本文整理汇总了Java中edu.berkeley.nlp.math.SloppyMath.isVeryDangerous方法的典型用法代码示例。如果您正苦于以下问题：Java SloppyMath.isVeryDangerous方法的具体用法？Java SloppyMath.isVeryDangerous怎么用？Java SloppyMath.isVeryDangerous使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类edu.berkeley.nlp.math.SloppyMath的用法示例。
在下文中一共展示了SloppyMath.isVeryDangerous方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Java代码示例。
示例1: delinearizeLexicon
import edu.berkeley.nlp.math.SloppyMath; //导入方法依赖的package包/类
@Override
public void delinearizeLexicon(double[] logProbs) {
int nDangerous = 0;
for (short tag = 0; tag < lexicon.hierarchicalScores.length; tag++) {
for (int word = 0; word < lexicon.hierarchicalScores[tag].length; word++) {
int index = linearIndex[tag][word];
double[] vals = lexicon.getLastLevel(tag, word);
for (int substate = 0; substate < vals.length; substate++) {
double val = logProbs[index++];
if (SloppyMath.isVeryDangerous(val)) {
nDangerous++;
continue;
}
vals[substate] = val;
}
}
}
if (nDangerous > 0)
System.out
.println("Left "
+ nDangerous
+ " lexicon weights unchanged since the proposed weight was dangerous.");
lexicon.explicitlyComputeScores(finalLevel);
// System.out.println(lexicon);
// return lexicon;
}
示例2: sanityCheckLLs
import edu.berkeley.nlp.math.SloppyMath; //导入方法依赖的package包/类
/**
* @param goldLL
* @param allLL
* @param stateSetTree
* @return
*/
protected boolean sanityCheckLLs(double goldLL, double allLL,
Tree<StateSet> stateSetTree) {
if (SloppyMath.isVeryDangerous(allLL)
|| SloppyMath.isVeryDangerous(goldLL)) {
unparsableTrees++;
return false;
}
if (goldLL - allLL > 1.0e-4) {
System.out.println("Something is wrong! The gold LL is "
+ goldLL + " and the all LL is " + allLL);// +"\n"+sentence+"\n"+stateSetTree);
System.out.println(stateSetTree);
incorrectLLTrees++;
return false;
}
return true;
}
示例3: l2_regularize
import edu.berkeley.nlp.math.SloppyMath; //导入方法依赖的package包/类
public double l2_regularize(double objective, double[] derivatives) {
// Incorporate penalty terms (regularization) into the objective and
// derivatives
if (SloppyMath.isVeryDangerous(objective))
return objective;
double sigma2 = sigma * sigma;
double penalty = 0.0;
for (int index = 0; index < x.length; index++) {
// if (lastX[index]==10000 || Double.isInfinite(lastX[index]))
// continue;
penalty += x[index] * x[index];
}
// System.out.print(" penalty="+penalty);
objective -= penalty / (2 * sigma2);
for (int index = 0; index < x.length; index++) {
// 'x' and 'derivatives' have same layout
// if (lastX[index]==10000 || Double.isInfinite(lastX[index]))
// continue;
derivatives[index] -= x[index] / sigma2;
if (SloppyMath.isVeryDangerous(derivatives[index])) {
System.out
.println("Setting regularized derivative to zero because it is Inf.");
derivatives[index] = 0;
}
}
return objective;
}
示例4: delinearizeLexicon
import edu.berkeley.nlp.math.SloppyMath; //导入方法依赖的package包/类
public void delinearizeLexicon(double[] logProbs) {
int nDangerous = 0;
for (short tag = 0; tag < lexicon.scores.length; tag++) {
for (int word = 0; word < lexicon.scores[tag][0].length; word++) {
int index = linearIndex[tag][word];
for (int substate = 0; substate < lexicon.numSubStates[tag]; substate++) {
double val = Math.exp(logProbs[index++]);
if (SloppyMath.isVeryDangerous(val)) {
System.out
.println("dangerous value when delinearizng lexicon "
+ lexicon.scores[tag][substate][word]);
System.out.println("Word "
+ lexicon.wordIndexer
.get(lexicon.tagWordIndexer[tag]
.get(word)) + " tag "
+ logProbs[index - 1]);
val = 0;
nDangerous++;
// continue;
}
lexicon.scores[tag][substate][word] = val;
}
}
}
if (nDangerous > 0)
System.out
.println("Left "
+ nDangerous
+ " lexicon weights unchanged since the proposed weight was dangerous.");
// return lexicon;
}