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

Matrix.timesEquals介绍

暂无

代码示例

代码示例来源：origin: openimaj/openimaj

/**
 * Multiply all elements by a constant.
 * 
 * @param d
 *            the multiplication factor.
 */
public void applyMultiplication(double d) {
  matrix.timesEquals(d);
}

代码示例来源：origin: openimaj/openimaj

/**
 * Zero the matrix
 *
 * @param m
 *            the matrix
 */
public static void zero(Matrix m) {
  m.timesEquals(0);
}

代码示例来源：origin: openimaj/openimaj

/**
 * @param m new data to update the SVD with
 * @param dampening each current eigen vector is weighted by this amount
 */
public void update(Matrix m, double dampening){
  Matrix d = Matrix.identity(this.updateK, this.updateK);
  update(m,d.timesEquals(dampening));
}
/**

代码示例来源：origin: openimaj/openimaj

@Override
public Matrix getCovariance() {
  final int d = mean.getColumnDimension();
  return Matrix.identity(d, d).timesEquals(variance);
}

代码示例来源：origin: org.openimaj/sandbox

/**
 * @param m new data to update the SVD with
 * @param dampening each current eigen vector is weighted by this amount
 */
public void update(Matrix m, double dampening){
  Matrix d = Matrix.identity(this.updateK, this.updateK);
  update(m,d.timesEquals(dampening));
}
/**

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

public void precomputeDenominatorForVariationalBayes( final double sumHyperParameterLambda ) {
  // Variational Bayes calculations from Bishop
  precomputeInverse();
  cachedSigmaInverse.timesEquals( hyperParameter_a );
  double sum = 0.0;
  for(int jjj = 1; jjj <= mu.length; jjj++) {
    sum += Gamma.digamma( (hyperParameter_a + 1.0 - jjj) / 2.0 );
  }
  sum -= Math.log( sigma.det() );
  sum += Math.log(2.0) * mu.length;
  final double lambda = 0.5 * sum;
  final double pi = Gamma.digamma( hyperParameter_lambda ) - Gamma.digamma( sumHyperParameterLambda );
  final double beta = (-1.0 * mu.length) / (2.0 * hyperParameter_b);
  cachedDenomLog10 = (pi / Math.log(10.0)) + (lambda / Math.log(10.0)) + (beta / Math.log(10.0));
}

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: net.sf.meka/meka

/**
 * InitWeights - Initialize a BPNN of H.length hidden layers with H[0], H[1], etc hidden units in each layer (W will be random, and of the corresponding dimensions).
 * @param    d    number of visible units
 * @param    L    number of labels (output units)
 * @param    H    number of units in hidden layers, H.length = number of hidden layers. CURRENTLY LIMITED TO 1.
 */
public void initWeights(int d, int L, int H[]) throws Exception {
  int numHidden = H.length;
  if (getDebug()) {
    System.out.println("Initializing "+(H.length)+" hidden Layers ...");
    System.out.println("d = "+d);
    System.out.println("L = "+L);
  }
  // We need weights for Z to Y, as well as from X to Z
  Matrix W[] = new Matrix[H.length+1];
  int h = H[0];
  H = new int[]{d,h,L};
  // Hidden layers 
  System.out.println(""+Arrays.toString(H));
  for(int n = 0; n < H.length-1; n++) {
    W[n] = MatrixUtils.randomn(H[n] + 1, H[n + 1], r).timesEquals(0.1);
    if (getDebug()) System.out.println("W["+n+"] = "+(H[n]+1)+" x "+H[n+1]);
  }
  //setWeights(W, L); 
  this.W = W;
  makeMomentumMatrices();
}

代码示例来源：origin: Waikato/meka

/**
 * Preset Weights - Initialize a BPNN with (pre-trained) weight matrices W (which also determines X dimensions).
 * @param    W    pre-trained weight matrix (should include bias weights, assume W[-1]-1 hidden units in penultimate layer not including bias])
 * @param    L    the number of labels (for making the final matrix)
 */
public void presetWeights(Matrix W[], int L) throws Exception {
  r = new Random(0);
  this.W = new Matrix[W.length+1];
  for(int l = 0; l < W.length; l++) {
    this.W[l] = W[l];
  }
  int h = W[1].getRowDimension()-1;
  this.W[W.length] = MatrixUtils.randomn(h + 1, L, r).timesEquals(0.1);
  makeMomentumMatrices();
}

代码示例来源：origin: net.sf.meka/meka

/**
 * Preset Weights - Initialize a BPNN with (pre-trained) weight matrices W (which also determines X dimensions).
 * @param    W    pre-trained weight matrix (should include bias weights, assume W[-1]-1 hidden units in penultimate layer not including bias])
 * @param    L    the number of labels (for making the final matrix)
 */
public void presetWeights(Matrix W[], int L) throws Exception {
  r = new Random(0);
  this.W = new Matrix[W.length+1];
  for(int l = 0; l < W.length; l++) {
    this.W[l] = W[l];
  }
  int h = W[1].getRowDimension()-1;
  this.W[W.length] = MatrixUtils.randomn(h + 1, L, r).timesEquals(0.1);
  makeMomentumMatrices();
}

代码示例来源：origin: Waikato/meka

/**
 * InitWeights - Initialize a BPNN of H.length hidden layers with H[0], H[1], etc hidden units in each layer (W will be random, and of the corresponding dimensions).
 * @param    d    number of visible units
 * @param    L    number of labels (output units)
 * @param    H    number of units in hidden layers, H.length = number of hidden layers. CURRENTLY LIMITED TO 1.
 */
public void initWeights(int d, int L, int H[]) throws Exception {
  int numHidden = H.length;
  if (getDebug()) {
    System.out.println("Initializing "+(H.length)+" hidden Layers ...");
    System.out.println("d = "+d);
    System.out.println("L = "+L);
  }
  // We need weights for Z to Y, as well as from X to Z
  Matrix W[] = new Matrix[H.length+1];
  int h = H[0];
  H = new int[]{d,h,L};
  // Hidden layers 
  System.out.println(""+Arrays.toString(H));
  for(int n = 0; n < H.length-1; n++) {
    W[n] = MatrixUtils.randomn(H[n] + 1, H[n + 1], r).timesEquals(0.1);
    if (getDebug()) System.out.println("W["+n+"] = "+(H[n]+1)+" x "+H[n+1]);
  }
  //setWeights(W, L); 
  this.W = W;
  makeMomentumMatrices();
}

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: 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: senbox-org/s1tbx

CrMat.timesEquals(1.0 / num);
CiMat.timesEquals(1.0 / num);
for (int i = 0; i < 4; i++) {
  Cr[i][0] = CrMat.get(i, 0);

代码示例来源：origin: senbox-org/s1tbx

CrMat.timesEquals(1.0 / num);
CiMat.timesEquals(1.0 / num);

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: senbox-org/s1tbx

CrMat.timesEquals(1.0 / num);
CiMat.timesEquals(1.0 / num);

代码示例来源：origin: openimaj/openimaj

final Matrix m = cv.plus(Matrix.identity(ndims, ndims).timesEquals(MIN_COVAR_RECONDITION));
cv_chol = m.chol().getL();

代码示例来源：origin: senbox-org/s1tbx

TrMat.timesEquals(1.0 / num);
TiMat.timesEquals(1.0 / num);
for (int i = 0; i < 3; i++) {
  for (int j = 0; j < 3; j++) {

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