gov.sandia.cognition.math.matrix.Vector.subVector()方法的使用及代码示例

x33g5p2x  于2022-02-01 转载在 其他  
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本文整理了Java中gov.sandia.cognition.math.matrix.Vector.subVector()方法的一些代码示例，展示了Vector.subVector()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.subVector()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：subVector

Vector.subVector介绍

[英]Gets a subvector of "this", specified by the inclusive indices
[中]获取由包含索引指定的“this”的子向量

代码示例

代码示例来源：origin: algorithmfoundry/Foundry

public void convertFromVector(
  Vector parameters )
{
  int M = this.getNumAutoRegressiveCoefficients();
  int N = this.getNumMovingAverageCoefficients();
  if( (M+N) != parameters.getDimensionality() )
  {
    throw new IllegalArgumentException(
      "Number of dimensions of the parameter Vector aren't equal to the number expected." );
  }
  this.setAutoregressiveCoefficients( parameters.subVector( 0, M-1 ) );
  this.setMovingAverageCoefficients( parameters.subVector( M, N+M-1 ) );
}

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

@Override
public void convertFromVector(
  Vector parameters)
{
  int N = this.getInputDimensionality();
  this.setMean(parameters.subVector(0, N - 1));
  Matrix m = this.getCovariance();
  m.convertFromVector(
    parameters.subVector(N, parameters.getDimensionality() - 1));
  this.setCovariance(m);
}

代码示例来源：origin: algorithmfoundry/Foundry

@Override
public void convertFromVector(
  Vector parameters)
{
  int N = this.getInputDimensionality();
  this.setMean(parameters.subVector(0, N - 1));
  Matrix m = this.getCovariance();
  m.convertFromVector(
    parameters.subVector(N, parameters.getDimensionality() - 1));
  this.setCovariance(m);
}

代码示例来源：origin: algorithmfoundry/Foundry

@Override
public void convertFromVector(
  Vector parameters)
{
  int N = this.getInputDimensionality();
  this.setMean(parameters.subVector(0, N - 1));
  Matrix m = this.getCovariance();
  m.convertFromVector(
    parameters.subVector(N, parameters.getDimensionality() - 1));
  this.setCovariance(m);
}

代码示例来源：origin: algorithmfoundry/Foundry

@Override
public void convertFromVector(
  final Vector parameters)
{
  int dim = this.getGaussian().getInputDimensionality();
  int N = dim + dim*dim;
  parameters.assertDimensionalityEquals(N+2);
  this.getGaussian().convertFromVector(parameters.subVector(0, N-1) );
  this.getInverseGamma().convertFromVector( parameters.subVector(N, N+1) );
}

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

@Override
public void convertFromVector(
  final Vector parameters)
{
  int dim = this.getGaussian().getInputDimensionality();
  int N = dim + dim*dim;
  parameters.assertDimensionalityEquals(N+2);
  this.getGaussian().convertFromVector(parameters.subVector(0, N-1) );
  this.getInverseGamma().convertFromVector( parameters.subVector(N, N+1) );
}

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

@Override
public void convertFromVector(
  Vector parameters)
{
  final int num =
    this.getInputDimensionality() * this.getOutputDimensionality();
  parameters.assertDimensionalityEquals(num + this.getOutputDimensionality());
  Vector mp = parameters.subVector(0,num-1);
  Vector bp = parameters.subVector(num, num+this.getOutputDimensionality()-1);
  super.convertFromVector( mp );
  this.bias.convertFromVector(bp);
}

代码示例来源：origin: algorithmfoundry/Foundry

public void convertFromVector(
  Vector parameters)
{
  final int d = this.getInputDimensionality();
  parameters.assertDimensionalityEquals( 1+d + 1+d*d );
  this.setCovarianceDivisor( parameters.getElement(0) );
  Vector mean = parameters.subVector(1, d);
  this.gaussian.setMean(mean);
  Vector iwp = parameters.subVector(d+1, parameters.getDimensionality()-1);
  this.inverseWishart.convertFromVector(iwp);
}

代码示例来源：origin: algorithmfoundry/Foundry

@Override
public void convertFromVector(
  Vector parameters)
{
  final int num =
    this.getInputDimensionality() * this.getOutputDimensionality();
  parameters.assertDimensionalityEquals(num + this.getOutputDimensionality());
  Vector mp = parameters.subVector(0,num-1);
  Vector bp = parameters.subVector(num, num+this.getOutputDimensionality()-1);
  super.convertFromVector( mp );
  this.bias.convertFromVector(bp);
}

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

public void convertFromVector(
  Vector parameters)
{
  final int d = this.getInputDimensionality();
  parameters.assertDimensionalityEquals( 1+d + 1+d*d );
  this.setCovarianceDivisor( parameters.getElement(0) );
  Vector mean = parameters.subVector(1, d);
  this.gaussian.setMean(mean);
  Vector iwp = parameters.subVector(d+1, parameters.getDimensionality()-1);
  this.inverseWishart.convertFromVector(iwp);
}

代码示例来源：origin: algorithmfoundry/Foundry

public void convertFromVector(
  Vector parameters)
{
  final int d = this.getInputDimensionality();
  parameters.assertDimensionalityEquals( 1+d + 1+d*d );
  this.setCovarianceDivisor( parameters.getElement(0) );
  Vector mean = parameters.subVector(1, d);
  this.gaussian.setMean(mean);
  Vector iwp = parameters.subVector(d+1, parameters.getDimensionality()-1);
  this.inverseWishart.convertFromVector(iwp);
}

代码示例来源：origin: algorithmfoundry/Foundry

@Override
public void convertFromVector(
  final Vector parameters)
{
  int dim = this.getGaussian().getInputDimensionality();
  int N = dim + dim*dim;
  parameters.assertDimensionalityEquals(N+2);
  this.getGaussian().convertFromVector(parameters.subVector(0, N-1) );
  this.getInverseGamma().convertFromVector( parameters.subVector(N, N+1) );
}

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

public void convertFromVector(
  Vector parameters)
{
  final int dim = this.getInputDimensionality();
  parameters.assertDimensionalityEquals(1+dim+dim*dim);
  this.setDegreesOfFreedom( parameters.getElement(0) );
  this.setMean( parameters.subVector(1, dim) );
  Matrix p = this.getPrecision();
  p.convertFromVector( parameters.subVector(
    dim+1, parameters.getDimensionality()-1) );
  this.setPrecision(p);
}

代码示例来源：origin: algorithmfoundry/Foundry

public void convertFromVector(
  Vector parameters)
{
  final int dim = this.getInputDimensionality();
  parameters.assertDimensionalityEquals(1+dim+dim*dim);
  this.setDegreesOfFreedom( parameters.getElement(0) );
  this.setMean( parameters.subVector(1, dim) );
  Matrix p = this.getPrecision();
  p.convertFromVector( parameters.subVector(
    dim+1, parameters.getDimensionality()-1) );
  this.setPrecision(p);
}

代码示例来源：origin: algorithmfoundry/Foundry

public void convertFromVector(
  Vector parameters)
{
  final int dim = this.getInputDimensionality();
  parameters.assertDimensionalityEquals(1+dim+dim*dim);
  this.setDegreesOfFreedom( parameters.getElement(0) );
  this.setMean( parameters.subVector(1, dim) );
  Matrix p = this.getPrecision();
  p.convertFromVector( parameters.subVector(
    dim+1, parameters.getDimensionality()-1) );
  this.setPrecision(p);
}

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

@Override
public void convertFromVector(
  Vector parameters)
{
  final int dim = this.getInputDimensionality() + 1;
  parameters.assertDimensionalityEquals( dim );
  this.setWeightVector( parameters.subVector(0, dim-2) );
  this.setBias( parameters.getElement(dim-1) );
}

代码示例来源：origin: algorithmfoundry/Foundry

@Override
public void convertFromVector(
  final Vector parameters)
{
  int p = this.getInputDimensionality();
  parameters.assertDimensionalityEquals( 1 + p*p );
  int dof = (int) Math.round(parameters.getElement(0));
  Vector matrix =
    parameters.subVector(1, parameters.getDimensionality()-1 );
  this.setDegreesOfFreedom(dof);
  this.getInverseScale().convertFromVector( matrix );
}

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

@Override
public void convertFromVector(
  final Vector parameters)
{
  int p = this.getInputDimensionality();
  parameters.assertDimensionalityEquals( 1 + p*p );
  int dof = (int) Math.round(parameters.getElement(0));
  Vector matrix =
    parameters.subVector(1, parameters.getDimensionality()-1 );
  this.setDegreesOfFreedom(dof);
  this.getInverseScale().convertFromVector( matrix );
}

代码示例来源：origin: algorithmfoundry/Foundry

@Override
public void convertFromVector(
  Vector parameters)
{
  final int dim = this.getInputDimensionality() + 1;
  parameters.assertDimensionalityEquals( dim );
  this.setWeightVector( parameters.subVector(0, dim-2) );
  this.setBias( parameters.getElement(dim-1) );
}

代码示例来源：origin: algorithmfoundry/Foundry

@Override
public void convertFromVector(
  Vector parameters)
{
  final int dim = this.getInputDimensionality() + 1;
  parameters.assertDimensionalityEquals( dim );
  this.setWeightVector( parameters.subVector(0, dim-2) );
  this.setBias( parameters.getElement(dim-1) );
}

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