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

Vector.convertToVector介绍

暂无

代码示例

代码示例来源：origin: algorithmfoundry/Foundry

public <SampleType> MultivariateGaussian.PDF integrate(
  Collection<? extends SampleType> samples,
  Evaluator<? super SampleType, ? extends Vector> expectationFunction)
{
  ArrayList<Vector> outputs = new ArrayList<Vector>( samples.size() );
  for( SampleType sample : samples )
  {
    outputs.add( expectationFunction.evaluate(sample).convertToVector() );
  }
  return MultivariateGaussian.MaximumLikelihoodEstimator.learn(
    outputs, DEFAULT_VARIANCE );
}

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

public <SampleType> MultivariateGaussian.PDF integrate(
  Collection<? extends SampleType> samples,
  Evaluator<? super SampleType, ? extends Vector> expectationFunction)
{
  ArrayList<Vector> outputs = new ArrayList<Vector>( samples.size() );
  for( SampleType sample : samples )
  {
    outputs.add( expectationFunction.evaluate(sample).convertToVector() );
  }
  return MultivariateGaussian.MaximumLikelihoodEstimator.learn(
    outputs, DEFAULT_VARIANCE );
}

代码示例来源：origin: algorithmfoundry/Foundry

public <SampleType> MultivariateGaussian.PDF integrate(
  Collection<? extends SampleType> samples,
  Evaluator<? super SampleType, ? extends Vector> expectationFunction)
{
  ArrayList<Vector> outputs = new ArrayList<Vector>( samples.size() );
  for( SampleType sample : samples )
  {
    outputs.add( expectationFunction.evaluate(sample).convertToVector() );
  }
  return MultivariateGaussian.MaximumLikelihoodEstimator.learn(
    outputs, DEFAULT_VARIANCE );
}

代码示例来源：origin: algorithmfoundry/Foundry

public <SampleType> MultivariateGaussian.PDF integrate(
  List<? extends WeightedValue<? extends SampleType>> samples,
  Evaluator<? super SampleType, ? extends Vector> expectationFunction)
{
  ArrayList<DefaultWeightedValue<Vector>> outputs =
    new ArrayList<DefaultWeightedValue<Vector>>( samples.size() );
  for( WeightedValue<? extends SampleType> sample : samples )
  {
    Vector output =
      expectationFunction.evaluate(sample.getValue()).convertToVector();
    outputs.add( new DefaultWeightedValue<Vector>(output, sample.getWeight()) );
  }
  return MultivariateGaussian.WeightedMaximumLikelihoodEstimator.learn(
    outputs, DEFAULT_VARIANCE);
}

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

public <SampleType> MultivariateGaussian.PDF integrate(
  List<? extends WeightedValue<? extends SampleType>> samples,
  Evaluator<? super SampleType, ? extends Vector> expectationFunction)
{
  ArrayList<DefaultWeightedValue<Vector>> outputs =
    new ArrayList<DefaultWeightedValue<Vector>>( samples.size() );
  for( WeightedValue<? extends SampleType> sample : samples )
  {
    Vector output =
      expectationFunction.evaluate(sample.getValue()).convertToVector();
    outputs.add( new DefaultWeightedValue<Vector>(output, sample.getWeight()) );
  }
  return MultivariateGaussian.WeightedMaximumLikelihoodEstimator.learn(
    outputs, DEFAULT_VARIANCE);
}

代码示例来源：origin: algorithmfoundry/Foundry

public <SampleType> MultivariateGaussian.PDF integrate(
  List<? extends WeightedValue<? extends SampleType>> samples,
  Evaluator<? super SampleType, ? extends Vector> expectationFunction)
{
  ArrayList<DefaultWeightedValue<Vector>> outputs =
    new ArrayList<DefaultWeightedValue<Vector>>( samples.size() );
  for( WeightedValue<? extends SampleType> sample : samples )
  {
    Vector output =
      expectationFunction.evaluate(sample.getValue()).convertToVector();
    outputs.add( new DefaultWeightedValue<Vector>(output, sample.getWeight()) );
  }
  return MultivariateGaussian.WeightedMaximumLikelihoodEstimator.learn(
    outputs, DEFAULT_VARIANCE);
}

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

Vector input = pair.getInput().convertToVector().stack(one);
final double weight = DatasetUtil.getWeight(pair);
if( weight != 1.0 )

代码示例来源：origin: algorithmfoundry/Foundry

Vector input = pair.getInput().convertToVector().stack(one);
final double weight = DatasetUtil.getWeight(pair);
if( weight != 1.0 )

代码示例来源：origin: algorithmfoundry/Foundry

Vector input = pair.getInput().convertToVector().stack(one);
final double weight = DatasetUtil.getWeight(pair);
if( weight != 1.0 )