Java 类gnu.trove.iterator.TIntFloatIterator 实例源码

项目：fnlp 文件：ItemFrequency.java

private boolean oneStepStatistic(Instance inst) {
    if(inst==null)
        return false;
    int[] type;
    Object t=inst.getTarget();
    if(t instanceof Integer){
        type=new int[1];
        type[0]=Integer.parseInt(t.toString());
    }
    else{
        return false;
    }

    HashSparseVector data = (HashSparseVector) inst.getData();
    TIntFloatIterator it = data.data.iterator();
    while (it.hasNext()) {
        it.advance();
        int feature=it.key();
        for(int i=0;i<type.length;i++){
            addItemFrequency(feature, type[i], (int)it.value());
        }
    }

    return true;
}

项目：fnlp 文件：KMeansWordCluster.java

private float distanceEuclidean(int n, HashSparseVector sv, float baseDistance) {
    HashSparseVector center = classCenter.get(n);
    int count = classCount.get(n);
    float dist = baseDistance / (count * count);
    TIntFloatHashMap data = center.data;
    TIntFloatIterator it = sv.data.iterator();
    while (it.hasNext()) {
        it.advance();
        int key = it.key();
        if (!data.containsKey(key)) {
            dist += it.value() * it.value();
        }
        else {
            float temp = data.get(key) / count;
            dist -= temp * temp;
            dist += (it.value() - temp) * (it.value() - temp);
        }
    }
    return dist;
}

项目：fnlp 文件：KMeansWordCluster.java

private void updateBaseDist(int classid, HashSparseVector vector) {
    float base = baseDistList.get(classid);
    TIntFloatHashMap center = classCenter.get(classid).data;
    TIntFloatIterator it =  vector.data.iterator();
    while (it.hasNext()) {
        it.advance();
        if (!center.containsKey(it.key())) {
            base += it.value() * it.value();
        }
        else {
            float temp = center.get(it.key());
            base -= temp * temp;
            base += (it.value() - temp) * (it.value() - temp);
        }
    }
    baseDistList.set(classid, base);
}

项目：fnlp 文件：MyCollection.java

/**
 * 由大到小排序
 * @param tmap
 * @return 数组下标
 */
public static int[] sort(TIntFloatHashMap tmap) {
    HashMap<Integer, Float> map = new HashMap<Integer, Float>();

    TIntFloatIterator it = tmap.iterator();
    while (it.hasNext()) {
        it.advance();
        int id = it.key();
        float val = it.value();
        map.put(id, Math.abs(val));
    }
    it = null;

    List<Entry> list = sort(map);
    int[] idx = new int[list.size()];
    Iterator<Entry> it1 = list.iterator();
    int i=0;
    while (it1.hasNext()) {
        Entry entry = it1.next();
        idx[i++] = (Integer) entry.getKey();
    }
    return idx;
}

项目：Zeppa-AppEngine 文件：KLDivergence.java

public double compare(
        TIntFloatMap v1,
        TIntFloatMap v2,
        boolean sorted) 
{
    //it does not matter if the vectors are sorted or not

    double DKL = 0.0;

               TIntFloatIterator iter = v1.iterator();
               while (iter.hasNext())
               {
                   iter.advance();
                   int key = iter.key();
                   if (!v2.containsKey(key))
                   {
                       continue;
                   }
                   double P = iter.value();
                   double Q = v2.get(key);
                   DKL += Math.log(P/Q) * P;
               }

    return DKL;

}

项目：Zeppa-AppEngine 文件：SemSigUtils.java

public static int[] getSortedIndices(TIntFloatMap vector)
{
    // NOTE: it's probably possible to do this using purely primitive
    // operations without having to resort to pushing things into an
    // Index[].  However, this code is much cleaner to have and since we
    // sort at most once per vector and the result is memoized, we don't
    // lose too much from the Object-based sorting.
    Index[] keyValPairs = new Index[vector.size()];
    TIntFloatIterator iter = vector.iterator();
    int i = 0;
    while (iter.hasNext())
    {
            iter.advance();
            keyValPairs[i++] = new Index(iter.key(), iter.value());
    }

    Arrays.sort(keyValPairs);
    int[] sortedIndices = new int[keyValPairs.length];
    for (i = 0; i < keyValPairs.length; ++i)
        sortedIndices[i] = keyValPairs[i].key;

    return sortedIndices;
}

项目：Zeppa-AppEngine 文件：SemSigUtils.java

/**
 * Normalizes the probability values in a vector so that to sum to 1.0
 * @param vector
 * @return
 */
public static TIntFloatMap normalizeVector(TIntFloatMap vector)
{
    float total = 0;

               TFloatIterator iter = vector.valueCollection().iterator();
               while (iter.hasNext())
                      total += iter.next();

               TIntFloatMap normalized = new TIntFloatHashMap(vector.size());

               TIntFloatIterator iter2 = vector.iterator();
               while (iter2.hasNext())
               {
                       iter2.advance();
                       normalized.put(iter2.key(), iter2.value() / total);
               }        
    return normalized;
}

项目：ADW 文件：KLDivergence.java

public double compare(
        TIntFloatMap v1,
        TIntFloatMap v2,
        boolean sorted) 
{
    //it does not matter if the vectors are sorted or not

    double DKL = 0.0;

               TIntFloatIterator iter = v1.iterator();
               while (iter.hasNext())
               {
                   iter.advance();
                   int key = iter.key();
                   if (!v2.containsKey(key))
                   {
                       continue;
                   }
                   double P = iter.value();
                   double Q = v2.get(key);
                   DKL += Math.log(P/Q) * P;
               }

    return DKL;

}

项目：ADW 文件：SemSigUtils.java

public static int[] getSortedIndices(TIntFloatMap vector)
{
    // NOTE: it's probably possible to do this using purely primitive
    // operations without having to resort to pushing things into an
    // Index[].  However, this code is much cleaner to have and since we
    // sort at most once per vector and the result is memoized, we don't
    // lose too much from the Object-based sorting.
    Index[] keyValPairs = new Index[vector.size()];
    TIntFloatIterator iter = vector.iterator();
    int i = 0;
    while (iter.hasNext())
    {
            iter.advance();
            keyValPairs[i++] = new Index(iter.key(), iter.value());
    }

    Arrays.sort(keyValPairs);
    int[] sortedIndices = new int[keyValPairs.length];
    for (i = 0; i < keyValPairs.length; ++i)
        sortedIndices[i] = keyValPairs[i].key;

    return sortedIndices;
}

项目：ADW 文件：SemSigUtils.java

/**
 * Normalizes the probability values in a vector so that to sum to 1.0
 * @param vector
 * @return
 */
public static TIntFloatMap normalizeVector(TIntFloatMap vector)
{
    float total = 0;

               TFloatIterator iter = vector.valueCollection().iterator();
               while (iter.hasNext())
                      total += iter.next();

               TIntFloatMap normalized = new TIntFloatHashMap(vector.size());

               TIntFloatIterator iter2 = vector.iterator();
               while (iter2.hasNext())
               {
                       iter2.advance();
                       normalized.put(iter2.key(), iter2.value() / total);
               }        
    return normalized;
}

项目：fudannlp 文件：KMeansWordCluster.java

private float distanceEuclidean(int n, HashSparseVector sv, float baseDistance) {
    HashSparseVector center = classCenter.get(n);
    int count = classCount.get(n);
    float dist = baseDistance / (count * count);
    TIntFloatHashMap data = center.data;
    TIntFloatIterator it = sv.data.iterator();
    while (it.hasNext()) {
        it.advance();
        int key = it.key();
        if (!data.containsKey(key)) {
            dist += it.value() * it.value();
        }
        else {
            float temp = data.get(key) / count;
            dist -= temp * temp;
            dist += (it.value() - temp) * (it.value() - temp);
        }
    }
    return dist;
}

项目：fudannlp 文件：KMeansWordCluster.java

private void updateBaseDist(int classid, HashSparseVector vector) {
    float base = baseDistList.get(classid);
    TIntFloatHashMap center = classCenter.get(classid).data;
    TIntFloatIterator it =  vector.data.iterator();
    while (it.hasNext()) {
        it.advance();
        if (!center.containsKey(it.key())) {
            base += it.value() * it.value();
        }
        else {
            float temp = center.get(it.key());
            base -= temp * temp;
            base += (it.value() - temp) * (it.value() - temp);
        }
    }
    baseDistList.set(classid, base);
}

项目：fudannlp 文件：WordCluster.java

/**
 * 一次性统计概率，节约时间
 */
private void statisticProb() {
    System.out.println("统计概率");
    float totalword = alpahbet.size();
    TIntFloatIterator it = wordProb.iterator();
    while(it.hasNext()){
        it.advance();
        float v = it.value()/totalword;
        it.setValue(v);
        Cluster cluster = new Cluster(it.key(),v,alpahbet.lookupString(it.key()));
        clusters.put(it.key(), cluster);
    }

    TIntObjectIterator<TIntFloatHashMap> it1 = pcc.iterator();
    while(it1.hasNext()){
        it1.advance();
        TIntFloatHashMap map = it1.value();
        TIntFloatIterator it2 = map.iterator();
        while(it2.hasNext()){
            it2.advance();
            it2.setValue(it2.value()/totalword);
        }
    }

}

项目：fudannlp 文件：MyCollection.java

/**
 * 由大到小排序
 * @param map
 * @return 数组下标
 */
public static int[] sort(TIntFloatHashMap tmap) {
    HashMap<Integer, Float> map = new HashMap<Integer, Float>();

    TIntFloatIterator it = tmap.iterator();
    while (it.hasNext()) {
        it.advance();
        int id = it.key();
        float val = it.value();
        map.put(id, Math.abs(val));
    }
    it = null;

    List<Entry> list = sort(map);
    int[] idx = new int[list.size()];
    Iterator<Entry> it1 = list.iterator();
    int i=0;
    while (it1.hasNext()) {
        Entry entry = it1.next();
        idx[i++] = (Integer) entry.getKey();
    }
    return idx;
}

项目：fnlp 文件：FeatureSelect.java

public HashSparseVector select(HashSparseVector vec){
    HashSparseVector sv=new HashSparseVector();     
    TIntFloatIterator it=vec.data.iterator();
    while(it.hasNext()){
        it.advance();
        if(isUseful[it.key()])
            sv.put(it.key(), it.value());
    }
    return sv;
}

项目：fnlp 文件：HashSparseVector.java

@Override
public float dotProduct(float[] vector) {
    float v =0f;
    TIntFloatIterator it = data.iterator();         
    while(it.hasNext()){
        it.advance();
        v += vector[it.key()]*it.value();
    }
    return v;
}

项目：fnlp 文件：HashSparseVector.java

public float l2Norm2() {
    TIntFloatIterator it = data.iterator();
    float norm = 0f;
    while(it.hasNext()){
        it.advance();
        norm += it.value()*it.value();
    }
    return norm;
}

项目：fnlp 文件：HashSparseVector.java

/**
 * 
 * @param c
 */
public void scaleDivide(float c) {
    TIntFloatIterator it = data.iterator();
    while(it.hasNext()){
        it.advance();
        float v = it.value()/c;
        data.put(it.key(), v);
    }

}

项目：fnlp 文件：HashSparseVector.java

/**
 * 欧氏距离
 * @param sv1
 * @param sv2
 * @return
 */
public static float distanceEuclidean(HashSparseVector sv1 ,HashSparseVector sv2) {
    float dist = 0.0f;
    TIntFloatIterator it1 = sv1.data.iterator();
    TIntFloatIterator it2 = sv2.data.iterator();
    int increa = 0;
    while(it1.hasNext()&&it2.hasNext()){
        if(increa==0){
            it1.advance();
            it2.advance();
        }else if(increa==1){
            it1.advance();
        }else if(increa==2){
            it2.advance();
        }
        if(it1.key()<it2.key()){
            dist += it1.value()*it1.value();
            increa = 1;
        }else if(it1.key()>it2.key()){
            dist += it2.value()*it2.value();
            increa = 2;
        }else{
            float t = it1.value() - it2.value();
            dist += t*t;
            increa = 0;
        }
    }
    while(it1.hasNext()){
        it1.advance();
        dist += it1.value()*it1.value();            
    }
    while(it2.hasNext()){
        it2.advance();
        dist += it2.value()*it2.value();

    }
    return dist;
}

项目：fnlp 文件：HashSparseVector.java

public String toString(){
    StringBuilder sb = new StringBuilder();
    TIntFloatIterator it = data.iterator();
    while(it.hasNext()){
        it.advance();
        sb.append(it.key());
        sb.append(":");
        sb.append(it.value());
        if(it.hasNext())
            sb.append(", ");
    }
    return sb.toString();
}

项目：fnlp 文件：ModelAnalysis.java

/**
 * 统计信息，计算删除非0特征后，权重的长度
 */
public void removeZero() {
    boolean freeze = false;
    if (feature.isStopIncrement()) {
        feature.setStopIncrement(false);
        freeze = true;
    }
    TIntObjectHashMap<String> index = (TIntObjectHashMap<String>) feature.toInverseIndexMap();

    System.out.println("原字典大小"+index.size());
    System.out.println("原字典大小"+feature.size());
    StringFeatureAlphabet newfeat = new StringFeatureAlphabet();
    cl.factory.setDefaultFeatureAlphabet(newfeat);
    for(int i=0;i<weights.length;i++){
            TIntFloatIterator itt = weights[i].data.iterator();
            HashSparseVector ww = new HashSparseVector();
            while(itt.hasNext()){
                itt.advance();
                float v = itt.value();
                if(Math.abs(v)<1e-3f)
                    continue;
                String fea = index.get(itt.key());
                int newidx = newfeat.lookupIndex(fea);
                ww.put(newidx, v);              
        }
        weights[i] = ww;    
    }

    newfeat.setStopIncrement(freeze);
    System.out.println("新字典大小"+newfeat.size());     
    System.out.println("新字典大小"+feature.size());     
    index.clear();      
}

项目：fnlp 文件：BayesClassifier.java

@Override
public Predict classify(Instance instance, int n) {
    // TODO Auto-generated method stub

    int typeSize=tf.getTypeSize();
    float[] score=new float[typeSize];
    Arrays.fill(score, 0.0f);

    Object obj=instance.getData();
    if(!(obj instanceof HashSparseVector)){
        System.out.println("error 输入类型非HashSparseVector！");
        return null;
    }
    HashSparseVector data = (HashSparseVector) obj;
    if(fs!=null)
        data=fs.select(data);
    TIntFloatIterator it = data.data.iterator();
    float feaSize=tf.getFeatureSize();
    while (it.hasNext()) {
        it.advance();
        if(it.key()==0)
            continue;
        int feature=it.key();
        for(int type=0;type<typeSize;type++){
            float itemF=tf.getItemFrequency(feature, type);
            float typeF=tf.getTypeFrequency(type);
            score[type]+=it.value()*Math.log((itemF+0.1)/(typeF+feaSize));
        }
    }

    Predict<Integer> res=new Predict<Integer>(n);
    for(int type=0;type<typeSize;type++)
        res.add(type, score[type]);

    return res;
}

项目：fnlp 文件：KMeansWordCluster.java

float distanceEuclidean(HashSparseVector sv1 ,HashSparseVector sv2) {
    float dist = 0.0f;
    TIntFloatIterator it1 = sv1.data.iterator();
    TIntFloatIterator it2 = sv2.data.iterator();
    if (it1.hasNext() && it2.hasNext()) {
        it1.advance();
        it2.advance();
    }
    while (it1.hasNext() && it2.hasNext()) {
        if(it1.key()<it2.key()){
            dist += it1.value()*it1.value();
            it1.advance();
        }else if(it1.key()>it2.key()){
            dist += it2.value()*it2.value();
            it2.advance();
        }else{
            float t = it1.value() - it2.value();
            dist += t*t;
            it1.advance();
            it2.advance();
        }
    }
    while (it1.hasNext()) {
        it1.advance();
        dist += it1.value() * it1.value();
    }
    while (it2.hasNext()) {
        it2.advance();
        dist += it2.value() * it2.value();
    }
    return dist;
}

项目：fnlp 文件：KMeansWordCluster.java

private float getBaseDist(int classid) {
    float base = 0.0f;
    TIntFloatIterator it = classCenter.get(classid).data.iterator();
    while (it.hasNext()) {
        it.advance();
        base += it.value() * it.value();
    }
    return base;
}

项目：fnlp 文件：WordCluster.java

/**
 * 一次性统计概率，节约时间
 */
private void statisticProb() {
    System.out.println("统计概率");
    TIntFloatIterator it = wordProb.iterator();
    while(it.hasNext()){
        it.advance();
        float v = it.value()/totalword;
        it.setValue(v);
        int key = it.key();
        if(key<0)
            continue;
        Cluster cluster = new Cluster(key,v,alpahbet.lookupString(key));
        clusters.put(key, cluster);
    }

    TIntObjectIterator<TIntFloatHashMap> it1 = pcc.iterator();
    while(it1.hasNext()){
        it1.advance();
        TIntFloatHashMap map = it1.value();
        TIntFloatIterator it2 = map.iterator();
        while(it2.hasNext()){
            it2.advance();
            it2.setValue(it2.value()/totalword);
        }
    }

}

项目：fnlp 文件：TFIDF.java

@Override
public void addThruPipe(Instance inst) {
    HashSparseVector data = (HashSparseVector) inst.getData();
    TIntFloatIterator it = data.data.iterator();
    while (it.hasNext()) {
        it.advance();
        int id = it.key();
        if (idf[id] > 0) {
            float value = (float) (it.value()*Math.log(docNum / idf[id]));
            data.put(id, value);
        }
    }

}

项目：fnlp 文件：TF2IDF.java

@Override
public void addThruPipe(Instance inst) {
    HashSparseVector data = (HashSparseVector) inst.getData();
    TIntFloatIterator it = data.data.iterator();
    while (it.hasNext()) {
        it.advance();
        idf[it.key()]++;
    }
}