本文整理了Java中edu.illinois.cs.cogcomp.sl.util.WeightVector.setExtendable()方法的一些代码示例，展示了WeightVector.setExtendable()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。WeightVector.setExtendable()方法的具体详情如下：
包路径：edu.illinois.cs.cogcomp.sl.util.WeightVector
类名称：WeightVector
方法名：setExtendable

WeightVector.setExtendable介绍

暂无

代码示例

代码示例来源：origin: edu.illinois.cs.cogcomp/illinois-sl-core

/**
 * The function for the users to call for the structured SVM
 * 
 * @param sp
 *            Structured Labeled Dataset
 * @param params
 *            parameters
 * @return
 * @throws Exception
 */
@Override
public WeightVector train(final SLProblem sp, SLParameters params) throws Exception {
  WeightVector wv = null;
  
  // +1 because wv.u[0] stores the bias term
  if(params.TOTAL_NUMBER_FEATURE >0){
    wv = new WeightVector(params.TOTAL_NUMBER_FEATURE + 1);
    wv.setExtendable(false);
  } else {
    wv = new WeightVector(8192);
    wv.setExtendable(true);
  }
  return train(sp,params,wv);
}

代码示例来源：origin: edu.illinois.cs.cogcomp/illinois-sl

@Deprecated
public static WeightVector getWeightVectorBySumAlpahFv(
    StructuredInstanceWithAlphas[] alphaInsList, boolean isExtendable,
    int numIns) {
  int numFeatures = -1;
  for (int i = 0; i < numIns; i++) {
    int currentMaxIdx = alphaInsList[i].getMaxIdx();
    if (currentMaxIdx > numFeatures)
      numFeatures = currentMaxIdx;
  }
  logger.info("number of features: " + numFeatures);
  WeightVector currentWv = new WeightVector(numFeatures + 1);
  currentWv.setExtendable(isExtendable);
  // float[] cur_w = new float[max_n + 1];
  for (int i = 0; i < numIns; i++) {
    alphaInsList[i].fillWeightVector(currentWv);
  }
  return currentWv;
}

代码示例来源：origin: edu.illinois.cs.cogcomp/IllinoisSL-core

/**
 * The function for the users to call for the structured SVM
 * 
 * @param infSolver
 *            The inference solver (dynamic programming, ILP,...). Given an
 *            input (IInstance) and a Weight vector (WeightVector), return
 *            the best structure (AbstractStructures)
 * @param sp
 *            Structured Labeled Dataset
 * @param parameters
 *            parameters for JLIS
 * @return
 * @throws Exception
 */
@Override
public WeightVector train(final StructuredProblem sp, Parameters params) throws Exception {
  WeightVector wv = null;
  
  // +1 because wv.u[0] stores the bias term
  if(params.TOTAL_NUMBER_FEATURE >0){
    wv = new WeightVector(params.TOTAL_NUMBER_FEATURE + 1);
    wv.setExtendable(false);
  } else {
    wv = new WeightVector(8192);
    wv.setExtendable(true);
  }
  return DCDForL2LossSSVM(wv, infSolver, sp, params);
}

代码示例来源：origin: edu.illinois.cs.cogcomp/illinois-sl

/**
 * The function for the users to call for the structured SVM
 * 
 * @param infSolver
 *            The inference solver (dynamic programming, ILP,...). Given an
 *            input (IInstance) and a Weight vector (WeightVector), return
 *            the best structure (AbstractStructures)
 * @param sp
 *            Structured Labeled Dataset
 * @param parameters
 *            parameters for JLIS
 * @return
 * @throws Exception
 */
@Override
public WeightVector train(final SLProblem sp, SLParameters params) throws Exception {
  WeightVector wv = null;
  
  // +1 because wv.u[0] stores the bias term
  if(params.TOTAL_NUMBER_FEATURE >0){
    wv = new WeightVector(params.TOTAL_NUMBER_FEATURE + 1);
    wv.setExtendable(false);
  } else {
    wv = new WeightVector(8192);
    wv.setExtendable(true);
  }
  return train(sp,params,wv);
}

代码示例来源：origin: edu.illinois.cs.cogcomp/illinois-sl-core

@Deprecated
public static WeightVector getWeightVectorBySumAlpahFv(
    StructuredInstanceWithAlphas[] alphaInsList, boolean isExtendable,
    int numIns) {
  int numFeatures = -1;
  for (int i = 0; i < numIns; i++) {
    int currentMaxIdx = alphaInsList[i].getMaxIdx();
    if (currentMaxIdx > numFeatures)
      numFeatures = currentMaxIdx;
  }
  logger.info("number of features: " + numFeatures);
  WeightVector currentWv = new WeightVector(numFeatures + 1);
  currentWv.setExtendable(isExtendable);
  // float[] cur_w = new float[max_n + 1];
  for (int i = 0; i < numIns; i++) {
    alphaInsList[i].fillWeightVector(currentWv);
  }
  return currentWv;
}

代码示例来源：origin: edu.illinois.cs.cogcomp/IllinoisSL-core

@Deprecated
public static WeightVector getWeightVectorBySumAlpahFv(
    StructuredInstanceWithAlphas[] alphaInsList, boolean isExtendable,
    int numIns) {
  int numFeatures = -1;
  for (int i = 0; i < numIns; i++) {
    int currentMaxIdx = alphaInsList[i].getMaxIdx();
    if (currentMaxIdx > numFeatures)
      numFeatures = currentMaxIdx;
  }
  logger.info("number of features: " + numFeatures);
  WeightVector currentWv = new WeightVector(numFeatures + 1);
  currentWv.setExtendable(isExtendable);
  // float[] cur_w = new float[max_n + 1];
  for (int i = 0; i < numIns; i++) {
    alphaInsList[i].fillWeightVector(currentWv);
  }
  return currentWv;
}