本文整理了Java中gov.sandia.cognition.math.matrix.Vector.norm1()方法的一些代码示例，展示了Vector.norm1()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.norm1()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：norm1

Vector.norm1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: 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.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: 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

@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(1.0 / this.parameters.norm1());
}

代码示例来源：origin: algorithmfoundry/Foundry

public double computeEquivalentSampleSize(
  DirichletDistribution belief)
{
  Vector a = belief.getParameters();
  return a.norm1() / this.getNumTrials();
}

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

public Vector computeLocalWeights(
  final Vector counts)
{
  // Since the counts are positive, the 1-norm of them is their sum.
  final Vector result = this.vectorFactory.copyVector(counts);
  final double countSum = counts.norm1();
  if (countSum != 0.0)
  {
    result.scaleEquals(1.0 / countSum);
  }
  return result;
}

代码示例来源：origin: algorithmfoundry/Foundry

public double computeEquivalentSampleSize(
  DirichletDistribution belief)
{
  Vector a = belief.getParameters();
  return a.norm1() / this.getNumTrials();
}

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

public double computeEquivalentSampleSize(
  DirichletDistribution belief)
{
  Vector a = belief.getParameters();
  return a.norm1() / this.getNumTrials();
}

代码示例来源：origin: algorithmfoundry/Foundry

public Vector computeLocalWeights(
  final Vector counts)
{
  // Since the counts are positive, the 1-norm of them is their sum.
  final Vector result = this.vectorFactory.copyVector(counts);
  final double countSum = counts.norm1();
  if (countSum != 0.0)
  {
    result.scaleEquals(1.0 / countSum);
  }
  return result;
}

代码示例来源：origin: algorithmfoundry/Foundry

public Vector computeLocalWeights(
  final Vector counts)
{
  // Since the counts are positive, the 1-norm of them is their sum.
  final Vector result = this.vectorFactory.copyVector(counts);
  final double countSum = counts.norm1();
  if (countSum != 0.0)
  {
    result.scaleEquals(1.0 / countSum);
  }
  return result;
}

代码示例来源：origin: algorithmfoundry/Foundry

/**
   * Evaluates the Manhattan distance between the two given vectors.
   * 
   * @param  first The first Vector.
   * @param  second The second Vector.
   * @return The Manhattan distance between the two given vectors.
   */
  public double evaluate(
    final Vectorizable first,
    final Vectorizable second)
  {
    return first.convertToVector().minus(second.convertToVector()).norm1();
  }
}

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

/**
   * Evaluates the Manhattan distance between the two given vectors.
   * 
   * @param  first The first Vector.
   * @param  second The second Vector.
   * @return The Manhattan distance between the two given vectors.
   */
  public double evaluate(
    final Vectorizable first,
    final Vectorizable second)
  {
    return first.convertToVector().minus(second.convertToVector()).norm1();
  }
}