Jama.Matrix.minus()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.minus()方法的一些代码示例，展示了Matrix.minus()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.minus()方法的具体详情如下：
包路径：Jama.Matrix
类名称：Matrix
方法名：minus

Matrix.minus介绍

暂无

代码示例

代码示例来源：origin: marytts/marytts

Matrix r = data.times(b).minus(indVar);
residuals = r.getColumnPackedCopy();

代码示例来源：origin: marytts/marytts

Matrix r = data.times(b).minus(indVar);
residuals = r.getColumnPackedCopy();

代码示例来源：origin: marytts/marytts

Matrix r = X.times(b).minus(y);
residuals = r.getColumnPackedCopy();

代码示例来源：origin: marytts/marytts

Matrix r = X.times(b).minus(y);
residuals = r.getColumnPackedCopy();

代码示例来源：origin: percyliang/fig

public void sub(SuffStats _stats) { // Remove several data points
 MultGaussianSuffStats stats = (MultGaussianSuffStats)_stats;
 // TODO : in place for efficiency
 sum = sum.minus(stats.sum);
 outerproducts = outerproducts.minus(stats.outerproducts);
 n += stats.n;
}

代码示例来源：origin: stackoverflow.com

import Jama.*

Matrix A = new Matrix(a);
Matrix B = new Matrix(b);
Matrix R = A.minus(B);

代码示例来源：origin: openimaj/openimaj

private static float distance(Matrix a,Matrix b) {
    return (float) a.minus(b).norm2();
  }
}

代码示例来源：origin: org.openimaj/image-local-features

private static float distance(Matrix a,Matrix b) {
    return (float) a.minus(b).norm2();
  }
}

代码示例来源：origin: percyliang/fig

public void sub(double[] _x) { // Remove a data point
 Matrix x = new Matrix(_x, _x.length);
 // TODO : in place for efficiency
 sum = sum.minus(x); 
 outerproducts = outerproducts.minus(x.times(x.transpose()));
 n--;
}
public void sub(SuffStats _stats) { // Remove several data points

代码示例来源：origin: gov.nist.math/jama

/** Check norm of difference of Matrices. **/
private static void check(Matrix X, Matrix Y) {
 double eps = Math.pow(2.0,-52.0);
 if (X.norm1() == 0. & Y.norm1() < 10*eps) return;
 if (Y.norm1() == 0. & X.norm1() < 10*eps) return;
 if (X.minus(Y).norm1() > 1000*eps*Math.max(X.norm1(),Y.norm1())) {
   throw new RuntimeException("The norm of (X-Y) is too large: " +  Double.toString(X.minus(Y).norm1()));
 }
}

代码示例来源：origin: us.ihmc/ihmc-robotics-toolkit

public static Matrix subtractAverageColumnFromEachRow(Matrix m)
{
 Matrix u = getAverageColumnVector(m);
 Matrix h = createRowVector(m.getColumnDimension(), 1.0);
 Matrix ret = m.copy();
 // System.out.println("Stats::subtractAverageColumnFromEachRow: u,
 // h, u*h : ");
 // u.print(3, 3);
 // h.print(3, 3);
 // u.times(h).print(3, 3);
 return ret.minus(u.times(h));
}

代码示例来源：origin: us.ihmc/IHMCRoboticsToolkit

public static Matrix subtractAverageColumnFromEachRow(Matrix m)
{
 Matrix u = getAverageColumnVector(m);
 Matrix h = createRowVector(m.getColumnDimension(), 1.0);
 Matrix ret = m.copy();
 // System.out.println("Stats::subtractAverageColumnFromEachRow: u,
 // h, u*h : ");
 // u.print(3, 3);
 // h.print(3, 3);
 // u.times(h).print(3, 3);
 return ret.minus(u.times(h));
}

代码示例来源：origin: org.ujmp/ujmp-jama

public Matrix minus(Matrix m) {
  if (m instanceof JamaDenseDoubleMatrix2D) {
    Matrix result = new JamaDenseDoubleMatrix2D(matrix.minus(((JamaDenseDoubleMatrix2D) m).matrix));
    MapMatrix<String, Object> a = getMetaData();
    if (a != null) {
      result.setMetaData(a.clone());
    }
    return result;
  } else {
    return super.minus(m);
  }
}

代码示例来源：origin: ujmp/universal-java-matrix-package

public Matrix minus(Matrix m) {
  if (m instanceof JamaDenseDoubleMatrix2D) {
    Matrix result = new JamaDenseDoubleMatrix2D(matrix.minus(((JamaDenseDoubleMatrix2D) m).matrix));
    MapMatrix<String, Object> a = getMetaData();
    if (a != null) {
      result.setMetaData(a.clone());
    }
    return result;
  } else {
    return super.minus(m);
  }
}

代码示例来源：origin: senbox-org/s1tbx

private double computeMLD(final double[] u, final ClusterInfo cluster) {
  Matrix uMat = new Matrix(u, u.length);
  Matrix uMatNew = uMat.minus(new Matrix(cluster.center, cluster.center.length));
  return uMatNew.transpose().times(cluster.invCov).times(uMatNew).get(0, 0) + cluster.logDet;
}

代码示例来源：origin: openimaj/openimaj

@Override
public Double aggregate(DoubleSynchronisedTimeSeriesCollection series) {
  Matrix squarediffs = null;
  for (DoubleTimeSeries ds: series.allseries()) {
    if(squarediffs == null){
      squarediffs = new Matrix(new double[][]{ds.getData()});
    }
    else{
      squarediffs = squarediffs.minus(new Matrix(new double[][]{ds.getData()}));
      squarediffs = squarediffs.arrayTimes(squarediffs );
    }
  }
  return MatrixUtils.sum(squarediffs);
}

代码示例来源：origin: openimaj/openimaj

@Override
public Double aggregate(DoubleSynchronisedTimeSeriesCollection series) {
  Matrix squarediffs = null;
  int size = 0;
  for (DoubleTimeSeries ds: series.allseries()) {
    if(squarediffs == null){
      squarediffs = new Matrix(new double[][]{ds.getData()});
    }
    else{
      squarediffs = squarediffs.minus(new Matrix(new double[][]{ds.getData()}));
      squarediffs = squarediffs.arrayTimes(squarediffs );
    }
    size = ds.size();
  }
  return MatrixUtils.sum(squarediffs)/size;
}

代码示例来源：origin: openimaj/openimaj

@Override
public double compare(MultivariateGaussian o1, MultivariateGaussian o2) {
  final Matrix sig0 = o1.getCovariance();
  final Matrix sig1 = o2.getCovariance();
  final Matrix mu0 = o1.getMean();
  final Matrix mu1 = o2.getMean();
  final int K = o1.numDims();
  final Matrix sig1inv = sig1.inverse();
  final double sigtrace = MatrixUtils.trace(sig1inv.times(sig0));
  final Matrix mudiff = mu1.minus(mu0);
  final double xt_s_x = mudiff.transpose().times(sig1inv).times(mudiff).get(0, 0);
  final double ln_norm_sig = Math.log(sig0.norm1() / sig1.norm1());
  return 0.5 * (sigtrace + xt_s_x - K - ln_norm_sig);
}

代码示例来源：origin: bcdev/beam

public void testUnconstrainedUnmixing() throws IOException {
  SpectralUnmixing mlm = new UnconstrainedLSU(endmembers.getArray());
  Matrix abundUnconstrBeam = new Matrix(mlm.unmix(spectra.getArray()));
  Matrix abundUnconstrEnvi = Matrix.read(getResourceReader("abundances-unconstr-envi.csv"));
  Matrix abundUnconstrExpected = Matrix.read(getResourceReader("abundances-unconstr-expected.csv"));
  assertEquals("Difference of abundances (BEAM minus ENVI, unconstrained)",
      0.0,
      maxAbs(abundUnconstrBeam.minus(abundUnconstrEnvi)),
      1e-4);
  assertEquals("Difference of abundances (BEAM minus EXPECTED, unconstrained)",
      0.0,
      maxAbs(abundUnconstrBeam.minus(abundUnconstrExpected)),
      1e-7);
}

代码示例来源：origin: openimaj/openimaj

@Override
  protected void mstep(EMGMM gmm, GaussianMixtureModelEM learner, Matrix X, Matrix responsibilities,
      Matrix weightedXsum,
      double[] norm)
{
    final Matrix avgX2uw = responsibilities.transpose().times(X.arrayTimes(X));
    for (int i = 0; i < gmm.gaussians.length; i++) {
      final Matrix weightedXsumi = new Matrix(new double[][] { weightedXsum.getArray()[i] });
      final Matrix avgX2uwi = new Matrix(new double[][] { avgX2uw.getArray()[i] });
      final Matrix avgX2 = avgX2uwi.times(norm[i]);
      final Matrix mu = ((AbstractMultivariateGaussian) gmm.gaussians[i]).mean;
      final Matrix avgMeans2 = MatrixUtils.pow(mu, 2);
      final Matrix avgXmeans = mu.arrayTimes(weightedXsumi).times(norm[i]);
      final Matrix covar = MatrixUtils.plus(avgX2.minus(avgXmeans.times(2)).plus(avgMeans2),
          learner.minCovar);
      ((DiagonalMultivariateGaussian) gmm.gaussians[i]).variance = covar.getArray()[0];
    }
  }
},

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