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

Matrix.plusEquals介绍

暂无

代码示例

代码示例来源：origin: openimaj/openimaj

private void updateMean(Matrix point) {
  Matrix newMean = mean.times(DAMPENING);
  newMean.plusEquals(point.times(1 - DAMPENING));
  mean = newMean;
}
private void redrawEllipses() {

代码示例来源：origin: org.openimaj/sandbox

private void updateMean(Matrix point) {
  Matrix newMean = mean.times(DAMPENING);
  newMean.plusEquals(point.times(1 - DAMPENING));
  mean = newMean;
}
private void redrawEllipses() {

代码示例来源：origin: cmu-phil/tetrad

/**
 * Compute compute test set covariances
 * @param loghyper column <code>Matrix</code> of hyperparameters
 * @param X  input dataset
 * @param Xstar  test set
 * @return  [K(Xstar,Xstar) K(X,Xstar)]
 */
public Matrix[] compute(Matrix loghyper, Matrix X, Matrix Xstar){
  Matrix A = new Matrix(Xstar.getRowDimension(),1);
  Matrix B = new Matrix(X.getRowDimension(),Xstar.getRowDimension());
  for(int i=0; i<f.length; i++){
    Matrix loghyperi = loghyper.getMatrix(idx[i],idx[i+1]-1,0,0);
    Matrix[] K = f[i].compute(loghyperi,X,Xstar);
    A.plusEquals(K[0]);
    B.plusEquals(K[1]);
  }
  return new Matrix[]{A,B};
}

代码示例来源：origin: cmu-phil/tetrad

/**
 * Sums across the rows of the <code>Matrix</code> and return the result as a single column <code>MAtrix</code>
 * @param A  input <code>Matrix</code>
 * @return result
 */
public static Matrix sumRows(Matrix A){
  Matrix sum = new Matrix(A.getRowDimension(),1);
  for(int i=0; i<A.getColumnDimension(); i++)
    sum.plusEquals(A.getMatrix(0,A.getRowDimension()-1,i,i));
  return sum;
}

代码示例来源：origin: cmu-phil/tetrad

private static Matrix sumColumns(Matrix a){
  Matrix sum = new Matrix(1,a.getColumnDimension());
  for(int i=0; i<a.getRowDimension(); i++)
    sum.plusEquals(a.getMatrix(i,i,0,a.getColumnDimension()-1));
  return sum;
}

代码示例来源：origin: cmu-phil/tetrad

/**
 * Compute covariance matrix of a dataset X
 * @param loghyper column <code>Matrix</code> of hyperparameters
 * @param X  input dataset
 * @return  K covariance <code>Matrix</code>
 */
public Matrix compute(Matrix loghyper, Matrix X){
  Matrix K = new Matrix(X.getRowDimension(),X.getRowDimension());
  for(int i=0; i<f.length; i++){
    Matrix loghyperi = loghyper.getMatrix(idx[i],idx[i+1]-1,0,0);
    K.plusEquals(f[i].compute(loghyperi,X));
  }
  return K;
}

代码示例来源：origin: broadgsa/gatk-protected

sigma.plusEquals( pVarSigma );
sigma.plusEquals( empiricalSigma );
sigma.plusEquals( wishart );

代码示例来源：origin: net.sf.meka/meka

/**
 * Update - Carry out one epoch of CD, update W.
 * We use dW_ to manage momentum.
 * <br>
 * TODO weight decay SHOULD NOT BE APPLIED TO BIASES
 * @param    X     X
 */
public void update(Matrix X) {
  Matrix CD = epoch(X);
  Matrix dW = (CD.minusEquals(this.W.times(COST))).timesEquals(LEARNING_RATE); 	// with COST
  this.W.plusEquals(dW.plus(this.dW_.timesEquals(MOMENTUM)));                  	// with MOMENTUM.
  this.dW_ = dW;																	// for the next update
}

代码示例来源：origin: Waikato/meka

/**
 * Update - Carry out one epoch of CD, update W.
 * We use dW_ to manage momentum.
 * <br>
 * TODO weight decay SHOULD NOT BE APPLIED TO BIASES
 * @param    X     X
 */
public void update(Matrix X) {
  Matrix CD = epoch(X);
  Matrix dW = (CD.minusEquals(this.W.times(COST))).timesEquals(LEARNING_RATE); 	// with COST
  this.W.plusEquals(dW.plus(this.dW_.timesEquals(MOMENTUM)));                  	// with MOMENTUM.
  this.dW_ = dW;																	// for the next update
}

代码示例来源：origin: Waikato/meka

/**
 * Update - Carry out one epoch of CD, update W.
 * <br>
 * TODO    combine with above fn.
 * @param    X     X
 * @param    s    multiply the gradient by this scalar
 */
public void update(Matrix X, double s) {
  Matrix CD = epoch(X);
  Matrix dW = (CD.minusEquals(this.W.times(COST))).timesEquals(LEARNING_RATE); 	// with COST
  dW = dW.times(s); // *scaling factor
  this.W.plusEquals(dW.plus(this.dW_.timesEquals(MOMENTUM)));                  	// with MOMENTUM.
  this.dW_ = dW;																	// for the next update
}

代码示例来源：origin: net.sf.meka/meka

/**
 * Update - Carry out one epoch of CD, update W.
 * <br>
 * TODO    combine with above fn.
 * @param    X     X
 * @param    s    multiply the gradient by this scalar
 */
public void update(Matrix X, double s) {
  Matrix CD = epoch(X);
  Matrix dW = (CD.minusEquals(this.W.times(COST))).timesEquals(LEARNING_RATE); 	// with COST
  dW = dW.times(s); // *scaling factor
  this.W.plusEquals(dW.plus(this.dW_.timesEquals(MOMENTUM)));                  	// with MOMENTUM.
  this.dW_ = dW;																	// for the next update
}

代码示例来源：origin: senbox-org/s1tbx

for (int xx = xSt; xx <= xEd; ++xx) {
  getCovarianceMatrixC3(srcIndex.getIndex(xx), dataBuffers, tempCr, tempCi);
  CrMat.plusEquals(new Matrix(tempCr));
  CiMat.plusEquals(new Matrix(tempCi));
  getCoherencyMatrixT3(srcIndex.getIndex(xx), dataBuffers, tempTr, tempTi);
  t3ToC3(tempTr, tempTi, tempCr, tempCi);
  CrMat.plusEquals(new Matrix(tempCr));
  CiMat.plusEquals(new Matrix(tempCi));
  getComplexScatterMatrix(srcIndex.getIndex(xx), dataBuffers, tempSr, tempSi);
  computeCovarianceMatrixC3(tempSr, tempSi, tempCr, tempCi);
  CrMat.plusEquals(new Matrix(tempCr));
  CiMat.plusEquals(new Matrix(tempCi));

代码示例来源：origin: senbox-org/s1tbx

for (int xx = xSt; xx <= xEd; ++xx) {
  getCoherencyMatrixT3(srcIndex.getIndex(xx), dataBuffers, tempTr, tempTi);
  TrMat.plusEquals(new Matrix(tempTr));
  TiMat.plusEquals(new Matrix(tempTi));
  getCovarianceMatrixC3(srcIndex.getIndex(xx), dataBuffers, tempCr, tempCi);
  c3ToT3(tempCr, tempCi, tempTr, tempTi);
  TrMat.plusEquals(new Matrix(tempTr));
  TiMat.plusEquals(new Matrix(tempTi));
  getComplexScatterMatrix(srcIndex.getIndex(xx), dataBuffers, tempSr, tempSi);
  computeCoherencyMatrixT3(tempSr, tempSi, tempTr, tempTi);
  TrMat.plusEquals(new Matrix(tempTr));
  TiMat.plusEquals(new Matrix(tempTi));

代码示例来源：origin: percyliang/fig

public double [] sample(Random random)
{
 Matrix L = getChol().getL();
 // start with a vector of iid std normals
 Matrix stdNormal = new Matrix(dim(), 1);
 for (int i = 0; i < dim(); i++)
 {
  stdNormal.set(i, 0, SampleUtils.sampleGaussian(random));
 }
 Matrix result = L.times(stdNormal);
 result.plusEquals(mean);
 return result.getColumnPackedCopy();
}
public double[] sampleObject(Random random) { return sample(random); }

代码示例来源：origin: senbox-org/s1tbx

for (int xx = xSt; xx <= xEd; ++xx) {
  getCovarianceMatrixC4(srcIndex.getIndex(xx), sourceProductType, dataBuffers, tempCr, tempCi);
  CrMat.plusEquals(new Matrix(tempCr));
  CiMat.plusEquals(new Matrix(tempCi));

代码示例来源：origin: senbox-org/s1tbx

CrMat.plusEquals(tmpCrMat);
CiMat.plusEquals(tmpCiMat);

代码示例来源：origin: broadgsa/gatk-protected

public void evaluateFinalModelParameters( final List<VariantDatum> data ) {
  sumProb = 0.0;
  zeroOutMu();
  zeroOutSigma();
  int datumIndex = 0;
  for( final VariantDatum datum : data ) {
    final double prob = pVarInGaussian.get(datumIndex++);
    sumProb += prob;
    incrementMu( datum, prob );
  }
  divideEqualsMu( sumProb );
  datumIndex = 0;
  final Matrix pVarSigma = new Matrix(mu.length, mu.length);
  for( final VariantDatum datum : data ) {
    final double prob = pVarInGaussian.get(datumIndex++);
    for( int iii = 0; iii < mu.length; iii++ ) {
      for( int jjj = 0; jjj < mu.length; jjj++ ) {
        pVarSigma.set(iii, jjj, prob * (datum.annotations[iii]-mu[iii]) * (datum.annotations[jjj]-mu[jjj]));
      }
    }
    sigma.plusEquals( pVarSigma );
  }
  sigma.timesEquals( 1.0 / sumProb );
  resetPVarInGaussian(); // clean up some memory
}

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)
{
    // Eq. 12 from K. Murphy,
    // "Fitting a Conditional Linear Gaussian Distribution"
    final int nfeatures = X.getColumnDimension();
    for (int c = 0; c < learner.nComponents; c++) {
      final Matrix post = responsibilities.getMatrix(0, X.getRowDimension() - 1, c, c).transpose();
      final double factor = 1.0 / (ArrayUtils.sumValues(post.getArray()) + 10 * MathUtils.EPSILON);
      final Matrix pXt = X.transpose();
      for (int i = 0; i < pXt.getRowDimension(); i++)
        for (int j = 0; j < pXt.getColumnDimension(); j++)
          pXt.set(i, j, pXt.get(i, j) * post.get(0, j));
      final Matrix argcv = pXt.times(X).times(factor);
      final Matrix mu = ((FullMultivariateGaussian) gmm.gaussians[c]).mean;
      ((FullMultivariateGaussian) gmm.gaussians[c]).covar = argcv.minusEquals(mu.transpose().times(mu))
          .plusEquals(Matrix.identity(nfeatures, nfeatures).times(learner.minCovar));
    }
  }
},

代码示例来源：origin: Waikato/meka

W[i].plusEquals(dW[i]);

代码示例来源：origin: net.sf.meka/meka

W[i].plusEquals(dW[i]);

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