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edu.illinois.cs.cogcomp.sl.util.WeightVector.get()方法的使用及代码示例

转载 作者:知者 更新时间:2024-03-25 05:09:05 26 4
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本文整理了Java中edu.illinois.cs.cogcomp.sl.util.WeightVector.get()方法的一些代码示例,展示了WeightVector.get()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.get()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:get

WeightVector.get介绍

[英]should avoid using this function
[中]应避免使用此功能

代码示例

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

float priorScore = wv.get(numOfEmissionFeatures * numOflabels + j);
float zeroOrderScore =  wv.get(sen.tokens[0] + j*numOfEmissionFeatures) +
    ((gold !=null && j != goldLabeledSeq.tags[0])?1:0);
dpTable[0][j] = priorScore + zeroOrderScore; 	 
  float zeroOrderScore = wv.get(sen.tokens[i] + j*numOfEmissionFeatures)
      + ((gold!=null && j != goldLabeledSeq.tags[i])?1:0);
    float candidateScore = dpTable[(i-1)%2][k] +  wv.get(offset + (k * numOflabels + j));
    if (candidateScore > bestScore) {
      bestScore = candidateScore;

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

float priorScore = wv.get(numOfEmissionFeatures * numOflabels + j);
float zeroOrderScore = wv.dotProduct(seq.baseFeatures[0], j*numOfEmissionFeatures)+
    ((gold !=null && j != goldLabeledSeq.tags[0])?1:0);
    float candidateScore = dpTable[(i-1)%2][k] +  wv.get(offset + (k * numOflabels + j));
    if (candidateScore > bestScore) {
      bestScore = candidateScore;

代码示例来源:origin: CogComp/cogcomp-nlp

float priorScore = wv.get(numOfEmissionFeatures * numOflabels + j);
float zeroOrderScore =
    wv.dotProduct(seq.baseFeatures[0], j * numOfEmissionFeatures)
  for (int k = 0; k < numOflabels; k++) {
    float candidateScore =
        dpTable[(i - 1) % 2][k] + wv.get(offset + (k * numOflabels + j));
    if (candidateScore > bestScore) {
      bestScore = candidateScore;

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

float priorScore = wv.get(numOfEmissionFeatures * numOflabels + j);
float zeroOrderScore =
    wv.dotProduct(seq.baseFeatures[0], j * numOfEmissionFeatures)
  for (int k = 0; k < numOflabels; k++) {
    float candidateScore =
        dpTable[(i - 1) % 2][k] + wv.get(offset + (k * numOflabels + j));
    if (candidateScore > bestScore) {
      bestScore = candidateScore;

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