Package gnu.trove.map.hash

Examples of gnu.trove.map.hash.TObjectDoubleHashMap$TDoubleValueCollection$TObjectDoubleValueHashIterator

58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
       /////
       String line = value.toString();
       // id gold_label injected_labels estimated_labels neighbors rw_probabilities
       String[] fields = line.split(_kDelim);      
       TObjectDoubleHashMap neighbors = CollectionUtil.String2Map(fields[4]);
             
       boolean isSeedNode = fields[2].length() > 0 ? true : false;
      
       // If the current node is a seed node but there is no
       // estimate label information yet, then transfer the seed label
       // to the estimated label distribution. Ideally, this is likely
       // to be used in the map of the very first iteration.
       if (isSeedNode && fields[3].length() == 0) {
         fields[3] = fields[2];
       }

       // Send two types of messages:
       //   -- self messages which will store the injection labels and
       //        random walk probabilities.
       //   -- messages to neighbors about current estimated scores
       //        of the node.
       //
       // message to self
       output.collect(new Text(fields[0]), new Text(line));

       // message to neighbors
       TObjectDoubleIterator neighIterator = neighbors.iterator();
       while (neighIterator.hasNext()) {
         neighIterator.advance();
        
         // message (neighbor_node, current_node + DELIM + curr_node_label_scores
         output.collect(new Text((String) neighIterator.key()),
View Full Code Here
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
 
  public static void Split(Graph g, double trainFract) {   
    Random r = new Random(_kDeterministicSeed);
    // Random r = new Random();
   
    TObjectDoubleHashMap instanceVertices = new TObjectDoubleHashMap();
    Iterator vIter = g.vertices().keySet().iterator();
    while (vIter.hasNext()) {
      Vertex v = g.vertices().get(vIter.next());
     
      // nodes without feature prefix and those with at least one
      // gold labels are considered valid instances
      if (!v.name().startsWith(Constants.GetFeatPrefix()) &&
          v.goldLabels().size() > 0) {
        instanceVertices.put(v, r.nextDouble());
      }
    }
   
    ArrayList<ObjectDoublePair> sortedRandomInstances =
      CollectionUtil.ReverseSortMap(instanceVertices);
View Full Code Here
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
   }
  
     public void reduce(Text key, Iterator<Text> values,
               OutputCollector<Text, Text> output, Reporter reporter) throws IOException {      
       // new scores estimated for the current node
       TObjectDoubleHashMap newEstimatedScores = new TObjectDoubleHashMap();
      
       // set to true only if the message sent to itself is found.
       boolean isSelfMessageFound = false;
      
       String vertexId = key.toString();
       String vertexString = "";
      
       TObjectDoubleHashMap neighbors = null;
       TObjectDoubleHashMap randWalkProbs = null;
      
       HashMap<String, String> neighScores =
                 new HashMap<String, String>();
      
       int totalMessagesReceived = 0;
      
       // iterate over all the messages received at the node
       while (values.hasNext()) {
         ++totalMessagesReceived;

         String val = values.next().toString();
         String[] fields = val.split(_kDelim);
        
         // System.out.println("src: " + fields[0] + " dest: " + vertexId +
         //       "MESSAGE>>" + val + "<<");

         // self-message check
         if (vertexId.equals(fields[0])) {
           isSelfMessageFound = true;
           vertexString = val;
          
           // System.out.println("Reduce: " + vertexId + " " + val + " " + fields.length);

           TObjectDoubleHashMap injLabels = CollectionUtil.String2Map(fields[2]);
           neighbors = CollectionUtil.String2Map(neighbors, fields[4]);
           randWalkProbs = CollectionUtil.String2Map(fields[5]);
          
           if (injLabels.size() > 0) {          
             // add injected labels to the estimated scores.
             ProbUtil.AddScores(newEstimatedScores,
                        mu1 * randWalkProbs.get(Constants._kInjProb),
                        injLabels);
           }
         } else {
           // an empty second field represents that the
           // neighbor has no valid label assignment yet.
           if (fields.length > 1) {
             neighScores.put(fields[0], fields[1]);
           }
         }
       }

       // terminate if message from self is not received.
       if (!isSelfMessageFound) {
         throw new RuntimeException("Self message not received for node " + vertexId);
       }
      
       // collect neighbors label distributions and create one single
       // label distribution
       TObjectDoubleHashMap weightedNeigLablDist = new TObjectDoubleHashMap();
       Iterator<String> neighIter = neighScores.keySet().iterator();
       while (neighIter.hasNext()) {
         String neighName = neighIter.next();
         ProbUtil.AddScores(weightedNeigLablDist, // newEstimatedScores,
                   mu2 * randWalkProbs.get(Constants._kContProb) * neighbors.get(neighName),
 
View Full Code Here
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
       /////
       String line = value.toString();
       // id gold_label injected_labels estimated_labels neighbors rw_probabilities
       String[] fields = line.split(_kDelim);      
       TObjectDoubleHashMap neighbors = CollectionUtil.String2Map(fields[4]);
      
       boolean isSeedNode = fields[2].length() > 0 ? true : false;
      
       // If the current node is a seed node but there is no
       // estimate label information yet, then transfer the seed label
       // to the estimated label distribution. Ideally, this is likely
       // to be used in the map of the very first iteration.
       if (isSeedNode && fields[3].length() == 0) {
         fields[3] = fields[2];
       }

       // Send two types of messages:
       //   -- self messages which will store the injection labels and
       //        random walk probabilities.
       //   -- messages to neighbors about current estimated scores
       //        of the node.
       //
       // message to self
       output.collect(new Text(fields[0]), new Text(line));

       // message to neighbors
       TObjectDoubleIterator neighIterator = neighbors.iterator();
       while (neighIterator.hasNext()) {
         neighIterator.advance();
        
         // message (neighbor_node, current_node + DELIM + curr_node_label_scores
         output.collect(new Text((String) neighIterator.key()),
View Full Code Here
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
   }
  
     public void reduce(Text key, Iterator<Text> values,
               OutputCollector<Text, Text> output, Reporter reporter) throws IOException {      
       // new scores estimated for the current node
       TObjectDoubleHashMap newEstimatedScores = new TObjectDoubleHashMap();
      
       // set to true only if the message sent to itself is found.
       boolean isSelfMessageFound = false;
      
       String vertexId = key.toString();
       String vertexString = "";
      
       TObjectDoubleHashMap neighbors = null;
       TObjectDoubleHashMap randWalkProbs = null;
      
       HashMap<String, String> neighScores =
                 new HashMap<String, String>();
      
       int totalMessagesReceived = 0;
       boolean isSeedNode = false;
      
       // iterate over all the messages received at the node
       while (values.hasNext()) {
         ++totalMessagesReceived;

         String val = values.next().toString();
         String[] fields = val.split(_kDelim);
        
         // System.out.println("src: " + fields[0] + " dest: " + vertexId +
         //       "MESSAGE>>" + val + "<<");

         // self-message check
         if (vertexId.equals(fields[0])) {
           isSelfMessageFound = true;
           vertexString = val;
          
           // System.out.println("Reduce: " + vertexId + " " + val + " " + fields.length);

           TObjectDoubleHashMap injLabels = CollectionUtil.String2Map(fields[2]);
           neighbors = CollectionUtil.String2Map(neighbors, fields[4]);
           randWalkProbs = CollectionUtil.String2Map(fields[5]);
          
           if (injLabels.size() > 0) {
             isSeedNode = true;

             // add injected labels to the estimated scores.
             ProbUtil.AddScores(newEstimatedScores,
                            mu1, injLabels);
           }
         } else {
           // an empty second field represents that the
           // neighbor has no valid label assignment yet.
           if (fields.length > 1) {
             neighScores.put(fields[0], fields[1]);
           }
         }
       }

       // terminate if message from self is not received.
       if (!isSelfMessageFound) {
         throw new RuntimeException("Self message not received for node " + vertexId);
       }
      
       // Add neighbor label scores to current node's label estimates only if the
       // current node is not a seed node. In case of seed nodes, clamp back the
       // injected label distribution, which is already done above when processing
       // the self messages
       if (!isSeedNode) {
         // collect neighbors label distributions and create one single
         // label distribution
         TObjectDoubleHashMap weightedNeigLablDist = new TObjectDoubleHashMap();
         Iterator<String> neighIter = neighScores.keySet().iterator();
         while (neighIter.hasNext()) {
           String neighName = neighIter.next();
           ProbUtil.AddScores(weightedNeigLablDist, // newEstimatedScores,
                         mu2 * neighbors.get(neighName),
 
View Full Code Here
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
      /////
      String line = value.toString();
      // id gold_label injected_labels estimated_labels neighbors rw_probabilities
      String[] fields = line.split(_kDelim);      
      TObjectDoubleHashMap neighbors = CollectionUtil.String2Map(fields[4]);      
      TObjectDoubleHashMap rwProbabilities = CollectionUtil.String2Map(fields[5]);
      
      // If the current node is a seed node but there is no
      // estimate label information yet, then transfer the seed label
      // to the estimated label distribution. Ideally, this is likely
      // to be used in the map of the very first iteration.
      boolean isSeedNode = fields[2].length() > 0 ? true : false;
      if (isSeedNode && fields[3].length() == 0) {
  fields[3] = fields[2];
      }

      // TODO(partha): move messages to ProtocolBuffers
      
      // Send two types of messages:
      //   -- self messages which will store the injection labels and
      //        random walk probabilities.
      //   -- messages to neighbors about current estimated scores
      //        of the node.
      //
      // message to self
      output.collect(new Text(fields[0]), new Text("labels" + _kDelim + line));

      // message to neighbors
      TObjectDoubleIterator neighIterator = neighbors.iterator();
      while (neighIterator.hasNext()) {
  neighIterator.advance();
        
  // message (neighbor_node, current_node + DELIM + curr_node_label_scores
  output.collect(new Text((String) neighIterator.key()),
           new Text("labels" + _kDelim + fields[0] + _kDelim + fields[3]));
        
  // message (neighbor_node, curr_node + DELIM + curr_node_edge_weights + DELIM curr_node_cont_prob
  assert(neighbors.containsKey((String) neighIterator.key()));
  output.collect(new Text((String) neighIterator.key()),
           new Text("edge_info" + _kDelim +
        fields[0] + _kDelim +
        neighbors.get((String) neighIterator.key()) + _kDelim +
        rwProbabilities.get(Constants._kContProb)));
      }
    }
View Full Code Here
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
    }
  
    public void reduce(Text key, Iterator<Text> values,
           OutputCollector<Text, Text> output, Reporter reporter) throws IOException {      
      // new scores estimated for the current node
      TObjectDoubleHashMap newEstimatedScores = new TObjectDoubleHashMap();
      
      // set to true only if the message sent to itself is found.
      boolean isSelfMessageFound = false;
      
      String vertexId = key.toString();
      String vertexString = "";
      
      TObjectDoubleHashMap neighbors = null;
      TObjectDoubleHashMap randWalkProbs = null;
      
      HashMap<String, String> neighScores =
  new HashMap<String, String>();
      
      TObjectDoubleHashMap incomingEdgeWeights = new TObjectDoubleHashMap();
      TObjectDoubleHashMap neighborContProb = new TObjectDoubleHashMap();
      
      int totalMessagesReceived = 0;
      
      // iterate over all the messages received at the node
      while (values.hasNext()) {
  ++totalMessagesReceived;

  String val = values.next().toString();
  String[] fields = val.split(_kDelim);
        
  // first field represents the type of message
  String msgType = fields[0];
        
  if (fields[0].equals("labels")) {          
    // self-message check
    if (vertexId.equals(fields[1])) {
      isSelfMessageFound = true;
      vertexString = val;          
 
      TObjectDoubleHashMap injLabels = CollectionUtil.String2Map(fields[3]);
      neighbors = CollectionUtil.String2Map(neighbors, fields[5]);
      randWalkProbs = CollectionUtil.String2Map(fields[6]);
            
      if (injLabels.size() > 0) {          
        // add injected labels to the estimated scores.
        ProbUtil.AddScores(newEstimatedScores,
         mu1 * randWalkProbs.get(Constants._kInjProb),
         injLabels);
      }
    } else {
      // an empty third field represents that the
      // neighbor has no valid label assignment yet.
      if (fields.length > 2) {
        neighScores.put(fields[1], fields[2]);
      }
    }
  } else if (msgType.equals("edge_info")) {
    // edge_info neigh_vertex incoming_edge_weight cont_prob
    String neighId = fields[1];
          
    if (!incomingEdgeWeights.contains(neighId)) {
      incomingEdgeWeights.put(neighId, Double.parseDouble(fields[2]));
    }
          
    if (!neighborContProb.contains(neighId)) {
      neighborContProb.put(neighId, Double.parseDouble(fields[3]));
    }
  } else {
      throw new RuntimeException("Invalid message: " + val);
  }
      }

      // terminate if message from self is not received.
      if (!isSelfMessageFound) {
          throw new RuntimeException("Self message not received for node " + vertexId);
      }
      
      // collect neighbors' label distributions and create one single
      // label distribution
      TObjectDoubleHashMap weightedNeigLablDist = new TObjectDoubleHashMap();
      Iterator<String> neighIter = neighScores.keySet().iterator();
      while (neighIter.hasNext()) {
  String neighName = neighIter.next();
        
  double mult = randWalkProbs.get(Constants._kContProb) * neighbors.get(neighName) +
View Full Code Here
11
12
13
14
15
16
17
18
19
20
21
    int totalLabels = labels.size();
    assert (totalLabels > 0);
    double prior = 1.0 / totalLabels;
    assert (prior > 0);

    TObjectDoubleHashMap retMap = new TObjectDoubleHashMap();
    for (int li = 0; li < totalLabels; ++li) {
      retMap.put(labels.get(li), prior);
    }
    return (retMap);
  }
View Full Code Here
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
    return (sum);
  }

  public static double GetDifferenceNorm2Squarred(TObjectDoubleHashMap m1,
                                                  double m1Mult, TObjectDoubleHashMap m2, double m2Mult) {
    TObjectDoubleHashMap diffMap = new TObjectDoubleHashMap();

    // copy m1 into the difference map
    TObjectDoubleIterator iter = m1.iterator();
    while (iter.hasNext()) {
      iter.advance();
      diffMap.put(iter.key(), m1Mult * iter.value());
    }

    iter = m2.iterator();
    while (iter.hasNext()) {
      iter.advance();
      diffMap.adjustOrPutValue(iter.key(), -1 * m2Mult * iter.value(), -1
                               * m2Mult * iter.value());
    }

    double val = 0;
    iter = diffMap.iterator();
    while (iter.hasNext()) {
      iter.advance();
      val += iter.value() * iter.value();
    }

View Full Code Here
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
    ArrayList<ArrayList> results = new ArrayList<ArrayList>();
    HashSet<String> uniqueConfigs = new HashSet<String>();
   
    // map from algo to the current best scores and the corresponding config
    HashMap<String,Hashtable> algo2BestConfig = new HashMap<String,Hashtable>();
    TObjectDoubleHashMap algo2BestScore = new TObjectDoubleHashMap();
   
    // store console
    PrintStream consoleOut = System.out;
    PrintStream consoleErr = System.err;

    for (int ci = 0; ci < configs.size(); ++ci) {
      Hashtable c = configs.get(ci);
     
      // if this a post-tune config, then generate seed and test files
      if (Defaults.GetValueOrDefault((String) c.get("is_final_run"), false)) {
        String splitId = Defaults.GetValueOrDie(c, "split_id");
        c.put("seed_file", c.remove("seed_base") + "." + splitId + ".train");
        c.put("test_file", c.remove("test_base") + "." + splitId + ".test");
      }
     
      // output file name is considered a unique identifier of a configuration
      String outputFile = GetOutputFileName(c, opDir, idenStr);
      if (uniqueConfigs.contains(outputFile)) {
        continue;
      }
      uniqueConfigs.add(outputFile);
      if (opDir != null) {
        c.put("output_file", outputFile);
      }

      System.out.println("Working with config: " + c.toString());

      try {
        // reset System.out so that the log printed using System.out.println
        // is directed to the right log file
        String logFile = GetLogFileName(c, logDir, idenStr);
       
        // if the log file exists, then don't repeat
        File lf = new File(logFile);
        if (skipExistingConfigs && lf.exists()) {
          continue;
        }
       
        FileOutputStream fos = new FileOutputStream(new File(logFile));
        PrintStream ps = new PrintStream(fos);
        System.setOut(ps);
        System.setErr(ps);
     
        results.add(new ArrayList());
        JuntoConfigRunner.apply(c, results.get(results.size() - 1));
        UpdateBestConfig((String) c.get("algo"), algo2BestScore,
                         algo2BestConfig, c, results.get(results.size() - 1));

        // reset System.out back to the original console value
        System.setOut(consoleOut);
        System.setErr(consoleErr);
       
        // close log file
        fos.close();

      } catch (FileNotFoundException fnfe) {
        fnfe.printStackTrace();
      } catch (IOException ioe) {
        ioe.printStackTrace();
      }
    }
   
    // print out the best parameters for each algorithm
    Iterator algoIter = algo2BestConfig.keySet().iterator();
    while (algoIter.hasNext()) {
      String algo = (String) algoIter.next();
      System.out.println("\n#################\n" +
                         "BEST_CONFIG_FOR " + algo + " " +
                         algo2BestScore.get(algo) + "\n" +
                         CollectionUtil.Map2StringPrettyPrint(algo2BestConfig.get(algo)));
     
      // run test with tuned parameters, if requested
      if (finalTestConfigFile != null) {
        Hashtable finalTestConfig = (Hashtable) algo2BestConfig.get(algo).clone();
View Full Code Here
TOP

Related Classes of gnu.trove.map.hash.TObjectDoubleHashMap$TDoubleValueCollection$TObjectDoubleValueHashIterator

  • gnu.trove.iterator.hash.TObjectHashIterator
  • gnu.trove.iterator.TDoubleIterator
  • gnu.trove.iterator.TObjectDoubleIterator
  • gnu.trove.map.TObjectDoubleMap
  • gnu.trove.procedure.TDoubleProcedure
  • upenn.junto.algorithm.parallel.AdsorptionHadoop$Map
  • upenn.junto.algorithm.parallel.AdsorptionHadoop$Reduce
  • upenn.junto.algorithm.parallel.LP_ZGL_Hadoop$LP_ZGL_Map
  • upenn.junto.algorithm.parallel.LP_ZGL_Hadoop$LP_ZGL_Reduce
  • upenn.junto.algorithm.parallel.MADHadoop$MADHadoopMap
    • Copyright © 2018 www.massapicom. All rights reserved.
    All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.