Jama.Matrix.getArray()方法的使用及代码示例

x33g5p2x  于2022-01-24 转载在 其他  
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本文整理了Java中Jama.Matrix.getArray()方法的一些代码示例，展示了Matrix.getArray()的具体用法。这些代码示例主要来源于Github/Stackoverflow/Maven等平台，是从一些精选项目中提取出来的代码，具有较强的参考意义，能在一定程度帮忙到你。Matrix.getArray()方法的具体详情如下：
包路径：Jama.Matrix
类名称：Matrix
方法名：getArray

Matrix.getArray介绍

暂无

代码示例

代码示例来源：origin: marytts/marytts

public double[][] getDataProjected(Matrix data, boolean debug) {
  // Project the original data set
  Matrix dataProjected;
  dataProjected = PC.transpose().times(data);
  if (debug) {
    System.out.println("Data projected:");
    dataProjected.print(dataProjected.getRowDimension(), 3);
  }
  return dataProjected.getArray();
}

代码示例来源：origin: h2oai/h2o-3

static double[] multiple(double[] diagYY /*diagonal*/, int nTot, int nVars) {
 int ny = diagYY.length;
 for (int i = 0; i < ny; i++) {
  diagYY[i] *= nTot;
 }
 double[][] uu = new double[ny][ny];
 for (int i = 0; i < ny; i++) {
  for (int j = 0; j < ny; j++) {
   double yyij = i==j ? diagYY[i] : 0;
   uu[i][j] = (yyij - diagYY[i] * diagYY[j] / nTot) / (nVars * Math.sqrt(diagYY[i] * diagYY[j]));
   if (Double.isNaN(uu[i][j])) {
    uu[i][j] = 0;
   }
  }
 }
 EigenvalueDecomposition eigen = new EigenvalueDecomposition(new Matrix(uu));
 double[] eigenvalues = eigen.getRealEigenvalues();
 double[][] eigenvectors = eigen.getV().getArray();
 int maxIndex = ArrayUtils.maxIndex(eigenvalues);
 return eigenvectors[maxIndex];
}

代码示例来源：origin: marytts/marytts

int M = data.getRowDimension();
int N = data.getColumnDimension();
double sd;
for (int i = 0; i < M; i++) {
  mn = MathUtils.mean(data.getArray()[i]);
  if (mn == 0.0)
    throw new Error("eigenPCA: mean of dimension " + (i + 1) + " is 0.0");
  if (scale) {
    sd = MathUtils.standardDeviation(data.getArray()[i]);
    if (sd == 0.0)
      throw new Error("eigenPCA: variance of dimension " + (i + 1) + " is 0.0");
  data.print(data.getRowDimension(), 3);
if (debug) {
  System.out.println("Covariance");
  covariance.print(covariance.getRowDimension(), 3);
    d[i][j] = pc.getV().get(i, k);
PC = new Matrix(d);
if (debug) {
  System.out.println("PC:");

代码示例来源：origin: h2oai/h2o-2

for( int j = 0; j < i; ++j )
  arr[j][i] = arr[i][j];
CholeskyDecomposition c = new Matrix(arr).chol();
fchol.setSPD(c.isSPD());
arr = c.getL().getArray();

代码示例来源：origin: marytts/marytts

int M = data.getRowDimension();
int N = data.getColumnDimension();
double sd;
for (int i = 0; i < M; i++) {
  mn = MathUtils.mean(data.getArray()[i]);
  if (mn == 0.0)
    throw new Error("eigenPCA: mean of dimension " + (i + 1) + " is 0.0");
  if (scale) {
    sd = MathUtils.standardDeviation(data.getArray()[i]);
    if (sd == 0.0)
      throw new Error("eigenPCA: variance of dimension " + (i + 1) + " is 0.0");
  data.print(data.getRowDimension(), 3);
if (debug) {
  System.out.println("Covariance");
  covariance.print(covariance.getRowDimension(), 3);
    d[i][j] = pc.getV().get(i, k);
PC = new Matrix(d);
if (debug) {
  System.out.println("PC:");

代码示例来源：origin: marytts/marytts

public double[][] getDataProjected(Matrix data, boolean debug) {
  // Project the original data set
  Matrix dataProjected;
  dataProjected = PC.transpose().times(data);
  if (debug) {
    System.out.println("Data projected:");
    dataProjected.print(dataProjected.getRowDimension(), 3);
  }
  return dataProjected.getArray();
}

代码示例来源：origin: h2oai/h2o-2

public PCAModel buildModel(DataInfo dinfo, GramTask tsk) {
 logStart();
 Matrix myGram = new Matrix(tsk._gram.getXX());   // X'X/n where n = num rows
 SingularValueDecomposition mySVD = myGram.svd();
 // Extract eigenvalues and eigenvectors
 // Note: Singular values ordered in weakly descending order by algorithm
 double[] Sval = mySVD.getSingularValues();
 double[][] eigVec = mySVD.getV().getArray();  // rows = features, cols = principal components
 assert Sval.length == eigVec.length;
 // DKV.put(EigenvectorMatrix.makeKey(input("source"), destination_key), new EigenvectorMatrix(eigVec));
 // Compute standard deviation
 double[] sdev = new double[Sval.length];
 double totVar = 0;
 double dfcorr = dinfo._adaptedFrame.numRows()/(dinfo._adaptedFrame.numRows() - 1.0);
 for(int i = 0; i < Sval.length; i++) {
  // if(standardize)
   Sval[i] = dfcorr*Sval[i];   // Correct since degrees of freedom = n-1
  sdev[i] = Math.sqrt(Sval[i]);
  totVar += Sval[i];
 }
 double[] propVar = new double[Sval.length];    // Proportion of total variance
 double[] cumVar = new double[Sval.length];    // Cumulative proportion of total variance
 for(int i = 0; i < Sval.length; i++) {
  propVar[i] = Sval[i]/totVar;
  cumVar[i] = i == 0 ? propVar[0] : cumVar[i-1] + propVar[i];
 }
 Key dataKey = input("source") == null ? null : Key.make(input("source"));
 int ncomp = Math.min(getNumPC(sdev, tolerance), max_pc);
 return new PCAModel(this, destination_key, dataKey, dinfo, tsk, sdev, propVar, cumVar, eigVec, mySVD.rank(), ncomp);
}

代码示例来源：origin: h2oai/h2o-3

false, false, false);
GramTask gtsk = new GramTask(_job._key, tbInfo).doAll(tB);
Matrix gramJ = new Matrix(gtsk._gram.getXX());  // form outer gram
SingularValueDecomposition svdJ = gramJ.svd();
u=makeUVec(model, u_name, u, qfrm, new Matrix(stsk._atq), svdJ);
model._output._d = ArrayUtils.mult((Arrays.copyOfRange(ArrayUtils.sqrtArr(svdJ.getSingularValues()),
    0, _parms._nv)), sqrt(tB.numRows()));
Matrix atqJ = new Matrix(stsk._atq);
SingularValueDecomposition svdJ = atqJ.svd();
model._output._v = svdJ.getU().getMatrix(0, atqJ.getRowDimension() - 1, 0, _parms._nv - 1).getArray();

代码示例来源：origin: marytts/marytts

private int[] checkMeanColumns(String dataFile, int Y[], String[] features) {
  try {
    BufferedReader reader = new BufferedReader(new FileReader(dataFile));
    Matrix data = Matrix.read(reader);
    reader.close();
    data = data.transpose(); // then I have easy access to the columns
    int rows = data.getRowDimension() - 1;
    int cols = data.getColumnDimension() - 1;
    data = data.getMatrix(0, rows, 1, cols); // dataVowels(:,1:cols) -> dependent variables
    int M = data.getRowDimension();
    double mn;
    for (int i = 0; i < M; i++) {
      mn = MathUtils.mean(data.getArray()[i]);
      if (mn == 0.0) {
        System.out.println("Removing feature: " + features[i] + " from list of features because it has mean=0.0");
        Y = MathUtils.removeIndex(Y, i);
      }
    }
  } catch (Exception e) {
    throw new RuntimeException("Problem reading file " + dataFile, e);
  }
  System.out.println();
  return Y;
}

代码示例来源：origin: h2oai/h2o-3

Matrix x_r = new Matrix(xxchol.getL()).transpose();
x_r = x_r.times(Math.sqrt(nobs));
Matrix yt = new Matrix(model._output._archetypes_raw.getY(true));
QRDecomposition yt_qr = new QRDecomposition(yt);
 model._output._eigenvectors_raw = eigvec.getArray();

代码示例来源：origin: openimaj/openimaj

/**
 * see {@link UpdateableCholeskyDecomposition#choldowndate(double[][], double[])}
 * @param x
 * @param b
 */
public void choldowndate(double[] x, boolean b) {
  if(b)  x = x.clone();
  Matrix L = this.getL();
  // work is done on an upper triangular matrix
  double[][] data = L.transpose().getArray();
  choldowndate(data, x);
  // Make the output lower triangular again
  int Ll = L.getRowDimension();
  L.setMatrix(0, Ll-1, 0, Ll-1, new Matrix(data, Ll, Ll).transpose());
}

代码示例来源：origin: marytts/marytts

private int[] checkMeanColumns(String dataFile, int Y[], String[] features) {
  try {
    BufferedReader reader = new BufferedReader(new FileReader(dataFile));
    Matrix data = Matrix.read(reader);
    reader.close();
    data = data.transpose(); // then I have easy access to the columns
    int rows = data.getRowDimension() - 1;
    int cols = data.getColumnDimension() - 1;
    data = data.getMatrix(0, rows, 1, cols); // dataVowels(:,1:cols) -> dependent variables
    int M = data.getRowDimension();
    double mn;
    for (int i = 0; i < M; i++) {
      mn = MathUtils.mean(data.getArray()[i]);
      if (mn == 0.0) {
        System.out.println("Removing feature: " + features[i] + " from list of features because it has mean=0.0");
        Y = MathUtils.removeIndex(Y, i);
      }
    }
  } catch (Exception e) {
    throw new RuntimeException("Problem reading file " + dataFile, e);
  }
  System.out.println();
  return Y;
}

代码示例来源：origin: h2oai/h2o-2

public void test () {
 Log.info("CholTest::test enter");
 for (int sz = 6000; sz < 10000; sz+=2000) {
  Log.info("CholTest::test sz is " + sz);
  DataSetup data = new DataSetup(sz, 12345);
  long start = System.currentTimeMillis();
  CholeskyDecomposition jamaChol = new Matrix(data.xx).chol();
  Log.info("JAMA CHOLESKY [N = " + sz + "] TAKES " + (System.currentTimeMillis() - start) + " MILLISECONDS.");
  if (!jamaChol.isSPD()) continue;
  ForkJoinPool fjp = new ForkJoinPool(32);
  for (int t = 2; t <= 32; t += 2) {
   for (int step : STEPS)
    fjp.invoke(new TestSetup(new DataSetup(data.xx),jamaChol.getL().getArray(),step,t));
  }
 }
 Log.info("CholTest::test exit");
}

代码示例来源：origin: sc.fiji/TrakEM2_

public void fit( final double x[][], final double y[][], final double lambda )
{
  final double[][] expandedX = kernelExpandMatrixNormalize( x );
  final Matrix phiX = new Matrix( expandedX, expandedX.length, length );
  final Matrix phiXTransp = phiX.transpose();
  final Matrix phiXProduct = phiXTransp.times( phiX );
  final int l = phiXProduct.getRowDimension();
  final double lambda2 = 2 * lambda;
  for (int i = 0; i < l; ++i )
    phiXProduct.set( i, i, phiXProduct.get( i, i ) + lambda2 );
  final Matrix phiXPseudoInverse = phiXProduct.inverse();
  final Matrix phiXProduct2 = phiXPseudoInverse.times( phiXTransp );
  final Matrix betaMatrix = phiXProduct2.times( new Matrix( y, y.length, 2 ) );
  setBeta( betaMatrix.getArray() );
}

代码示例来源：origin: marytts/marytts

int M = data.getRowDimension();
int N = data.getColumnDimension();
double sd;
for (int i = 0; i < M; i++) {
  mn = MathUtils.mean(data.getArray()[i]);
  if (mn == 0.0)
    throw new Error("svdPCA: mean of dimension " + (i + 1) + " is 0.0");
  if (scale) {
    sd = MathUtils.standardDeviation(data.getArray()[i]);
    if (sd == 0.0)
      throw new Error("svdPCA: variance of dimension " + (i + 1) + " is 0.0");
  data.print(data.getRowDimension(), 3);
V = new double[svd.getS().getRowDimension()];
for (int i = 0; i < svd.getS().getRowDimension(); i++) {
  V[i] = svd.getS().get(i, i);

代码示例来源：origin: h2oai/h2o-3

Matrix gramJ = new Matrix(gtsk._gram.getXX());
SingularValueDecomposition svdJ = gramJ.svd();
double[][] v = svdJ.getV().getArray();
assert v.length == _ncolExp && LinearAlgebraUtils.numColsExp(dinfo._adaptedFrame,_parms._use_all_factor_levels) == _ncolExp;
model._output._v = MemoryManager.malloc8d(_ncolExp, _parms._nv);

代码示例来源：origin: marytts/marytts

int M = data.getRowDimension();
int N = data.getColumnDimension();
double sd;
for (int i = 0; i < M; i++) {
  mn = MathUtils.mean(data.getArray()[i]);
  if (mn == 0.0)
    throw new Error("svdPCA: mean of dimension " + (i + 1) + " is 0.0");
  if (scale) {
    sd = MathUtils.standardDeviation(data.getArray()[i]);
    if (sd == 0.0)
      throw new Error("svdPCA: variance of dimension " + (i + 1) + " is 0.0");
  data.print(data.getRowDimension(), 3);
V = new double[svd.getS().getRowDimension()];
for (int i = 0; i < svd.getS().getRowDimension(); i++) {
  V[i] = svd.getS().get(i, i);

代码示例来源：origin: stackoverflow.com

double[][] array = {{1.,2.,3},{1.,2.,3.},{1.,2.,3.}}; 
Matrix a = new Matrix(array);   
Matrix b = new Matrix(new double[]{1., 1., 1.}, 1);     
Matrix c = b.times(a);  
System.out.println(Arrays.deepToString(c.getArray()));

代码示例来源：origin: openimaj/openimaj

private double dotCol(Matrix colvec, Matrix m, int col) {
  final int rows = colvec.getRowDimension();
  final double[][] colvec_arr = colvec.getArray();
  final double[][] m_arr = m.getArray();
  double dp = 0;
  for (int i = 0; i < rows; i++)
    dp += colvec_arr[i][0] * m_arr[i][col];
  return dp;
}

代码示例来源：origin: h2oai/h2o-3

Matrix ysmall = new Matrix(stsk._atq);  // small only for n_exp << m.  Not for wide dataset.
QRDecomposition ysmall_qr = new QRDecomposition(ysmall);
ysmall_q = ysmall_qr.getQ().getArray();  // memory allocation here too.

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