本文整理汇总了Java中edu.stanford.nlp.util.HashIndex.add方法的典型用法代码示例。如果您正苦于以下问题：Java HashIndex.add方法的具体用法？Java HashIndex.add怎么用？Java HashIndex.add使用的例子？那么恭喜您, 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.stanford.nlp.util.HashIndex的用法示例。
在下文中一共展示了HashIndex.add方法的3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: applyFeatureCountThreshold
import edu.stanford.nlp.util.HashIndex; //导入方法依赖的package包/类
/**
* Applies a feature count threshold to the RVFDataset. All features that
* occur fewer than <i>k</i> times are expunged.
*/
public void applyFeatureCountThreshold(int k) {
float[] counts = getFeatureCounts();
HashIndex<F> newFeatureIndex = new HashIndex<F>();
int[] featMap = new int[featureIndex.size()];
for (int i = 0; i < featMap.length; i++) {
F feat = featureIndex.get(i);
if (counts[i] >= k) {
int newIndex = newFeatureIndex.size();
newFeatureIndex.add(feat);
featMap[i] = newIndex;
} else {
featMap[i] = -1;
}
// featureIndex.remove(feat);
}
featureIndex = newFeatureIndex;
// counts = null; // This is unnecessary; JVM can clean it up
for (int i = 0; i < size; i++) {
List<Integer> featList = new ArrayList<Integer>(data[i].length);
List<Double> valueList = new ArrayList<Double>(values[i].length);
for (int j = 0; j < data[i].length; j++) {
if (featMap[data[i][j]] >= 0) {
featList.add(featMap[data[i][j]]);
valueList.add(values[i][j]);
}
}
data[i] = new int[featList.size()];
values[i] = new double[valueList.size()];
for (int j = 0; j < data[i].length; j++) {
data[i][j] = featList.get(j);
values[i][j] = valueList.get(j);
}
}
}
示例2: applyFeatureMaxCountThreshold
import edu.stanford.nlp.util.HashIndex; //导入方法依赖的package包/类
/**
* Applies a feature max count threshold to the RVFDataset. All features that
* occur greater than <i>k</i> times are expunged.
*/
public void applyFeatureMaxCountThreshold(int k) {
float[] counts = getFeatureCounts();
HashIndex<F> newFeatureIndex = new HashIndex<F>();
int[] featMap = new int[featureIndex.size()];
for (int i = 0; i < featMap.length; i++) {
F feat = featureIndex.get(i);
if (counts[i] <= k) {
int newIndex = newFeatureIndex.size();
newFeatureIndex.add(feat);
featMap[i] = newIndex;
} else {
featMap[i] = -1;
}
// featureIndex.remove(feat);
}
featureIndex = newFeatureIndex;
// counts = null; // This is unnecessary; JVM can clean it up
for (int i = 0; i < size; i++) {
List<Integer> featList = new ArrayList<Integer>(data[i].length);
List<Double> valueList = new ArrayList<Double>(values[i].length);
for (int j = 0; j < data[i].length; j++) {
if (featMap[data[i][j]] >= 0) {
featList.add(featMap[data[i][j]]);
valueList.add(values[i][j]);
}
}
data[i] = new int[featList.size()];
values[i] = new double[valueList.size()];
for (int j = 0; j < data[i].length; j++) {
data[i][j] = featList.get(j);
values[i][j] = valueList.get(j);
}
}
}
示例3: applyFeatureMaxCountThreshold
import edu.stanford.nlp.util.HashIndex; //导入方法依赖的package包/类
/**
* Applies a max feature count threshold to the Dataset. All features that
* occur greater than <i>k</i> times are expunged.
*/
public void applyFeatureMaxCountThreshold(int k) {
float[] counts = getFeatureCounts();
HashIndex<F> newFeatureIndex = new HashIndex<F>();
int[] featMap = new int[featureIndex.size()];
for (int i = 0; i < featMap.length; i++) {
F feat = featureIndex.get(i);
if (counts[i] <= k) {
int newIndex = newFeatureIndex.size();
newFeatureIndex.add(feat);
featMap[i] = newIndex;
} else {
featMap[i] = -1;
}
// featureIndex.remove(feat);
}
featureIndex = newFeatureIndex;
// counts = null; // This is unnecessary; JVM can clean it up
for (int i = 0; i < size; i++) {
List<Integer> featList = new ArrayList<Integer>(data[i].length);
for (int j = 0; j < data[i].length; j++) {
if (featMap[data[i][j]] >= 0) {
featList.add(featMap[data[i][j]]);
}
}
data[i] = new int[featList.size()];
for (int j = 0; j < data[i].length; j++) {
data[i][j] = featList.get(j);
}
}
}