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  3. gnu.trove.map.hash.TIntFloatHashMap

Java 类gnu.trove.map.hash.TIntFloatHashMap 实例源码

项目：lodreclib    文件：Evaluator.java    
private void computeItemSim() {

        List<Integer> sortedItems = new ArrayList<Integer>();
        sortedItems.addAll(items);
        Collections.sort(sortedItems);
        int id1, id2;
        for (int i = 0; i < sortedItems.size() - 1; i++) {
            id1 = sortedItems.get(i);
            this.itemSim.put(id1, new TIntFloatHashMap());
            for (int j = i + 1; j < sortedItems.size(); j++) {
                id2 = sortedItems.get(j);
                float val = 0;
                if (map_item_intFeatures.containsKey(id1)
                        & map_item_intFeatures.containsKey(id2))
                    val = cmpJaccardSim(this.map_item_intFeatures.get(id1)
                            .keySet(), this.map_item_intFeatures.get(id2)
                            .keySet());

                if (val > MIN_SIM) {
                    itemSim.get(id1).put(id2, val);
                }
            }

        }
    }
项目：wikit    文件：SimUtils.java    
/**
 * Normalize a vector to unit length.
 * @param X
 * @return
 */
public static TIntFloatMap normalizeVector(TIntFloatMap X) {
    TIntFloatHashMap Y = new TIntFloatHashMap();
    double sumSquares = 0.0;
    for (double x : X.values()) {
        sumSquares += x * x;
    }
    if (sumSquares != 0.0) {
        double norm = Math.sqrt(sumSquares);
        for (int id : X.keys()) {
            Y.put(id, (float) (X.get(id) / norm));
        }
        return Y;
    }
    return X;
}
项目：openimaj    文件：HoughCircles.java    
/**
 * Construct with the given parameters.
 * 
 * @param minRad
 *            minimum search radius
 * @param maxRad
 *            maximum search radius
 * @param radIncrement
 *            amount to increment search radius by between min and max.
 * @param nDegree
 *            number of degree increments
 */
public HoughCircles(int minRad, int maxRad, int radIncrement, int nDegree) {
    super();
    this.minRad = minRad;
    if (this.minRad <= 0)
        this.minRad = 1;
    this.maxRad = maxRad;
    this.radmap = new TIntObjectHashMap<TIntObjectHashMap<TIntFloatHashMap>>();
    this.radIncr = radIncrement;
    this.nRadius = (maxRad - minRad) / this.radIncr;
    this.nDegree = nDegree;
    this.cosanglemap = new float[nRadius][nDegree];
    this.sinanglemap = new float[nRadius][nDegree];
    for (int radIndex = 0; radIndex < this.nRadius; radIndex++) {
        for (int angIndex = 0; angIndex < nDegree; angIndex++) {
            final double ang = angIndex * (2 * PI / nDegree);
            final double rad = minRad + (radIndex * this.radIncr);
            this.cosanglemap[radIndex][angIndex] = (float) (rad * cos(ang));
            this.sinanglemap[radIndex][angIndex] = (float) (rad * sin(ang));
        }
    }
}
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：WordCluster.java    
private float getweight(int c1, int c2) {
    int max,min;
    if(c1<=c2){
        max = c2;
        min = c1;
    }else{
        max = c1;
        min = c2;
    }
    float w;
    TIntFloatHashMap map2 = wcc.get(min);
    if(map2==null){
        w = 0;
    }else
        w = map2.get(max);
    return w;
}
项目：fnlp    文件：MyArrays.java    
/**
 * 得到总能量值大于thres的元素对应的下标
 * 
 * @param data 稀疏向量
 * @param thres
 * @return 元素下标 int[][] 第一列表示大于阈值的元素 第二列表示小于阈值的元素
 */
public static int[][] getTop(TIntFloatHashMap data, float thres) {
    int[] idx = sort(data);
    int i;
    float total = 0;
    float[] cp = new float[idx.length];
    for (i = idx.length; i-- > 0;) {
        cp[i] = (float) Math.pow(data.get(idx[i]), 2);
        total += cp[i];
    }

    float ratio = 0;
    for (i = 0; i < idx.length; i++) {
        ratio += cp[i] / total;
        if (ratio > thres)
            break;
    }
    int[][] a = new int[2][];
    a[0] = Arrays.copyOfRange(idx, 0, i);
    a[1] = Arrays.copyOfRange(idx, i, idx.length);
    return a;
}
项目：fnlp    文件：MyHashSparseArrays.java    
/**
 * 得到总能量值大于thres的元素对应的下标
 * 
 * @param data 稀疏向量
 * @param thres
 * @return 元素下标 int[][] 第一列表示大于阈值的元素 第二列表示小于阈值的元素
 */
public static int[][] getTop(TIntFloatHashMap data, float thres) {
    int[] idx = sort(data);
    int i;
    float total = 0;
    float[] cp = new float[idx.length];
    for (i = idx.length; i-- > 0;) {
        cp[i] = (float) Math.pow(data.get(idx[i]), 2);
        total += cp[i];
    }

    float ratio = 0;
    for (i = 0; i < idx.length; i++) {
        ratio += cp[i] / total;
        if (ratio > thres)
            break;
    }
    int[][] a = new int[2][];
    a[0] = Arrays.copyOfRange(idx, 0, i);
    a[1] = Arrays.copyOfRange(idx, i, idx.length);
    return a;
}
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：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：lodreclib    文件：Evaluator.java    
private void computeItemSim() {

        List<Integer> sortedItems = new ArrayList<Integer>();
        sortedItems.addAll(items);
        Collections.sort(sortedItems);
        int id1, id2;
        for (int i = 0; i < sortedItems.size() - 1; i++) {
            id1 = sortedItems.get(i);
            this.itemSim.put(id1, new TIntFloatHashMap());
            for (int j = i + 1; j < sortedItems.size(); j++) {
                id2 = sortedItems.get(j);
                float val = 0;
                if (map_item_intFeatures.containsKey(id1)
                        & map_item_intFeatures.containsKey(id2))
                    val = cmpJaccardSim(this.map_item_intFeatures.get(id1)
                            .keySet(), this.map_item_intFeatures.get(id2)
                            .keySet());

                if (val > MIN_SIM) {
                    itemSim.get(id1).put(id2, val);
                }
            }

        }
    }
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：ADW    文件：SemSigUtilsTest.java    
@Test
public void testGetSortedIndices() 
{
           TIntFloatMap m = new TIntFloatHashMap();
           m.put(0, 1f);
           m.put(1, 10f);
           m.put(2, 5f);
           m.put(3, 2f);

           int[] sorted = SemSigUtils.getSortedIndices(m);
           assertEquals(4, sorted.length);
           assertEquals(1, sorted[0]);
           assertEquals(2, sorted[1]);
           assertEquals(3, sorted[2]);
           assertEquals(0, sorted[3]);
       }
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：WordCluster.java    
private float getweight(int c1, int c2) {
    int max,min;
    if(c1<=c2){
        max = c2;
        min = c1;
    }else{
        max = c1;
        min = c2;
    }
    float w;
    TIntFloatHashMap map2 = wcc.get(min);
    if(map2==null){
        w = 0;
    }else
        w = map2.get(max);
    return w;
}
项目：fudannlp    文件：MyArrays.java    
/**
 * 得到总能量值大于thres的元素对应的下标
 * 
 * @param data 稀疏向量
 * @param thres
 * @return 元素下标 int[][] 第一列表示大于阈值的元素 第二列表示小于阈值的元素
 */
public static int[][] getTop(TIntFloatHashMap data, float thres) {
    int[] idx = sort(data);
    int i;
    float total = 0;
    float[] cp = new float[idx.length];
    for (i = idx.length; i-- > 0;) {
        cp[i] = (float) Math.pow(data.get(idx[i]), 2);
        total += cp[i];
    }

    float ratio = 0;
    for (i = 0; i < idx.length; i++) {
        ratio += cp[i] / total;
        if (ratio > thres)
            break;
    }
    int[][] a = new int[2][];
    a[0] = Arrays.copyOfRange(idx, 0, i);
    a[1] = Arrays.copyOfRange(idx, i, idx.length);
    return a;
}
项目：fudannlp    文件：MyHashSparseArrays.java    
/**
 * 得到总能量值大于thres的元素对应的下标
 * 
 * @param data 稀疏向量
 * @param thres
 * @return 元素下标 int[][] 第一列表示大于阈值的元素 第二列表示小于阈值的元素
 */
public static int[][] getTop(TIntFloatHashMap data, float thres) {
    int[] idx = sort(data);
    int i;
    float total = 0;
    float[] cp = new float[idx.length];
    for (i = idx.length; i-- > 0;) {
        cp[i] = (float) Math.pow(data.get(idx[i]), 2);
        total += cp[i];
    }

    float ratio = 0;
    for (i = 0; i < idx.length; i++) {
        ratio += cp[i] / total;
        if (ratio > thres)
            break;
    }
    int[][] a = new int[2][];
    a[0] = Arrays.copyOfRange(idx, 0, i);
    a[1] = Arrays.copyOfRange(idx, i, idx.length);
    return a;
}
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：artista    文件：HoughCircles.java    
/**
 * Construct with the given parameters.
 *
 * @param minRad minimum search radius
 * @param maxRad maximum search radius
 */
public HoughCircles(int minRad, int maxRad, int radIncrement, int nDegree) {
    super();
    this.minRad = minRad;
    if(this.minRad <= 0) this.minRad = 1;
    this.maxRad = maxRad;
    this.radmap = new TIntObjectHashMap<TIntObjectHashMap<TIntFloatHashMap>>();
    this.radIncr = radIncrement;
    this.nRadius = (maxRad-minRad) / this.radIncr;
    this.nDegree = nDegree;
    this.cosanglemap = new float[nRadius][nDegree];
    this.sinanglemap = new float[nRadius][nDegree];
    for (int radIndex=0; radIndex<this.nRadius; radIndex++) {
        for (int angIndex=0; angIndex<nDegree; angIndex++) {
            double ang = angIndex * (2 * PI / nDegree);
            double rad = minRad + (radIndex * this.radIncr);
            this.cosanglemap [radIndex][angIndex] = (float) (rad*cos(ang));
            this.sinanglemap [radIndex][angIndex] = (float) (rad*sin(ang));
        }
    }
}
项目：lodreclib    文件：ItemPreProcessing.java    
private void writeData(String filename) {
    try {
        BufferedWriter writer = new BufferedWriter(new FileWriter(filename));
        TIntFloatHashMap m;
        StringBuffer buf;
        for (int id : items) {

            buf = new StringBuffer();
            buf.append(id + "\t");
            m = map_item_intFeatures.get(id);
            int[] fIDs = m.keys();
            Arrays.sort(fIDs);
            for (int i = 0; i < fIDs.length; i++) {
                buf.append(fIDs[i] + ":" + m.get(fIDs[i]) + " ");
            }
            writer.append(buf);
            writer.newLine();

        }

        writer.flush();
        writer.close();

    } catch (IOException ex) {
        System.out.println(ex.getMessage());
    }
}
项目：lodreclib    文件：UserModelRecommenderWorker.java    
public UserModelRecommenderWorker(int u, BufferedWriter bw,
        TIntObjectHashMap<TIntFloatHashMap> map_item_intFeatures,
        Evaluator trainEval, Evaluator validEval, boolean silent, int topN,
        int num_features, List<Double> listC, List<Double> listEps,
        List<Integer> listSolverType, Map<Integer, Float> userTrainRatings,
        Map<Integer, Float> userValRatings, boolean implicit,
        int nValidNegEx, boolean addNegValidationEx, int timesRealFb, int minTrainEx,
        HashSet<Integer> items, float relUnknownItems, int topK, String metric) {

    this.topK = topK;
    this.metric = metric;
    this.u = u;
    this.bw = bw;
    this.map_item_intFeatures = map_item_intFeatures;
    this.trainEval = trainEval;
    this.validEval = validEval;
    this.silent = silent;
    this.topN = topN;
    this.num_features = num_features;
    this.listC = listC;
    this.listEps = listEps;
    this.listSolverType = listSolverType;
    this.userTrainRatings = userTrainRatings;
    this.userValRatings = userValRatings;
    this.implicit = implicit;
    this.relUnknownItems = relUnknownItems;
    this.nValidNegEx = nValidNegEx;
    this.timesRealFb = timesRealFb;
    this.minTrainEx = minTrainEx;
    this.items = items;
    this.addNegValidationEx = addNegValidationEx;
    originalTrainItems = new HashSet<Integer>();

}
项目：lodreclib    文件：UserProfileSimilarityRecommenderWorker.java    
public UserProfileSimilarityRecommenderWorker(int u,
        HashSet<Integer> items, BufferedWriter bw,
        TIntObjectHashMap<TIntFloatHashMap> map_item_intFeatures, int topN,
        Map<Integer, Float> userTrainRatings, boolean implicit,
        Float evalRatingThresh) {

    this.u = u;
    this.items = items;
    this.bw = bw;
    this.map_item_intFeatures = map_item_intFeatures;
    this.topN = topN;
    this.userTrainRatings = userTrainRatings;
    this.implicit = implicit;
    this.evalRatingThresh = evalRatingThresh;
}
项目：lodreclib    文件：UserProfileSimilarityRecommenderWorker.java    
private float cmpCosineSim(TIntFloatHashMap v1, TIntFloatHashMap v2) {

        TIntHashSet inters = new TIntHashSet();
        inters.addAll(v1.keySet());
        inters.retainAll(v2.keySet());

        if (inters.size() == 0)
            return 0;
        else {
            int i = 0;
            TIntIterator it = inters.iterator();
            float num = 0;
            float norm_v1 = 0;
            float norm_v2 = 0;
            while (it.hasNext()) {
                i = it.next();
                num += v1.get(i) * v2.get(i);
            }
            for (int k1 : v1.keys())
                norm_v1 += (v1.get(k1) * v1.get(k1));
            for (int k2 : v2.keys())
                norm_v2 += (v2.get(k2) * v2.get(k2));
            return num / (float) (Math.sqrt(norm_v1) * Math.sqrt(norm_v2));

        }

    }
项目：lodreclib    文件：Evaluator.java    
private void loadItemFeatureData(String file_name) {

        BufferedReader br;
        try {
            br = new BufferedReader(new FileReader(file_name));
            String line = null;
            int count = 0;
            while ((line = br.readLine()) != null) {
                try {
                    String[] vals = line.split("\t");
                    int id = Integer.parseInt(vals[0]);

                    if (items.contains(id)) {

                        map_item_intFeatures.put(id, new TIntFloatHashMap());

                        String[] values = vals[1].trim().split(" ");
                        for (int i = 0; i < values.length; i++) {
                            String[] pair = values[i].split(":");
                            int fId = Integer.parseInt(pair[0]);
                            float fVal = Float.parseFloat(pair[1]);
                            map_item_intFeatures.get(id).put(fId, fVal);
                        }
                        count++;
                    }
                } catch (Exception ex) {
                    // System.out.println(ex.getMessage());
                    // System.out.println(line);
                }
            }
            logger.info("item metadata loaded for evaluation - " + count
                    + " items");
            br.close();
        } catch (IOException e) {
            e.printStackTrace();
        }

    }
项目：lodreclib    文件：TextFileUtils.java    
public static TIntObjectHashMap<TIntFloatHashMap> loadInputUsersRatings(String file) {
    TIntObjectHashMap<TIntFloatHashMap> user_rating =new TIntObjectHashMap<TIntFloatHashMap>();
    try {
        BufferedReader br = new BufferedReader(new FileReader(file));
        String line = null;
        float rate;
        int user_id, item_id;

        while ((line = br.readLine()) != null) {
            String[] vals = line.split("\t");

            if(vals.length==2)
                rate=1;
            else
                rate = Float.parseFloat(vals[2]);

            user_id = Integer.parseInt(vals[0]);
            item_id = Integer.parseInt(vals[1]);

            user_rating.putIfAbsent(user_id, new TIntFloatHashMap());
            user_rating.get(user_id).put(item_id, rate);

        }

        br.close();
    } catch (Exception e) {
        e.printStackTrace();
    }
    return user_rating;
}
项目：lodreclib    文件：TextFileUtils.java    
public static void loadInputUsersRatings(String file, 
        TIntObjectHashMap<TIntFloatHashMap> user_rating, TFloatHashSet labels){

    try{
        BufferedReader br = new BufferedReader(new FileReader(file));
        String line = null;
        float rate;
        int user_id, item_id;

        while((line=br.readLine()) != null){
            String[] vals = line.split("\t");
            rate = Float.parseFloat(vals[2]);
            user_id = Integer.parseInt(vals[0]);
            item_id = Integer.parseInt(vals[1]);

            user_rating.putIfAbsent(user_id, new TIntFloatHashMap());
            user_rating.get(user_id).put(item_id, rate);
            labels.add(rate);

        }

        br.close();
    }
    catch(Exception e){
        e.printStackTrace();
    }

}
项目：lodreclib    文件：UserPathExtractorWorker.java    
/**
 * Constuctor
 */
public UserPathExtractorWorker(int user_id, TIntFloatHashMap trainRatings,
                               TIntFloatHashMap validationRatings, ArrayList<String> items_id,
                               BufferedWriter train_file, BufferedWriter validation_file,
                               BufferedWriter test_file, boolean normalize,
                               THashMap<String, String> items_path_index, String path_file,
                               TObjectIntHashMap<String> path_index, THashMap<String, String> paths,
                               int user_items_sampling, float ratesThreshold,
                               TIntObjectHashMap<TIntHashSet> items_link) {

    this.user_id = user_id;
    this.items_id = items_id;
    this.trainRatings = trainRatings;
    this.validationRatings = validationRatings;
    this.train_file = train_file;
    this.validation_file = validation_file;
    this.test_file = test_file;
    this.normalize = normalize;
    this.items_path_index = items_path_index;
    this.path_index = path_index;
    this.paths = paths;
    this.path_file = path_file;
    this.user_items_sampling = user_items_sampling;
    this.ratesThreshold = ratesThreshold;
    this.items_link = items_link;

}
项目：wikit    文件：LinkRelatedness.java    
private TIntFloatMap makeOutlinkVector(TIntSet links) {
    TIntFloatMap vector = new TIntFloatHashMap();
    for (int wpId : links.toArray()) {
        vector.put(wpId, (float) Math.log(1.0 * linkCache.getTotalPages() / linkCache
                                          .getInlinks(wpId).size()));
    }

    if(wlmExtended) {
        TIntFloatMap vector2 = new TIntFloatHashMap();
        //考虑二级链接
        for (int id1 : links.toArray()) {
            for (int id2 : linkCache.getOutlinks(id1).toArray()) {
                double tfidf = Math.log(1.0 * linkCache.getTotalPages()
                        / linkCache.getInlinks(id2).size());
                float w = (float) tfidf * vector.get(id1);

                float old = 0;
                if (vector2.containsKey(id2)) {
                    old = vector2.get(id2);
                }
                vector2.put(id2, old + w);
            }
        }
        vector.putAll(vector2);
    }
    return vector;
}
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：WordCluster.java    
private float getProb(int c1, int c2) {
    float p;
    TIntFloatHashMap map = pcc.get(c1);
    if(map == null){
        p = 0f;
    }else{
        p = pcc.get(c1).get(c2);                        
    }
    return p;
}
项目：lodreclib    文件：ItemPreProcessing.java    
private void writeData(String filename) {
    try {
        BufferedWriter writer = new BufferedWriter(new FileWriter(filename));
        TIntFloatHashMap m;
        StringBuffer buf;
        for (int id : items) {

            buf = new StringBuffer();
            buf.append(id + "\t");
            m = map_item_intFeatures.get(id);
            int[] fIDs = m.keys();
            Arrays.sort(fIDs);
            for (int i = 0; i < fIDs.length; i++) {
                buf.append(fIDs[i] + ":" + m.get(fIDs[i]) + " ");
            }
            writer.append(buf);
            writer.newLine();

        }

        writer.flush();
        writer.close();

    } catch (IOException ex) {
        System.out.println(ex.getMessage());
    }
}
项目：lodreclib    文件：UserModelRecommenderWorker.java    
public UserModelRecommenderWorker(int u, BufferedWriter bw,
        TIntObjectHashMap<TIntFloatHashMap> map_item_intFeatures,
        Evaluator trainEval, Evaluator validEval, boolean silent, int topN,
        int num_features, List<Double> listC, List<Double> listEps,
        List<Integer> listSolverType, Map<Integer, Float> userTrainRatings,
        Map<Integer, Float> userValRatings, boolean implicit,
        int nValidNegEx, boolean addNegValidationEx, int timesRealFb, int minTrainEx,
        HashSet<Integer> items, float relUnknownItems, int topK, String metric) {

    this.topK = topK;
    this.metric = metric;
    this.u = u;
    this.bw = bw;
    this.map_item_intFeatures = map_item_intFeatures;
    this.trainEval = trainEval;
    this.validEval = validEval;
    this.silent = silent;
    this.topN = topN;
    this.num_features = num_features;
    this.listC = listC;
    this.listEps = listEps;
    this.listSolverType = listSolverType;
    this.userTrainRatings = userTrainRatings;
    this.userValRatings = userValRatings;
    this.implicit = implicit;
    this.relUnknownItems = relUnknownItems;
    this.nValidNegEx = nValidNegEx;
    this.timesRealFb = timesRealFb;
    this.minTrainEx = minTrainEx;
    this.items = items;
    this.addNegValidationEx = addNegValidationEx;
    originalTrainItems = new HashSet<Integer>();

}
项目：lodreclib    文件：UserProfileSimilarityRecommenderWorker.java    
public UserProfileSimilarityRecommenderWorker(int u,
        HashSet<Integer> items, BufferedWriter bw,
        TIntObjectHashMap<TIntFloatHashMap> map_item_intFeatures, int topN,
        Map<Integer, Float> userTrainRatings, boolean implicit,
        Float evalRatingThresh) {

    this.u = u;
    this.items = items;
    this.bw = bw;
    this.map_item_intFeatures = map_item_intFeatures;
    this.topN = topN;
    this.userTrainRatings = userTrainRatings;
    this.implicit = implicit;
    this.evalRatingThresh = evalRatingThresh;
}
项目：lodreclib    文件：UserProfileSimilarityRecommenderWorker.java    
private float cmpCosineSim(TIntFloatHashMap v1, TIntFloatHashMap v2) {

        TIntHashSet inters = new TIntHashSet();
        inters.addAll(v1.keySet());
        inters.retainAll(v2.keySet());

        if (inters.size() == 0)
            return 0;
        else {
            int i = 0;
            TIntIterator it = inters.iterator();
            float num = 0;
            float norm_v1 = 0;
            float norm_v2 = 0;
            while (it.hasNext()) {
                i = it.next();
                num += v1.get(i) * v2.get(i);
            }
            for (int k1 : v1.keys())
                norm_v1 += (v1.get(k1) * v1.get(k1));
            for (int k2 : v2.keys())
                norm_v2 += (v2.get(k2) * v2.get(k2));
            return num / (float) (Math.sqrt(norm_v1) * Math.sqrt(norm_v2));

        }

    }
项目：lodreclib    文件：Evaluator.java    
private void loadItemFeatureData(String file_name) {

        BufferedReader br;
        try {
            br = new BufferedReader(new FileReader(file_name));
            String line = null;
            int count = 0;
            while ((line = br.readLine()) != null) {
                try {
                    String[] vals = line.split("\t");
                    int id = Integer.parseInt(vals[0]);

                    if (items.contains(id)) {

                        map_item_intFeatures.put(id, new TIntFloatHashMap());

                        String[] values = vals[1].trim().split(" ");
                        for (int i = 0; i < values.length; i++) {
                            String[] pair = values[i].split(":");
                            int fId = Integer.parseInt(pair[0]);
                            float fVal = Float.parseFloat(pair[1]);
                            map_item_intFeatures.get(id).put(fId, fVal);
                        }
                        count++;
                    }
                } catch (Exception ex) {
                    // System.out.println(ex.getMessage());
                    // System.out.println(line);
                }
            }
            logger.info("item metadata loaded for evaluation - " + count
                    + " items");
            br.close();
        } catch (IOException e) {
            e.printStackTrace();
        }

    }
项目：lodreclib    文件：TextFileUtils.java    
public static TIntObjectHashMap<TIntFloatHashMap> loadInputUsersRatings(String file) {
    TIntObjectHashMap<TIntFloatHashMap> user_rating =new TIntObjectHashMap<TIntFloatHashMap>();
    try {
        BufferedReader br = new BufferedReader(new FileReader(file));
        String line = null;
        float rate;
        int user_id, item_id;

        while ((line = br.readLine()) != null) {
            String[] vals = line.split("\t");

            if(vals.length==2)
                rate=1;
            else
                rate = Float.parseFloat(vals[2]);

            user_id = Integer.parseInt(vals[0]);
            item_id = Integer.parseInt(vals[1]);

            user_rating.putIfAbsent(user_id, new TIntFloatHashMap());
            user_rating.get(user_id).put(item_id, rate);

        }

        br.close();
    } catch (Exception e) {
        e.printStackTrace();
    }
    return user_rating;
}
项目：lodreclib    文件：TextFileUtils.java    
public static void loadInputUsersRatings(String file, 
        TIntObjectHashMap<TIntFloatHashMap> user_rating, TFloatHashSet labels){

    try{
        BufferedReader br = new BufferedReader(new FileReader(file));
        String line = null;
        float rate;
        int user_id, item_id;

        while((line=br.readLine()) != null){
            String[] vals = line.split("\t");
            rate = Float.parseFloat(vals[2]);
            user_id = Integer.parseInt(vals[0]);
            item_id = Integer.parseInt(vals[1]);

            user_rating.putIfAbsent(user_id, new TIntFloatHashMap());
            user_rating.get(user_id).put(item_id, rate);
            labels.add(rate);

        }

        br.close();
    }
    catch(Exception e){
        e.printStackTrace();
    }

}
项目：lodreclib    文件：UserPathExtractorWorker.java    
/**
 * Constuctor
 */
public UserPathExtractorWorker(int user_id, TIntFloatHashMap trainRatings,
                               TIntFloatHashMap validationRatings, ArrayList<String> items_id,
                               BufferedWriter train_file, BufferedWriter validation_file,
                               BufferedWriter test_file, boolean normalize,
                               THashMap<String, String> items_path_index, String path_file,
                               TObjectIntHashMap<String> path_index, THashMap<String, String> paths,
                               int user_items_sampling, float ratesThreshold,
                               TIntObjectHashMap<TIntHashSet> items_link) {

    this.user_id = user_id;
    this.items_id = items_id;
    this.trainRatings = trainRatings;
    this.validationRatings = validationRatings;
    this.train_file = train_file;
    this.validation_file = validation_file;
    this.test_file = test_file;
    this.normalize = normalize;
    this.items_path_index = items_path_index;
    this.path_index = path_index;
    this.paths = paths;
    this.path_file = path_file;
    this.user_items_sampling = user_items_sampling;
    this.ratesThreshold = ratesThreshold;
    this.items_link = items_link;

}