本文整理了Java中gov.sandia.cognition.math.matrix.Vector.scale()方法的一些代码示例，展示了Vector.scale()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。Vector.scale()方法的具体详情如下：
包路径：gov.sandia.cognition.math.matrix.Vector
类名称：Vector
方法名：scale

Vector.scale介绍

暂无

代码示例

代码示例来源：origin: gov.sandia.foundry/gov-sandia-cognition-learning-core

@Override
public Vector getMean()
{
  return this.parameters.scale(1.0 / this.parameters.norm1());
}

代码示例来源：origin: algorithmfoundry/Foundry

@Override
public Vector getMean()
{
  return this.parameters.scale(1.0 / this.parameters.norm1());
}

代码示例来源：origin: algorithmfoundry/Foundry

@Override
public Vector getMean()
{
  return this.parameters.scale( this.numTrials/this.parameters.norm1() );
}

代码示例来源：origin: gov.sandia.foundry/gov-sandia-cognition-learning-core

@Override
public Vector getMean()
{
  return this.parameters.scale( this.numTrials / this.parameters.norm1() );
}

代码示例来源：origin: gov.sandia.foundry/gov-sandia-cognition-learning-core

@Override
public Vector getMean()
{
  return this.parameters.scale( this.parameters.norm1() );
}

代码示例来源：origin: gov.sandia.foundry/gov-sandia-cognition-learning-core

public Vector evaluate(
  Vector input)
{
  return input.scale( this.getScaleFactor() );
}

代码示例来源：origin: gov.sandia.foundry/gov-sandia-cognition-learning-core

@Override
public Vector getMean()
{
  return this.parameters.scale( this.numTrials/this.parameters.norm1() );
}

代码示例来源：origin: algorithmfoundry/Foundry

public Vector evaluate(
  Vector input)
{
  return input.scale( this.getScaleFactor() );
}

代码示例来源：origin: algorithmfoundry/Foundry

@Override
public Vector getMean()
{
  return this.parameters.scale( this.numTrials/this.parameters.norm1() );
}

代码示例来源：origin: algorithmfoundry/Foundry

@Override
public Vector getMean()
{
  return this.parameters.scale( this.numTrials / this.parameters.norm1() );
}

代码示例来源：origin: algorithmfoundry/Foundry

@Override
public Vector getMean()
{
  return this.parameters.scale( this.parameters.norm1() );
}

代码示例来源：origin: algorithmfoundry/Foundry

@Override
public Vector getMean()
{
  return this.parameters.scale( this.numTrials / this.parameters.norm1() );
}

代码示例来源：origin: algorithmfoundry/Foundry

@Override
public Vector getMean()
{
  return this.parameters.scale( this.parameters.norm1() );
}

代码示例来源：origin: algorithmfoundry/Foundry

public Vector evaluate(
  Vector input)
{
  return input.scale( this.getScaleFactor() );
}

代码示例来源：origin: algorithmfoundry/Foundry

@Override
public Vector getMean()
{
  return this.parameters.scale(1.0 / this.parameters.norm1());
}

代码示例来源：origin: gov.sandia.foundry/gov-sandia-cognition-learning-core

@Override
public Vector getMean()
{
  RingAccumulator<Vector> mean =
    new RingAccumulator<Vector>();
  final int K = this.getDistributionCount();
  for( int k = 0; k < K; k++ )
  {
    mean.accumulate( this.getDistributions().get(k).getMean().scale(
      this.getPriorWeights()[k] ) );
  }
  return mean.getSum().scale( 1.0 / this.getPriorWeightSum() );
}

代码示例来源：origin: gov.sandia.foundry/gov-sandia-cognition-learning-core

@Override
protected boolean initializeAlgorithm()
{
  this.result = new DefaultInputOutputPair<Vector, Double>(
    this.initialGuess, this.data.evaluate( this.initialGuess ) );
  this.gradient = this.data.differentiate( this.initialGuess );
  
  this.lineFunction = new DirectionalVectorToDifferentiableScalarFunction(
    this.data, this.initialGuess, this.gradient.scale(-1.0) );
  
  return true;
}

代码示例来源：origin: algorithmfoundry/Foundry

@Override
protected boolean initializeAlgorithm()
{
  this.result = new DefaultInputOutputPair<Vector, Double>(
    this.initialGuess, this.data.evaluate( this.initialGuess ) );
  this.gradient = this.data.differentiate( this.initialGuess );
  
  this.lineFunction = new DirectionalVectorToDifferentiableScalarFunction(
    this.data, this.initialGuess, this.gradient.scale(-1.0) );
  
  return true;
}

代码示例来源：origin: algorithmfoundry/Foundry

@Override
protected boolean initializeAlgorithm()
{
  this.result = new DefaultInputOutputPair<Vector, Double>(
    this.initialGuess, this.data.evaluate( this.initialGuess ) );
  this.gradient = this.data.differentiate( this.initialGuess );
  
  this.lineFunction = new DirectionalVectorToDifferentiableScalarFunction(
    this.data, this.initialGuess, this.gradient.scale(-1.0) );
  
  return true;
}

代码示例来源：origin: openimaj/openimaj

@Override
public Matrix init(int rows, int cols) {
  Matrix currentValues = getCurrentValues();
  Vector mean = currentValues.sumOfRows().scale(1f/currentValues.getNumRows());
  Matrix m = DenseMatrixFactoryMTJ.INSTANCE.createMatrix(rows, cols);
  for (int r = 0; r < m.getNumRows(); r++) {
    m.setRow(r, mean);
  }
  return m;
}