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

Vector.plusEquals介绍

暂无

代码示例

代码示例来源：origin: algorithmfoundry/Foundry

@Override
public Vector evaluate(
  Vector input)
{
  Vector discriminant = super.evaluate( input );
  discriminant.plusEquals(this.bias);
  return discriminant;
}

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 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-learning-core

@Override
public Vector evaluate(
  Vector input)
{
  Vector discriminant = super.evaluate( input );
  discriminant.plusEquals(this.bias);
  return discriminant;
}

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

@Override
public Vector evaluate(
  Vector input)
{
  Vector discriminant = super.evaluate( input );
  discriminant.plusEquals(this.bias);
  return discriminant;
}

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

/**
 * Computes the raw (unsquashed) activation at the hidden layer for the
 * given input.
 * @param input
 * Input to compute the raw hidden activation of.
 * @return
 * Raw (unsquashed) activation at the hidden layer.
 */
protected Vector evaluateHiddenLayerActivation(
  Vector input )
{
  Vector hiddenActivation = this.inputToHiddenWeights.times( input );
  hiddenActivation.plusEquals( this.inputToHiddenBiasWeights );
  return hiddenActivation;
}

代码示例来源：origin: algorithmfoundry/Foundry

/**
 * Computes the raw (unsquashed) activation at the hidden layer for the
 * given input.
 * @param input
 * Input to compute the raw hidden activation of.
 * @return
 * Raw (unsquashed) activation at the hidden layer.
 */
protected Vector evaluateHiddenLayerActivation(
  Vector input )
{
  Vector hiddenActivation = this.inputToHiddenWeights.times( input );
  hiddenActivation.plusEquals( this.inputToHiddenBiasWeights );
  return hiddenActivation;
}

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

/**
 * Evaluates the output from the squashed hidden-layer activation.
 * @param squashedHiddenActivation
 * Squashed hidden-layer activation.
 * @return
 * Output of the neural net.
 */
protected Vector evaluateOutputFromSquashedHiddenLayerActivation(
  Vector squashedHiddenActivation )
{
  Vector outputActivation = this.hiddenToOutputWeights.times(
    squashedHiddenActivation );
  outputActivation.plusEquals( this.hiddenToOutputBiasWeights );
  return outputActivation;
}

代码示例来源：origin: algorithmfoundry/Foundry

/**
 * Evaluates the output from the squashed hidden-layer activation.
 * @param squashedHiddenActivation
 * Squashed hidden-layer activation.
 * @return
 * Output of the neural net.
 */
protected Vector evaluateOutputFromSquashedHiddenLayerActivation(
  Vector squashedHiddenActivation )
{
  Vector outputActivation = this.hiddenToOutputWeights.times(
    squashedHiddenActivation );
  outputActivation.plusEquals( this.hiddenToOutputBiasWeights );
  return outputActivation;
}

代码示例来源：origin: algorithmfoundry/Foundry

/**
 * Computes the raw (unsquashed) activation at the hidden layer for the
 * given input.
 * @param input
 * Input to compute the raw hidden activation of.
 * @return
 * Raw (unsquashed) activation at the hidden layer.
 */
protected Vector evaluateHiddenLayerActivation(
  Vector input )
{
  Vector hiddenActivation = this.inputToHiddenWeights.times( input );
  hiddenActivation.plusEquals( this.inputToHiddenBiasWeights );
  return hiddenActivation;
}

代码示例来源：origin: algorithmfoundry/Foundry

/**
 * Evaluates the output from the squashed hidden-layer activation.
 * @param squashedHiddenActivation
 * Squashed hidden-layer activation.
 * @return
 * Output of the neural net.
 */
protected Vector evaluateOutputFromSquashedHiddenLayerActivation(
  Vector squashedHiddenActivation )
{
  Vector outputActivation = this.hiddenToOutputWeights.times(
    squashedHiddenActivation );
  outputActivation.plusEquals( this.hiddenToOutputBiasWeights );
  return outputActivation;
}

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: 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: 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: gov.sandia.foundry/gov-sandia-cognition-common-core

public Vector evaluate(
  Vector input)
{
  Vector xnm1 = this.getState();
  Vector xn = A.times(xnm1);
  xn.plusEquals( B.times(input) );
  this.setState(xn);
  return C.times(xn);
}

代码示例来源：origin: algorithmfoundry/Foundry

public Vector evaluate(
  Vector input)
{
  Vector xnm1 = this.getState();
  Vector xn = A.times(xnm1);
  xn.plusEquals( B.times(input) );
  this.setState(xn);
  return C.times(xn);
}

代码示例来源：origin: algorithmfoundry/Foundry

public Vector evaluate(
  Vector input)
{
  Vector xnm1 = this.getState();
  Vector xn = A.times(xnm1);
  xn.plusEquals( B.times(input) );
  this.setState(xn);
  return C.times(xn);
}

代码示例来源：origin: algorithmfoundry/Foundry

@Override
final protected double iterate()
{
  Vector q = A.evaluate(residual);
  double alpha = delta / (residual.dotProduct(q));
  x.plusEquals(residual.scale(alpha));
  if (((iterationCounter + 1) % 50) == 0)
  {
    residual = rhs.minus(A.evaluate(x));
  }
  else
  {
    residual = residual.minus(q.scale(alpha));
  }
  delta = residual.dotProduct(residual);
  return delta;
}

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

@Override
final protected double iterate()
{
  Vector q = A.evaluate(residual);
  double alpha = delta / (residual.dotProduct(q));
  x.plusEquals(residual.scale(alpha));
  if (((iterationCounter + 1) % 50) == 0)
  {
    residual = rhs.minus(A.evaluate(x));
  }
  else
  {
    residual = residual.minus(q.scale(alpha));
  }
  delta = residual.dotProduct(residual);
  return delta;
}