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

Vector.clone介绍

暂无

代码示例

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

/**
 * Basic setter for the categorized vector.
 * @param newCategorizedVector 
 */
public void setCategorizedVector(Vector newCategorizedVector) {
  categorizedVector = newCategorizedVector.clone();
}

代码示例来源：origin: algorithmfoundry/Foundry

/**
 * Sets the initial guess ("x0")
 *
 * @param initialGuess the initial guess ("x0")
 */
@Override
final public void setInitialGuess(Vector initialGuess)
{
  x0 = initialGuess.clone();
}

代码示例来源：origin: algorithmfoundry/Foundry

@Override
public Vector convertToVector()
{
  return this.parameters.clone();
}

代码示例来源：origin: algorithmfoundry/Foundry

/**
 * Returns the initial guess at "x"
 *
 * @return the initial guess at "x"
 */
@Override
final public Vector getInitialGuess()
{
  return x0.clone();
}

代码示例来源：origin: algorithmfoundry/Foundry

/**
 * Basic setter for the test vector.
 * @param newTestVector 
 */
public void setTestVector(Vector newTestVector) {
  testingVector = newTestVector.clone();
}

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

/**
 * Sets the initial guess ("x0")
 *
 * @param initialGuess the initial guess ("x0")
 */
@Override
final public void setInitialGuess(Vector initialGuess)
{
  x0 = initialGuess.clone();
}

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

@Override
final public Vector plus(
  final Vector v)
{
  // I need to flip this so that if it the input is a dense vector, I
  // return a dense vector.  If it's a sparse vector, then a sparse vector
  // is still returned.
  Vector result = v.clone();
  result.plusEquals(this);
  return result;
}

代码示例来源：origin: algorithmfoundry/Foundry

@Override
final public Vector plus(
  final Vector v)
{
  // I need to flip this so that if it the input is a dense vector, I
  // return a dense vector.  If it's a sparse vector, then a sparse vector
  // is still returned.
  Vector result = v.clone();
  result.plusEquals(this);
  return result;
}

代码示例来源：origin: algorithmfoundry/Foundry

/**
 * {@inheritDoc}
 * @return {@inheritDoc}
 */
protected boolean initializeAlgorithm()
{
  this.previousDelta = null;
  this.result = new DefaultInputOutputPair<Vector, Double>(
    this.initialGuess.clone(), null );
  return true;
}

代码示例来源：origin: algorithmfoundry/Foundry

@Override
final public Vector plus(
  final Vector v)
{
  // I need to flip this so that if it the input is a dense vector, I
  // return a dense vector.  If it's a sparse vector, then a sparse vector
  // is still returned.
  Vector result = v.clone();
  result.plusEquals(this);
  return result;
}

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

@Override
final public Vector minus(
  final Vector v)
{
  // I need to flip this so that if it the input is a dense vector, I
  // return a dense vector.  If it's a sparse vector, then a sparse vector
  // is still returned.
  Vector result = v.clone();
  result.negativeEquals();
  result.plusEquals(this);
  return result;
}

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

/**
 * Copy Constructor
 * @param other VectorBasedCognitiveModelInput to clone
 */
public VectorBasedCognitiveModelInput(
  VectorBasedCognitiveModelInput other )
{
  this( other.getIdentifiers(), other.getValues().clone() );
}

代码示例来源：origin: algorithmfoundry/Foundry

@Override
final public Vector minus(
  final Vector v)
{
  // I need to flip this so that if it the input is a dense vector, I
  // return a dense vector.  If it's a sparse vector, then a sparse vector
  // is still returned.
  Vector result = v.clone();
  result.negativeEquals();
  result.plusEquals(this);
  return result;
}

代码示例来源：origin: algorithmfoundry/Foundry

@Override
public AutoRegressiveMovingAverageFilter clone()
{
  AutoRegressiveMovingAverageFilter clone =
    (AutoRegressiveMovingAverageFilter) super.clone();
  clone.setAutoregressiveCoefficients(
    this.getAutoRegressiveCoefficients().clone() );
  clone.setMovingAverageCoefficients(
    this.getMovingAverageCoefficients().clone() );
  return clone;
}

代码示例来源：origin: algorithmfoundry/Foundry

@Override
public MovingAverageFilter clone()
{
  MovingAverageFilter clone = (MovingAverageFilter) super.clone();
  clone.setMovingAverageCoefficients(
    this.getMovingAverageCoefficients().clone() );
  return clone;
}

代码示例来源：origin: algorithmfoundry/Foundry

@Override
public MovingAverageFilter clone()
{
  MovingAverageFilter clone = (MovingAverageFilter) super.clone();
  clone.setMovingAverageCoefficients(
    this.getMovingAverageCoefficients().clone() );
  return clone;
}

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

@Override
public MovingAverageFilter clone()
{
  MovingAverageFilter clone = (MovingAverageFilter) super.clone();
  clone.setMovingAverageCoefficients(
    this.getMovingAverageCoefficients().clone() );
  return clone;
}

代码示例来源：origin: algorithmfoundry/Foundry

public MultivariateGaussian createInitialLearnedObject()
{
  return new MultivariateGaussian(
    this.getMotionModel().getState().clone(),
    this.getModelCovariance() );
}

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

public MultivariateGaussian createPredictiveDistribution(
  MultivariateGaussian posterior)
{
  Vector mean = posterior.getMean().clone();
  Matrix C = posterior.getCovariance().plus(
    this.parameter.getConditionalDistribution().getCovariance() );
  return new MultivariateGaussian( mean, C );
}

代码示例来源：origin: algorithmfoundry/Foundry

public MultivariateGaussian createPredictiveDistribution(
  MultivariateGaussian posterior)
{
  Vector mean = posterior.getMean().clone();
  Matrix C = posterior.getCovariance().plus(
    this.parameter.getConditionalDistribution().getCovariance() );
  return new MultivariateGaussian( mean, C );
}