本文整理了Java中edu.illinois.cs.cogcomp.sl.util.WeightVector.dotProduct()方法的一些代码示例，展示了WeightVector.dotProduct()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.dotProduct()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：dotProduct

WeightVector.dotProduct介绍

暂无

代码示例

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

/***
 * Computes wv^T\phi(x,y).
 * Override this function if you have a faster implementation for computing
 * wv^T\phi(x,y).
 * @param wv
 * @param x
 * @param y
 * @return
 */
public float decisionValue(WeightVector wv, IInstance x, IStructure y) {
  return wv.dotProduct(getFeatureVector(x, y));
}

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

/***
 * Computes wv^T\phi(x,y).
 * Override this function if you have a faster implementation for computing
 * wv^T\phi(x,y).
 * @param wv
 * @param x
 * @param y
 * @return
 */
public float decisionValue(WeightVector wv, IInstance x, IStructure y) {
  return wv.dotProduct(getFeatureVector(x, y));
}

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

@Override
public IStructure getBestStructure(WeightVector weight, IInstance ins)
    throws Exception {
  RankingInstance ri = (RankingInstance) ins;
  int maxIndex = -1;
  double maxScore = Double.NEGATIVE_INFINITY;
  
  for(int i=0; i < ri.featureList.size(); i ++){
    double score = weight.dotProduct(ri.featureList.get(i));
    if (score > maxScore){
      maxScore = score;
      maxIndex = i;
    }
  }
  assert maxIndex != -1;
  
  return new RankingLabel(maxIndex);
}
@Override

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

@Override
public IStructure getLossAugmentedBestStructure(
    WeightVector weight, IInstance ins, IStructure goldStructure)
    throws Exception {
  RankingInstance ri = (RankingInstance) ins;
  RankingLabel lri = (RankingLabel) goldStructure;
  int max_index = -1;
  double max_score = Double.NEGATIVE_INFINITY;
  
  for(int i=0; i < ri.featureList.size(); i ++){
    float loss = -ri.scoreList.get(i)+ ri.scoreList.get(lri.pred_item);
    if (loss< 0){
      System.out.println("-----------");
      System.out.println(ri.scoreList);
      System.out.println("best: " + lri.pred_item);
    }
    assert loss >= 0;
    float score = weight.dotProduct(ri.featureList.get(i)) + loss;
    if (score > max_score){
      max_score = score;
      max_index = i;
    }
  }
  assert max_index != -1;
  
  RankingLabel pred = new RankingLabel(max_index);
  //System.out.println("pred: " + max_index + " gold: " + lri.pred_item);
  return pred;
}

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

@Override
public IStructure getLossAugmentedBestStructure(
    WeightVector weight, IInstance ins, IStructure goldStructure)
    throws Exception {
  
  MultiClassInstance mi = (MultiClassInstance) ins;
  MultiClassLabel lmi = (MultiClassLabel) goldStructure;
  
  int bestOutput = -1;
  float bestScore = Float.NEGATIVE_INFINITY;
  
  for(int i=0; i < mi.numberOfClasses ; i ++){
    float score = weight.dotProduct(mi.baseFv,mi.baseNfeature*i);
    
    if (lmi!=null && i != lmi.output){
      if(lossMatrix == null)
        score += 1.0;
      else
        score += lossMatrix[lmi.output][i];
    }                                
    
    if (score > bestScore){
      bestOutput = i;
      bestScore = score;
    }
  }
        
  assert bestOutput >= 0 ;
  return new MultiClassLabel(bestOutput);		
}

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

private String predictLabel(int head, int node, DepInst ins, WeightVector weight) {
  if (head == -1)
    throw new IllegalArgumentException("Invalid arc, head must be positive!");
  String rel = null;
  float max = Float.NEGATIVE_INFINITY;
  String keyPOS = ins.strPos[head] + " " + ins.strPos[node];
  Set<String> candidates = new HashSet<>();
  if (deprelDict.get(keyPOS) != null)
    candidates.addAll(deprelDict.get(keyPOS));
  if (candidates.size() == 1)
    return candidates.iterator().next();
  else if (candidates.isEmpty()) {
    if (keyPOS.contains("."))
      return "P";
    candidates.addAll(ALL_RELS);
  }
  for (String candidate : candidates) {
    FeatureVectorBuffer edgefv = depfeat.getLabeledEdgeFeatures(head, node, ins, candidate);
    float decision = weight.dotProduct(edgefv.toFeatureVector(false));
    if (decision > max) {
      rel = candidate;
      max = decision;
    }
  }
  return rel;
}

代码示例来源：origin: CogComp/cogcomp-nlp

private String predictLabel(int head, int node, DepInst ins, WeightVector weight) {
  if (head == -1)
    throw new IllegalArgumentException("Invalid arc, head must be positive!");
  String rel = null;
  float max = Float.NEGATIVE_INFINITY;
  String keyPOS = ins.strPos[head] + " " + ins.strPos[node];
  Set<String> candidates = new HashSet<>();
  if (deprelDict.get(keyPOS) != null)
    candidates.addAll(deprelDict.get(keyPOS));
  if (candidates.size() == 1)
    return candidates.iterator().next();
  else if (candidates.isEmpty()) {
    if (keyPOS.contains("."))
      return "P";
    candidates.addAll(ALL_RELS);
  }
  for (String candidate : candidates) {
    FeatureVectorBuffer edgefv = depfeat.getLabeledEdgeFeatures(head, node, ins, candidate);
    float decision = weight.dotProduct(edgefv.toFeatureVector(false));
    if (decision > max) {
      rel = candidate;
      max = decision;
    }
  }
  return rel;
}

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

private void printErrorLogForIncorrectInference(WeightVector wv, float loss, IStructure h,
    float xi, float dotProduct, float score,
    float max_score_in_cache) {
  logger.error("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!");
  logger.error("The inference procedure is not correct!");
  logger.error("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!");
  logger.error("Pred: " + h);
  logger.error("docproduct on pred: "
      + wv.dotProduct(h.getFeatureVector()));
  logger.error("dotproduct on diff: " + dotProduct);
  logger.error("loss: " + loss);
  logger.error("xi: " + xi);
  logger.error("Warning: the inference (argmax) code is not right......");
  logger.error("score: " + score);
  logger.error("max score in cache: " + max_score_in_cache);
  for (int i = 0; i < goldStructureList.size(); i++) {
    IStructure f = goldStructureList.get(i);
    Pair<float[], FeatureVector> pair = alphaFeatureVectorList.get(i);
    logger.error(">>>" + f + " alpha: " + pair.getFirst()[0]
        + " loss: " + pair.getFirst()[1]);
    logger.error(">>> (dot) "
        + wv.dotProduct(pair.getSecond()));
  }
  logger.error("[GOLD]" + goldStructure);
  logger.error("gold dot product: "
      + wv.dotProduct(goldStructure.getFeatureVector()));
}

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

float dot_product = wv.dotProduct(fv);
float xij_norm2 = fv.getSquareL2Norm();

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

float dotProduct = wv.dotProduct(diff);
float score = (loss - dotProduct) - xi;	// line 12 in DCD-SSVM (algorithm 3 in paper)
  for(AlphaStruct as:new ArrayList<AlphaStruct>(candidateAlphas)){
    if(as !=null){
      float s = as.loss - wv.dotProduct(as.alphaFeactureVector) - xi;
      if (max_score_in_cache < s)
        max_score_in_cache = s;

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

/**
 * Get primal objective function value with respect to the weight vector wv
 * @param sp
 * @param wv
 * @param infSolver
 * @param C
 * @return
 * @throws Exception
 */
public static float getPrimalObjective(
    StructuredProblem sp, WeightVector wv,
    AbstractInferenceSolver infSolver, float C) throws Exception {
  float obj = 0;
  obj += wv.getSquareL2Norm() * 0.5;
  List<IInstance> input_list = sp.instanceList;
  List<IStructure> output_list = sp.goldStructureList;
  for (int i = 0; i < input_list.size(); i++) {
    IInstance ins = input_list.get(i);
    IStructure gold_struct = output_list.get(i);
    float sC= C;
    Pair<IStructure, Float > res = infSolver
        .getLossAugmentedBestStructure(wv, ins, gold_struct);
    float loss = res.getSecond()
        + wv.dotProduct(res.getFirst().getFeatureVector())
        - wv.dotProduct(gold_struct.getFeatureVector());
    obj += sC * loss * loss;
  }
  return obj;
}

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

public double getIdentifierRawScore(SRLMulticlassInstance x) throws Exception {
  log.debug("Classifying {}", x);
  WeightVector w = manager.getModelInfo(Models.Identifier).getWeights();
  SRLMulticlassLabel y1 = new SRLMulticlassLabel(1, Models.Identifier, manager);
  SRLMulticlassLabel y0 = new SRLMulticlassLabel(0, Models.Identifier, manager);
  double score1= w.dotProduct(x.getCachedFeatureVector(Models.Identifier),1 * manager.getModelInfo(Models.Identifier).getLexicon().size());
  double score2= w.dotProduct(x.getCachedFeatureVector(Models.Identifier));
  return (double) (score1 - score2);
}

代码示例来源：origin: CogComp/cogcomp-nlp

/**
 * Scores instance for the different labels allowed for it
 */
public double[] getScores(SenseInstance x, boolean rescoreInvalidLabels) {
  int numLabels = this.getNumLabels();
  double[] scores = new double[numLabels];
  WeightVector w;
  try {
    w = this.getModelInfo().getWeights();
    assert w != null;
  } catch (Exception e) {
    log.error("Unable to load weight vector, exception:\n{}", e);
    throw new RuntimeException(e);
  }
  for (int label = 0; label < numLabels; label++) {
    if (!this.isValidLabel(x, label) && rescoreInvalidLabels) {
      scores[label] = -50;
    } else {
      SenseStructure y = new SenseStructure(x, label, this);
      scores[label] = w.dotProduct(y.getFeatureVector());
    }
  }
  scores = MathUtilities.softmax(scores);
  return scores;
}

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

/**
 * Scores instance for the different labels allowed for it
 */
public double[] getScores(SenseInstance x, boolean rescoreInvalidLabels) {
  int numLabels = this.getNumLabels();
  double[] scores = new double[numLabels];
  WeightVector w;
  try {
    w = this.getModelInfo().getWeights();
    assert w != null;
  } catch (Exception e) {
    log.error("Unable to load weight vector, exception:\n{}", e);
    throw new RuntimeException(e);
  }
  for (int label = 0; label < numLabels; label++) {
    if (!this.isValidLabel(x, label) && rescoreInvalidLabels) {
      scores[label] = -50;
    } else {
      SenseStructure y = new SenseStructure(x, label, this);
      scores[label] = w.dotProduct(y.getFeatureVector());
    }
  }
  scores = MathUtilities.softmax(scores);
  return scores;
}

代码示例来源：origin: CogComp/cogcomp-nlp

double score = weight.dotProduct(y.getFeatureVector());

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

double score = weight.dotProduct(y.getFeatureVector());

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

/**
 * Scores instance for the different labels allowed for it
 */
public double[] getScores(SRLMulticlassInstance x, Models type, boolean rescoreInvalidLabels) {
  int numLabels = this.getNumLabels(type);
  double[] scores = new double[numLabels];
  WeightVector w;
  try {
    w = this.getModelInfo(type).getWeights();
    assert w != null;
  } catch (Exception e) {
    log.error("Unable to load weight vector for {}", type, e);
    throw new RuntimeException(e);
  }
  for (int label = 0; label < numLabels; label++) {
    if (!this.isValidLabel(x, type, label) && rescoreInvalidLabels) {
      scores[label] = -50;
    }
    else {
      scores[label] = w.dotProduct(x.getCachedFeatureVector(type),label * this.getModelInfo(type).getLexicon().size());
    }
  }
  scores = MathUtilities.softmax(scores);
  return scores;
}

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

IFeatureVector predictedFeatures = featureGenerator.getFeatureVector(example, prediction);
IFeatureVector update = goldFeatures.difference(predictedFeatures);
double loss_term = loss - w.dotProduct(update);

代码示例来源：origin: CogComp/cogcomp-nlp

@Override
public IStructure getLossAugmentedBestStructure(WeightVector weight, IInstance ins,
    IStructure goldStructure) throws Exception {
  DepInst sent = (DepInst) ins;
  DepStruct gold = goldStructure != null ? (DepStruct) goldStructure : null;
  // edgeScore[i][j] score of edge from head i to modifier j
  // i (head) varies from 0..n, while j (token idx) varies over 1..n
  double[][] edgeScore = new double[sent.size() + 1][sent.size() + 1];
  String[][] edgeLabel = new String[sent.size() + 1][sent.size() + 1];
  initEdge(edgeScore, edgeLabel);
  for (int head = 0; head <= sent.size(); head++) {
    for (int j = 1; j <= sent.size(); j++) {
      if (head == j) {
        edgeScore[head][j] = Double.NEGATIVE_INFINITY;
        continue;
      }
      String deprel = predictLabel(head, j, sent, weight);
      edgeLabel[head][j] = deprel;
      FeatureVectorBuffer edgefv = depfeat.getCombineEdgeFeatures(head, j, sent, deprel);
      // edge from head i to modifier j
      edgeScore[head][j] = weight.dotProduct(edgefv.toFeatureVector(false));
      if (gold != null) {
        if (gold.heads[j] != head || !deprel.equals(gold.deprels[j])) // incur loss
          edgeScore[head][j] += 1.0f;
      }
    }
  }
  return LabeledChuLiuEdmonds(edgeScore, edgeLabel);
}

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

@Override
public IStructure getLossAugmentedBestStructure(WeightVector weight, IInstance ins,
    IStructure goldStructure) throws Exception {
  DepInst sent = (DepInst) ins;
  DepStruct gold = goldStructure != null ? (DepStruct) goldStructure : null;
  // edgeScore[i][j] score of edge from head i to modifier j
  // i (head) varies from 0..n, while j (token idx) varies over 1..n
  double[][] edgeScore = new double[sent.size() + 1][sent.size() + 1];
  String[][] edgeLabel = new String[sent.size() + 1][sent.size() + 1];
  initEdge(edgeScore, edgeLabel);
  for (int head = 0; head <= sent.size(); head++) {
    for (int j = 1; j <= sent.size(); j++) {
      if (head == j) {
        edgeScore[head][j] = Double.NEGATIVE_INFINITY;
        continue;
      }
      String deprel = predictLabel(head, j, sent, weight);
      edgeLabel[head][j] = deprel;
      FeatureVectorBuffer edgefv = depfeat.getCombineEdgeFeatures(head, j, sent, deprel);
      // edge from head i to modifier j
      edgeScore[head][j] = weight.dotProduct(edgefv.toFeatureVector(false));
      if (gold != null) {
        if (gold.heads[j] != head || !deprel.equals(gold.deprels[j])) // incur loss
          edgeScore[head][j] += 1.0f;
      }
    }
  }
  return LabeledChuLiuEdmonds(edgeScore, edgeLabel);
}