本文整理了Java中gov.sandia.cognition.math.matrix.Vector.norm2Squared()方法的一些代码示例，展示了Vector.norm2Squared()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.norm2Squared()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：norm2Squared

Vector.norm2Squared介绍

暂无

代码示例

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

protected double computeScaleFactor(
  Vector gradientCurrent,
  Vector gradientPrevious )
{
  double gradientTgradient = gradientCurrent.norm2Squared();
  double denom = gradientPrevious.norm2Squared();
  
  double beta = gradientTgradient / denom;
  return beta;
}

代码示例来源：origin: algorithmfoundry/Foundry

protected double computeScaleFactor(
  Vector gradientCurrent,
  Vector gradientPrevious )
{
  double gradientTgradient = gradientCurrent.norm2Squared();
  double denom = gradientPrevious.norm2Squared();
  
  double beta = gradientTgradient / denom;
  return beta;
}

代码示例来源：origin: algorithmfoundry/Foundry

protected double computeScaleFactor(
  Vector gradientCurrent,
  Vector gradientPrevious )
{
  double gradientTgradient = gradientCurrent.norm2Squared();
  double denom = gradientPrevious.norm2Squared();
  
  double beta = gradientTgradient / denom;
  return beta;
}

代码示例来源：origin: algorithmfoundry/Foundry

@Override
public double euclideanDistanceSquared(
  final Vector other )
{
  return this.minus( other ).norm2Squared();
}

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

@Override
public double euclideanDistanceSquared(
  final Vector other )
{
  return this.minus( other ).norm2Squared();
}

代码示例来源：origin: algorithmfoundry/Foundry

@Override
public double euclideanDistanceSquared(
  final Vector other )
{
  return this.minus( other ).norm2Squared();
}

代码示例来源：origin: algorithmfoundry/Foundry

@Override
protected double computeUpdate(
  final LinearBinaryCategorizer target,
  final Vector input,
  final boolean actualCategory,
  final double predicted)
{
  // Get the actual category.
  final double actual = actualCategory ? +1.0 : -1.0;
  // Compute the margin.
  final double margin = actual * predicted;
    final double norm = input.norm2Squared();
  return computeUpdate(margin, norm);
}

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

@Override
protected double computeUpdate(
  final LinearBinaryCategorizer target,
  final Vector input,
  final boolean actualCategory,
  final double predicted)
{
  // Get the actual category.
  final double actual = actualCategory ? +1.0 : -1.0;
  // Compute the margin.
  final double margin = actual * predicted;
    final double norm = input.norm2Squared();
  return computeUpdate(margin, norm);
}

代码示例来源：origin: algorithmfoundry/Foundry

@Override
protected double computeUpdate(
  final LinearBinaryCategorizer target,
  final Vector input,
  final boolean actualCategory,
  final double predicted)
{
  // Get the actual category.
  final double actual = actualCategory ? +1.0 : -1.0;
  // Compute the margin.
  final double margin = actual * predicted;
    final double norm = input.norm2Squared();
  return computeUpdate(margin, norm);
}

代码示例来源：origin: algorithmfoundry/Foundry

@Override
public double computeUpdate(
  final LinearBinaryCategorizer target,
  final Vector input,
  final boolean actualCategory,
  final double predicted)
{
  final double actual = actualCategory ? +1.0 : -1.0;
  final double margin = actual * predicted;
  final double hingeLoss = 1.0 - margin;
  if (Math.abs(margin) > 1.0)
  {
    // Passive when there is no loss.
    return 0.0;
  }
  else
  {
    // Update methods use ||x||^2.
    final double inputNorm2Squared = input.norm2Squared();
    // Compute the update value (tau).
    return this.computeUpdate(
      actual, predicted, hingeLoss, inputNorm2Squared);
  }
}

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

@Override
public double computeUpdate(
  final LinearBinaryCategorizer target,
  final Vector input,
  final boolean actualCategory,
  final double predicted)
{
  final double actual = actualCategory ? +1.0 : -1.0;
  final double loss = 1.0 - actual * predicted;
  if (loss <= 0.0)
  {
    // Passive when there is no loss.
    return 0.0;
  }
  else
  {
    // Update methods use ||x||^2.
    final double inputNorm2Squared = input.norm2Squared();
    // Compute the update value (tau).
    return this.computeUpdate(
      actual, predicted, loss, inputNorm2Squared);
  }
}

代码示例来源：origin: algorithmfoundry/Foundry

@Override
public double computeUpdate(
  final LinearBinaryCategorizer target,
  final Vector input,
  final boolean actualCategory,
  final double predicted)
{
  final double actual = actualCategory ? +1.0 : -1.0;
  final double loss = 1.0 - actual * predicted;
  if (loss <= 0.0)
  {
    // Passive when there is no loss.
    return 0.0;
  }
  else
  {
    // Update methods use ||x||^2.
    final double inputNorm2Squared = input.norm2Squared();
    // Compute the update value (tau).
    return this.computeUpdate(
      actual, predicted, loss, inputNorm2Squared);
  }
}

代码示例来源：origin: algorithmfoundry/Foundry

@Override
public double computeUpdate(
  final LinearBinaryCategorizer target,
  final Vector input,
  final boolean actualCategory,
  final double predicted)
{
  final double actual = actualCategory ? +1.0 : -1.0;
  final double loss = 1.0 - actual * predicted;
  if (loss <= 0.0)
  {
    // Passive when there is no loss.
    return 0.0;
  }
  else
  {
    // Update methods use ||x||^2.
    final double inputNorm2Squared = input.norm2Squared();
    // Compute the update value (tau).
    return this.computeUpdate(
      actual, predicted, loss, inputNorm2Squared);
  }
}

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

protected double computeScaleFactor(
  Vector gradientCurrent,
  Vector gradientPrevious )
{
  Vector deltaGradient = gradientCurrent.minus( gradientPrevious );
  double deltaTgradient = deltaGradient.dotProduct( gradientCurrent );
  double denom = gradientPrevious.norm2Squared();
  
  double beta = deltaTgradient / denom;
  return beta;
}

代码示例来源：origin: algorithmfoundry/Foundry

protected double computeScaleFactor(
  Vector gradientCurrent,
  Vector gradientPrevious )
{
  Vector deltaGradient = gradientCurrent.minus( gradientPrevious );
  double deltaTgradient = deltaGradient.dotProduct( gradientCurrent );
  double denom = gradientPrevious.norm2Squared();
  
  double beta = deltaTgradient / denom;
  return beta;
}

代码示例来源：origin: algorithmfoundry/Foundry

protected double computeScaleFactor(
  Vector gradientCurrent,
  Vector gradientPrevious )
{
  Vector deltaGradient = gradientCurrent.minus( gradientPrevious );
  double deltaTgradient = deltaGradient.dotProduct( gradientCurrent );
  double denom = gradientPrevious.norm2Squared();
  
  double beta = deltaTgradient / denom;
  return beta;
}

代码示例来源：origin: algorithmfoundry/Foundry

/**
 * Gets the regularization penalty term for the current result. It
 * computes the squared 2-norm of the parameters of the factorization
 * machine, each multiplied with their appropriate regularization weight.
 * 
 * @return 
 *      The regularization penalty term for the objective.
 */
public double getRegularizationPenalty()
{
  final double bias = this.result.getBias();
  double penalty = this.biasRegularization * bias * bias;
  
  if (this.result.hasWeights())
  {
    penalty += this.weightRegularization 
      * this.result.getWeights().norm2Squared();
  }
  
  if (this.result.hasFactors())
  {
    penalty += this.factorRegularization 
      * this.result.getFactors().normFrobeniusSquared();
  }
  
  return penalty;
}

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

/**
 * Gets the regularization penalty term in the error for the objective.
 * 
 * @return 
 *      The regularization penalty term.
 */
public double getRegularizationPenalty()
{
  if (this.result == null)
  {
    return 0.0;
  }
  
  double penalty = this.biasRegularization * this.result.getBias();
  
  if (this.result.hasWeights())
  {
    penalty += this.weightRegularization * this.result.getWeights().norm2Squared();
  }
  
  if (this.result.hasFactors())
  {
    penalty += this.factorRegularization * this.result.getFactors().normFrobeniusSquared();
  }
  
  return penalty;
}

代码示例来源：origin: algorithmfoundry/Foundry

/**
 * Gets the regularization penalty term in the error for the objective.
 * 
 * @return 
 *      The regularization penalty term.
 */
public double getRegularizationPenalty()
{
  if (this.result == null)
  {
    return 0.0;
  }
  
  double penalty = this.biasRegularization * this.result.getBias();
  
  if (this.result.hasWeights())
  {
    penalty += this.weightRegularization * this.result.getWeights().norm2Squared();
  }
  
  if (this.result.hasFactors())
  {
    penalty += this.factorRegularization * this.result.getFactors().normFrobeniusSquared();
  }
  
  return penalty;
}

代码示例来源：origin: algorithmfoundry/Foundry

/**
 * Gets the regularization penalty term in the error for the objective.
 * 
 * @return 
 *      The regularization penalty term.
 */
public double getRegularizationPenalty()
{
  if (this.result == null)
  {
    return 0.0;
  }
  
  double penalty = this.biasRegularization * this.result.getBias();
  
  if (this.result.hasWeights())
  {
    penalty += this.weightRegularization * this.result.getWeights().norm2Squared();
  }
  
  if (this.result.hasFactors())
  {
    penalty += this.factorRegularization * this.result.getFactors().normFrobeniusSquared();
  }
  
  return penalty;
}