本文整理了Java中edu.illinois.cs.cogcomp.lbjava.classify.Feature.isDiscrete()
方法的一些代码示例,展示了Feature.isDiscrete()
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。Feature.isDiscrete()
方法的具体详情如下:
包路径:edu.illinois.cs.cogcomp.lbjava.classify.Feature
类名称:Feature
方法名:isDiscrete
[英]Determines if this feature is discrete.
[中]确定此功能是否是离散的。
代码示例来源:origin: edu.illinois.cs.cogcomp/LBJava
/**
* Returns the strength of this feature if it were to be placed in a mathematical vector space.
**/
public double getStrength() {
return (left.isDiscrete() ? 1 : left.getStrength())
* (right.isDiscrete() ? 1 : right.getStrength());
}
代码示例来源:origin: edu.illinois.cs.cogcomp/LBJava
/**
* Returns a new feature object that's identical to this feature except its strength is given by
* <code>s</code>.
*
* @param s The strength of the new feature.
* @return A new feature object as above, or <code>null</code> if this feature cannot take the
* specified strength.
**/
public Feature withStrength(double s) {
RealConjunctiveFeature result = (RealConjunctiveFeature) clone();
if (!left.isDiscrete()) {
result.left = left.withStrength(s);
if (!right.isDiscrete())
result.right = right.withStrength(1);
} else
result.right = right.withStrength(s);
return result;
}
代码示例来源:origin: edu.illinois.cs.cogcomp/LBJava
/**
* Returns a new feature object, the same as this one in all respects except the value has been
* multiplied by the specified number.
*
* @param m The multiplier.
* @return A new real feature whose value is the product of this feature's value and the
* specified multiplier.
**/
public RealFeature multiply(double m) {
RealConjunctiveFeature result = (RealConjunctiveFeature) clone();
if (!left.isDiscrete())
result.left = ((RealFeature) left).multiply(m);
else
result.right = ((RealFeature) right).multiply(m);
return result;
}
代码示例来源:origin: edu.illinois.cs.cogcomp/LBJava
/**
* A helper method for {@link #getFeatureKey(Lexicon,boolean,int)}, this method computes the
* feature keys corresponding to the arguments of the conjunction. Here, we lookup the arguments
* to the conjunction in the lexicon so that their counts are never less than the conjunction's,
* and we return the actual feature object that's already a key in the lexicon.
*
* @param f The argument feature for which a key will be computed.
* @param lexicon The lexicon into which this feature will be indexed.
* @param training Whether or not the learner is currently training.
* @param label The label of the example containing this feature, or -1 if we aren't doing per
* class feature counting.
* @return A feature object appropriate for use as the key of a map.
**/
protected Feature getArgumentKey(Feature f, Lexicon lexicon, boolean training, int label) {
if (f.isDiscrete()) {
if (!training)
return f;
if (!f.isPrimitive())
f = f.getFeatureKey(lexicon, true, label);
} else {
f = f.getFeatureKey(lexicon, training, label);
if (!training)
return f;
}
return lexicon.getChildFeature(f, label);
}
代码示例来源:origin: edu.illinois.cs.cogcomp/LBJava
if (!f.isDiscrete())
inst.setValue(attIndex, exampleValues[featureIndex]);
else { // it's a discrete or conjunctive feature.
if (!label.isDiscrete())
inst.setValue(0, labelValues[0]);
else
代码示例来源:origin: edu.illinois.cs.cogcomp/saul
System.exit(1);
if (f.isDiscrete())
inst.setValue(attIndex, "1"); // this feature is used in this example so we set it to "1"
else
if (!label.isDiscrete())
inst.setValue(0, instance.labelValues[0]);
else
代码示例来源:origin: edu.illinois.cs.cogcomp/LBJava
Feature f = (Feature) o;
boolean b1 = isDiscrete();
boolean b2 = f.isDiscrete();
int d = (b2 ? 1 : 0) - (b1 ? 1 : 0);
if (d != 0)
代码示例来源:origin: edu.illinois.cs.cogcomp/LBJava
/**
* Instantiates a feature vector from example arrays and lexicons.
*
* @param ex The example array.
* @param lex The feature lexicon.
* @param llex The label lexicon.
**/
public FeatureVector(Object[] ex, Lexicon lex, Lexicon llex) {
this();
int[] fs = (int[]) ex[0];
double[] vs = (double[]) ex[1];
for (int i = 0; i < fs.length; ++i) {
Feature f = lex.lookupKey(fs[i]);
Feature ff = f.withStrength(vs[i]);
addFeature(ff == null ? f : ff);
}
if (ex.length > 2) {
int[] ls = (int[]) ex[2];
double[] lvs = (double[]) ex[3];
for (int i = 0; i < ls.length; ++i) {
Feature f = llex.lookupKey(ls[i]);
if (!f.isDiscrete())
f = f.withStrength(lvs[i]);
addLabel(f);
}
}
}
代码示例来源:origin: edu.illinois.cs.cogcomp/saul
} else {
Feature label = labelLexicon.lookupKey(0);
if (!label.isDiscrete()) {
Attribute a = new Attribute(label.getStringIdentifier());
attributeInfo.addElement(a);
for (int featureIndex = 0; featureIndex < lexicon.size(); ++featureIndex) {
Feature f = lexicon.lookupKey(featureIndex);
Attribute a = f.isDiscrete() ?
new Attribute(f.toString(), binaryValues) :
new Attribute(f.toString());
代码示例来源:origin: edu.illinois.cs.cogcomp/LBJava
if (label.isDiscrete())
labelArray[f] = labelLexicon.lookup(label, true);
else
内容来源于网络,如有侵权,请联系作者删除!