rank
  int apparentRank = decomposition.getRank(0.01); // Better value?
  log.warn("{} x {} matrix is near-singular (threshold {}). Add more data or decrease the " +
           "number of features, to <= about {}",
           M.getRowDimension(), 
           M.getColumnDimension(),
           singularityThreshold,
           apparentRank);
  throw new SingularMatrixSolverException(apparentRank, "Apparent rank: " + apparentRank);
}

项目：morpheus-core 文件：XDataFrameLeastSquares.java

/**
 * Creates the X design matrix for this regression model
 * @return  the X design matrix
 */
RealMatrix createX() {
    final int n = frame.rows().count();
    final int offset = hasIntercept() ? 1 : 0;
    final int p = hasIntercept() ? regressors.size() + 1 : regressors.size();
    final int[] colIndexes = regressors.stream().mapToInt(k -> frame.cols().ordinalOf(k)).toArray();
    final RealMatrix x = new Array2DRowRealMatrix(n, p);
    for (int i = 0; i < n; ++i) {
        x.setEntry(i, 0, 1d);
        for (int j = offset; j < p; ++j) {
            final double value = frame.data().getDouble(i, colIndexes[j - offset]);
            x.setEntry(i, j, value);
        }
    }
    return x;
}

项目：hdf5-java-bindings 文件：HDF5LibraryUnitTest.java

@Test
public void testCreateLargeMatrix(){
    // Creates a large PoN of junk values and simply tests that these can be written and read.

    // Make a big, fake set of read counts.
    final int numRows = 2500000;
    final int numCols = 10;
    final double mean = 3e-7;
    final double sigma = 1e-9;

    final RealMatrix bigCounts = createMatrixOfGaussianValues(numRows, numCols, mean, sigma);
    final File tempOutputHD5 = Utils.createTempFile("big-ol-", ".hd5");
    final HDF5File hdf5File = new HDF5File(tempOutputHD5, HDF5File.OpenMode.CREATE);
    final String hdf5Path = "/test/m";
    hdf5File.makeDoubleMatrix(hdf5Path, bigCounts.getData());
    hdf5File.close();

    final HDF5File hdf5FileForReading = new HDF5File(tempOutputHD5, HDF5File.OpenMode.READ_ONLY);
    final double[][] result = hdf5FileForReading.readDoubleMatrix(hdf5Path);
    final RealMatrix resultAsRealMatrix = new Array2DRowRealMatrix(result);
    Assert.assertTrue(resultAsRealMatrix.getRowDimension() == numRows);
    Assert.assertTrue(resultAsRealMatrix.getColumnDimension() == numCols);
    final RealMatrix readMatrix = new Array2DRowRealMatrix(result);
    assertEqualsMatrix(readMatrix, bigCounts, false);
}

项目：Fog-Computing-Mobile-Architecture 文件：PowerVmSelectionPolicyMaximumCorrelation.java

/**
 * Gets the correlation coefficients.
 * 
 * @param data the data
 * @return the correlation coefficients
 */
protected List<Double> getCorrelationCoefficients(final double[][] data) {
    int n = data.length;
    int m = data[0].length;
    List<Double> correlationCoefficients = new LinkedList<Double>();
    for (int i = 0; i < n; i++) {
        double[][] x = new double[n - 1][m];
        int k = 0;
        for (int j = 0; j < n; j++) {
            if (j != i) {
                x[k++] = data[j];
            }
        }

        // Transpose the matrix so that it fits the linear model
        double[][] xT = new Array2DRowRealMatrix(x).transpose().getData();

        // RSquare is the "coefficient of determination"
        correlationCoefficients.add(MathUtil.createLinearRegression(xT,
                data[i]).calculateRSquared());
    }
    return correlationCoefficients;
}