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

Vector.assertSameDimensionality介绍

[英]Asserts that this vector has the same dimensionality as the given vector. If this assertion fails, a DimensionalityMismatchException is thrown.
[中]

代码示例

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

@Override
public void convertFromVector(
  final Vector parameters)
{
  this.parameters.assertSameDimensionality(parameters);
  this.setParameters(parameters);
}

代码示例来源：origin: algorithmfoundry/Foundry

@Override
public void convertFromVector(
  final Vector parameters)
{
  this.parameters.assertSameDimensionality(parameters);
  this.setParameters(parameters);
}

代码示例来源：origin: algorithmfoundry/Foundry

@Override
public void convertFromVector(
  final Vector parameters)
{
  this.parameters.assertSameDimensionality(parameters);
  this.setParameters(parameters);
}

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

public void setValue(
  Vector value)
{
  value.assertSameDimensionality(
    this.conditionalDistribution.getParameters() );
  this.conditionalDistribution.setParameters(value);
}

代码示例来源：origin: algorithmfoundry/Foundry

public void setValue(
  Vector value)
{
  value.assertSameDimensionality(
    this.conditionalDistribution.getParameters() );
  this.conditionalDistribution.setParameters(value);
}

代码示例来源：origin: algorithmfoundry/Foundry

public void setValue(
  Vector value)
{
  value.assertSameDimensionality(
    this.conditionalDistribution.getParameters() );
  this.conditionalDistribution.setParameters(value);
}

代码示例来源：origin: algorithmfoundry/Foundry

@Override
public MultinomialDistribution createConditionalDistribution(
  Vector parameter)
{
  parameter.assertSameDimensionality(
    this.parameter.getConditionalDistribution().getParameters() );
  return super.createConditionalDistribution(parameter);
}

代码示例来源：origin: algorithmfoundry/Foundry

@Override
public void convertFromVector(
  final Vector parameters)
{
  parameters.assertSameDimensionality( this.getParameters() );
  this.setParameters( ObjectUtil.cloneSafe(parameters) );
}

代码示例来源：origin: algorithmfoundry/Foundry

@Override
public MultinomialDistribution createConditionalDistribution(
  Vector parameter)
{
  parameter.assertSameDimensionality(
    this.parameter.getConditionalDistribution().getParameters() );
  return super.createConditionalDistribution(parameter);
}

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

@Override
public void convertFromVector(
  final Vector parameters)
{
  parameters.assertSameDimensionality( this.getParameters() );
  this.setParameters( ObjectUtil.cloneSafe(parameters) );
}

代码示例来源：origin: algorithmfoundry/Foundry

@Override
public void convertFromVector(
  final Vector parameters)
{
  parameters.assertSameDimensionality( this.getParameters() );
  this.setParameters( ObjectUtil.cloneSafe(parameters) );
}

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

@Override
public void convertFromVector(
  final Vector parameters)
{
  parameters.assertSameDimensionality( this.getParameters() );
  this.setParameters( ObjectUtil.cloneSafe(parameters) );
}

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

@Override
public MultinomialDistribution createConditionalDistribution(
  Vector parameter)
{
  parameter.assertSameDimensionality(
    this.parameter.getConditionalDistribution().getParameters() );
  return super.createConditionalDistribution(parameter);
}

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

@Override
public void convertFromVector(
  final Vector parameters)
{
  parameters.assertSameDimensionality( this.getParameters() );
  this.setParameters( ObjectUtil.cloneSafe(parameters) );
}

代码示例来源：origin: algorithmfoundry/Foundry

@Override
public void convertFromVector(
  final Vector parameters)
{
  parameters.assertSameDimensionality( this.getParameters() );
  this.setParameters( ObjectUtil.cloneSafe(parameters) );
}

代码示例来源：origin: algorithmfoundry/Foundry

@Override
public void convertFromVector(
  final Vector parameters)
{
  parameters.assertSameDimensionality( this.getParameters() );
  this.setParameters( ObjectUtil.cloneSafe(parameters) );
}

代码示例来源：origin: algorithmfoundry/Foundry

@Override
public void convertFromVector(
  final Vector parameters)
{
  parameters.assertSameDimensionality( this.getParameters() );
  this.setParameters( ObjectUtil.cloneSafe(parameters) );
}

代码示例来源：origin: algorithmfoundry/Foundry

@Override
public void convertFromVector(
  final Vector parameters)
{
  parameters.assertSameDimensionality( this.getParameters() );
  this.setParameters( ObjectUtil.cloneSafe(parameters) );
}

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

@Override
public double logEvaluate(
  final Vector input)
{
  Vector xn = input.scale( 1.0 / input.norm1() );
  Vector a = this.getParameters();
  input.assertSameDimensionality( a );
  double logsum = 0.0;
  final int K = a.getDimensionality();
  for( int i = 0; i < K; i++ )
  {
    double xi = xn.getElement(i);
    if( (xi <= 0.0) || (1.0 <= xi) )
    {
      throw new IllegalArgumentException(
        "Expected all inputs to be (0.0,infinity): " + input );
    }
    double ai = a.getElement(i);
    logsum += (ai-1.0) * Math.log( xi );
  }
  logsum -= MathUtil.logMultinomialBetaFunction( a );
  return logsum;
}

代码示例来源：origin: algorithmfoundry/Foundry

@Override
public double logEvaluate(
  final Vector input)
{
  Vector xn = input.scale( 1.0 / input.norm1() );
  Vector a = this.getParameters();
  input.assertSameDimensionality( a );
  double logsum = 0.0;
  final int K = a.getDimensionality();
  for( int i = 0; i < K; i++ )
  {
    double xi = xn.getElement(i);
    if( (xi <= 0.0) || (1.0 <= xi) )
    {
      throw new IllegalArgumentException(
        "Expected all inputs to be (0.0,infinity): " + input );
    }
    double ai = a.getElement(i);
    logsum += (ai-1.0) * Math.log( xi );
  }
  logsum -= MathUtil.logMultinomialBetaFunction( a );
  return logsum;
}