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

Vector.plus介绍

暂无

代码示例

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

/**
 * Transforms the scaleFactor into a multidimensional Vector using the
 * direction
 *
 * @param scaleFactor scale factor to move along the direction from
 *        vectorOffset
 * @return Multidimensional vector corresponding to the scale factor
 *         along the direction
 */
public Vector computeVector(
  double scaleFactor )
{
  return this.vectorOffset.plus(
    this.direction.scale( scaleFactor ) );
}

代码示例来源：origin: algorithmfoundry/Foundry

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

代码示例来源：origin: algorithmfoundry/Foundry

/**
 * Transforms the scaleFactor into a multidimensional Vector using the
 * direction
 *
 * @param scaleFactor scale factor to move along the direction from
 *        vectorOffset
 * @return Multidimensional vector corresponding to the scale factor
 *         along the direction
 */
public Vector computeVector(
  double scaleFactor )
{
  return this.vectorOffset.plus(
    this.direction.scale( scaleFactor ) );
}

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

@Override
final protected double iterate()
{
  // This code is _exactly_ the same as the standard CG code because
  // evaluate does the work of A^TAx, and A^Tb was calculated in init.
  Vector q = A.evaluate(d);
  double alpha = delta / (d.dotProduct(q));
  x.plusEquals(d.scale(alpha));
  if (((iterationCounter + 1) % 50) == 0)
  {
    residual = AtransB.minus(A.evaluate(x));
  }
  else
  {
    residual = residual.minus(q.scale(alpha));
  }
  double delta_old = delta;
  delta = residual.dotProduct(residual);
  double beta = delta / delta_old;
  d = residual.plus(d.scale(beta));
  return delta;
}

代码示例来源：origin: algorithmfoundry/Foundry

/**
 * Transforms the scaleFactor into a multidimensional Vector using the
 * direction
 *
 * @param scaleFactor scale factor to move along the direction from
 *        vectorOffset
 * @return Multidimensional vector corresponding to the scale factor
 *         along the direction
 */
public Vector computeVector(
  double scaleFactor )
{
  return this.vectorOffset.plus(
    this.direction.scale( scaleFactor ) );
}

代码示例来源：origin: algorithmfoundry/Foundry

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

代码示例来源：origin: algorithmfoundry/Foundry

public void update(
  DirichletDistribution belief,
  Vector value)
{
  Vector a = belief.getParameters();
  Vector anext = a.plus( value );
  belief.setParameters(anext);
}

代码示例来源：origin: algorithmfoundry/Foundry

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

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

public void update(
  DirichletDistribution belief,
  Vector value)
{
  Vector a = belief.getParameters();
  Vector anext = a.plus( value );
  belief.setParameters(anext);
}

代码示例来源：origin: algorithmfoundry/Foundry

public void update(
  DirichletDistribution belief,
  Vector value)
{
  Vector a = belief.getParameters();
  Vector anext = a.plus( value );
  belief.setParameters(anext);
}

代码示例来源：origin: algorithmfoundry/Foundry

/**
 * Overrides the default implementation so that L_tilde can be raised to a
 * power and the diagonal weights can be added implicitly (which is much
 * faster and memory efficient than the explicit representation).
 *
 * @param input The vector to multiply by the implicit represetation of the
 * matrix
 * @return The result of the function.
 */
@Override
public Vector evaluate(Vector input)
{
  Vector v = input;
  for (int i = 0; i < power; ++i)
  {
    v = m.times(v);
  }
  Vector plusV = additional.times(input);
  return v.plus(plusV);
}

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

/**
 * Overrides the default implementation so that L_tilde can be raised to a
 * power and the diagonal weights can be added implicitly (which is much
 * faster and memory efficient than the explicit representation).
 *
 * @param input The vector to multiply by the implicit represetation of the
 * matrix
 * @return The result of the function.
 */
@Override
public Vector evaluate(Vector input)
{
  Vector v = input;
  for (int i = 0; i < power; ++i)
  {
    v = m.times(v);
  }
  Vector plusV = additional.times(input);
  return v.plus(plusV);
}

代码示例来源：origin: algorithmfoundry/Foundry

/**
 * Overrides the default implementation so that L_tilde can be raised to a
 * power and the diagonal weights can be added implicitly (which is much
 * faster and memory efficient than the explicit representation).
 *
 * @param input The vector to multiply by the implicit represetation of the
 * matrix
 * @return The result of the function.
 */
@Override
public Vector evaluate(Vector input)
{
  Vector v = input;
  for (int i = 0; i < power; ++i)
  {
    v = m.times(v);
  }
  Vector plusV = additional.times(input);
  return v.plus(plusV);
}

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

/**
 * Convolves this Gaussian with the other Gaussian.
 *
 * @param other Other Gaussian to convolve with this.
 * @return Convolved Gaussians.
 */
public MultivariateGaussian convolve(
  MultivariateGaussian other)
{
  Vector meanHat = this.mean.plus(other.getMean());
  Matrix covarianceHat = this.getCovariance().plus(other.getCovariance());
  return new MultivariateGaussian(meanHat, covarianceHat);
}

代码示例来源：origin: algorithmfoundry/Foundry

/**
 * Convolves this Gaussian with the other Gaussian.
 *
 * @param other Other Gaussian to convolve with this.
 * @return Convolved Gaussians.
 */
public MultivariateGaussian convolve(
  MultivariateGaussian other)
{
  Vector meanHat = this.mean.plus(other.getMean());
  Matrix covarianceHat = this.getCovariance().plus(other.getCovariance());
  return new MultivariateGaussian(meanHat, covarianceHat);
}

代码示例来源：origin: algorithmfoundry/Foundry

/**
 * Convolves this Gaussian with the other Gaussian.
 *
 * @param other Other Gaussian to convolve with this.
 * @return Convolved Gaussians.
 */
public MultivariateGaussian convolve(
  MultivariateGaussian other)
{
  Vector meanHat = this.mean.plus(other.getMean());
  Matrix covarianceHat = this.getCovariance().plus(other.getCovariance());
  return new MultivariateGaussian(meanHat, covarianceHat);
}

代码示例来源：origin: algorithmfoundry/Foundry

/**
 * Adds two MultivariateGaussian random variables together and returns the
 * resulting MultivariateGaussian
 *
 * @param other MultivariateGaussian to add to this MultivariateGaussian
 * @return Effective addition of the two MultivariateGaussian random
 * variables
 */
public MultivariateGaussian plus(
  MultivariateGaussian other)
{
  Vector m = this.mean.plus(other.getMean());
  Matrix C = this.getCovariance().plus(other.getCovariance());
  return new MultivariateGaussian(m, C);
}

代码示例来源：origin: algorithmfoundry/Foundry

/**
 * Adds two MultivariateGaussian random variables together and returns the
 * resulting MultivariateGaussian
 *
 * @param other MultivariateGaussian to add to this MultivariateGaussian
 * @return Effective addition of the two MultivariateGaussian random
 * variables
 */
public MultivariateGaussian plus(
  MultivariateGaussian other)
{
  Vector m = this.mean.plus(other.getMean());
  Matrix C = this.getCovariance().plus(other.getCovariance());
  return new MultivariateGaussian(m, C);
}

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

/**
 * Adds two MultivariateGaussian random variables together and returns the
 * resulting MultivariateGaussian
 *
 * @param other MultivariateGaussian to add to this MultivariateGaussian
 * @return Effective addition of the two MultivariateGaussian random
 * variables
 */
public MultivariateGaussian plus(
  MultivariateGaussian other)
{
  Vector m = this.mean.plus(other.getMean());
  Matrix C = this.getCovariance().plus(other.getCovariance());
  return new MultivariateGaussian(m, C);
}