Package JKernelMachines.fr.lip6.kernel.typed

Examples of JKernelMachines.fr.lip6.kernel.typed.DoubleLinear

54
55
56
57
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
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
159
160
161
162
163
164
165
166
167
168
169
170
171
  {
    eprintln(2, "training on "+train.size()+" train data and "+test.size()+" test data");
   
    //first training
    eprint(3, "first training ");
    svm = new DoublePegasosSVM();
    svm.setLambda(lambda);
    svm.setK(k);
    svm.setT(T);
    svm.setT0(t0);
    svm.train(train);
    eprintln(3, " done.");
   
    //affect numplus highest output to plus class
    eprintln(3, "affecting 1 to the "+numplus+" highest output");
    SortedSet<TrainingSample<double[]>> sorted = new TreeSet<TrainingSample<double[]>>(new Comparator<TrainingSample<double[]>>(){

      public int compare(TrainingSample<double[]> o1, TrainingSample<double[]> o2) {
        int ret = (new Double(svm.valueOf(o2.sample))).compareTo(svm.valueOf(o1.sample));
        if(ret == 0)
          ret = -1;
        return ret;
      }
     
    });
    sorted.addAll(test);
    eprintln(4, "sorted size : "+sorted.size()+" test size : "+test.size());
    int n = 0;
    for(TrainingSample<double[]> t : sorted)
    {
      if(n < numplus)
        t.label = 1;
      else
        t.label = -1;
      n++;
    }
   
    double C = 1. / (train.size()*lambda) ;
    double Cminus = 1e-5;
    double Cplus = 1e-5 * numplus/(test.size() - numplus);
   
    while(Cminus < C || Cplus < C)
    {
      //solve full problem
      ArrayList<TrainingSample<double[]>> full = new ArrayList<TrainingSample<double[]>>();
      full.addAll(train);
      full.addAll(test);
     
      eprint(3, "full training ");
      svm = new DoublePegasosSVM();
      svm.setLambda(lambda);
      svm.setK(k);
      svm.setT(T);
      svm.setT0(t0);
      svm.train(full);
      eprintln(3, "done.");
     
      boolean changed = false;
     
      do
      {
        changed = false;
        //0. computing error
        final Map<TrainingSample<double[]>, Double> errorCache = new HashMap<TrainingSample<double[]>, Double>();
        for(TrainingSample<double[]> t : test)
        {
          double err1 = 1. - t.label * svm.valueOf(t.sample);
          errorCache.put(t, err1);
        }
        eprintln(3, "Error cache done.");
       
        // 1 . sort by descending error
        sorted = new TreeSet<TrainingSample<double[]>>(new Comparator<TrainingSample<double[]>>(){

          public int compare(TrainingSample<double[]> o1,
              TrainingSample<double[]> o2) {
            int ret = errorCache.get(o2).compareTo(errorCache.get(o1));
            if(ret == 0)
              ret = -1;
            return ret;
          }
        });
        sorted.addAll(test);
        List<TrainingSample<double[]>> sortedList = new ArrayList<TrainingSample<double[]>>();
        sortedList.addAll(sorted);
       
       
        eprintln(3, "sorting done, checking couple");
       
        // 2 . test all couple by decreasing error order
//        for(TrainingSample<T> i1 : sorted)
        for(int i = 0 ; i < sortedList.size(); i++)
        {
          TrainingSample<double[]> i1 = sortedList.get(i);
//          for(TrainingSample<T> i2 : sorted)
          for(int j = i+1; j < sortedList.size(); j++)
          {
            TrainingSample<double[]> i2 = sortedList.get(j);
            if(examine(i1, i2, errorCache))
            {
              eprintln(3, "couple found !");
              changed = true;
              break;
            }
          }
          if(changed)
            break;
        }

        if(changed)
        {
          eprintln(3, "re-training");
          svm = new DoublePegasosSVM();
          svm.setLambda(lambda);
          svm.setK(k);
          svm.setT(T);
          svm.setT0(t0);
          svm.train(full);
View Full Code Here
49
50
51
52
53
54
55
56
57
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
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
159
  {
    eprintln(2, "training on "+train.size()+" train data and "+test.size()+" test data");
   
    //first training
    eprint(3, "first training ");
    svm = new DoubleSGDQN();
    DoubleSGDQN.VERBOSE = false;
    svm.train(train);
    eprintln(3, " done.");
   
    //affect numplus highest output to plus class
    eprintln(3, "affecting 1 to the "+numplus+" highest output");
    SortedSet<TrainingSample<double[]>> sorted = new TreeSet<TrainingSample<double[]>>(new Comparator<TrainingSample<double[]>>(){

      public int compare(TrainingSample<double[]> o1, TrainingSample<double[]> o2) {
        int ret = (new Double(svm.valueOf(o2.sample))).compareTo(svm.valueOf(o1.sample));
        if(ret == 0)
          ret = -1;
        return ret;
      }
     
    });
    sorted.addAll(test);
    eprintln(4, "sorted size : "+sorted.size()+" test size : "+test.size());
    int n = 0;
    for(TrainingSample<double[]> t : sorted)
    {
      if(n < numplus)
        t.label = 1;
      else
        t.label = -1;
      n++;
    }
   
    double Cminus = 1e-5;
    double Cplus = 1e-5 * numplus/(test.size() - numplus);
   
    while(Cminus < C || Cplus < C)
    {
      //solve full problem
      ArrayList<TrainingSample<double[]>> full = new ArrayList<TrainingSample<double[]>>();
      full.addAll(train);
      full.addAll(test);
     
      eprint(3, "full training ");
      svm = new DoubleSGDQN();
      svm.setC((Cminus+Cplus)/2.);
      svm.train(full);
      eprintln(3, "done.");
     
      boolean changed = false;
     
      do
      {
        changed = false;
        //0. computing error
        final Map<TrainingSample<double[]>, Double> errorCache = new HashMap<TrainingSample<double[]>, Double>();
        for(TrainingSample<double[]> t : test)
        {
          double err1 = 1. - t.label * svm.valueOf(t.sample);
          errorCache.put(t, err1);
        }
        eprintln(3, "Error cache done.");
       
        // 1 . sort by descending error
        sorted = new TreeSet<TrainingSample<double[]>>(new Comparator<TrainingSample<double[]>>(){

          public int compare(TrainingSample<double[]> o1,
              TrainingSample<double[]> o2) {
            int ret = errorCache.get(o2).compareTo(errorCache.get(o1));
            if(ret == 0)
              ret = -1;
            return ret;
          }
        });
        sorted.addAll(test);
        List<TrainingSample<double[]>> sortedList = new ArrayList<TrainingSample<double[]>>();
        sortedList.addAll(sorted);
       
       
        eprintln(3, "sorting done, checking couple");
       
        // 2 . test all couple by decreasing error order
//        for(TrainingSample<T> i1 : sorted)
        for(int i = 0 ; i < sortedList.size(); i++)
        {
          TrainingSample<double[]> i1 = sortedList.get(i);
//          for(TrainingSample<T> i2 : sorted)
          for(int j = i+1; j < sortedList.size(); j++)
          {
            TrainingSample<double[]> i2 = sortedList.get(j);
            if(examine(i1, i2, errorCache))
            {
              eprintln(3, "couple found !");
              changed = true;
              break;
            }
          }
          if(changed)
            break;
        }

        if(changed)
        {
          eprintln(3, "re-training");
          svm = new DoubleSGDQN();
          svm.setC((Cminus+Cplus)/2.);
          svm.train(full);
        }
      }
      while(changed);
View Full Code Here
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
    if (files.size() != bow.size()){
      System.err.println("Bow and files of different size !!!! STOPING ...");
      return null;
    }

    DoubleGaussChi2 gaussKernel = new DoubleGaussChi2(gamma);
    double distmean = 0.0;

    if (bComptuteMeanDist){
      int cpt = 0;
      for (int i=0; i < files.size(); i++ ){
        for (int j = i + 1; j < files.size(); j++ ){
          distmean += gaussKernel.distChi2(bow.get(i).get(0), bow.get(j).get(0));
          cpt++;
        }
      }
      distmean = distmean / ((double)cpt);
      if (distmean < Double.MIN_VALUE)
        System.err.println("distmean = " + distmean + " to small - pathological signatures !!!!!! STOPING ...");

      gamma = 1/distmean;
      gaussKernel.setGamma(gamma);

      System.out.println("distmean = " + distmean + " gamma = " + gamma);
    }

    ThreadedSumKernel<ArrayList<double[]>> tsk = new ThreadedSumKernel<ArrayList<double[]>>();
View Full Code Here
78
79
80
81
82
83
84
85
86
87
88
    if(!isInialize){
      System.err.println("You need initialize this algorithm for training.");
      return ;
    }
    /* train SVM */
    DoubleLinear kernel = new DoubleLinear();
    svm = new SMOSVM<double[]>(kernel);
    //svm.setC(1);
    svm.setVerbosityLevel(0);

    //////////////////////////////////////////////////////////////////
View Full Code Here
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
      map.put(s, index);
      index++;
    }
   
    //computing matrix       
    ThreadedMatrixOperator factory = new ThreadedMatrixOperator()
    {
      @Override
      public void doLine(int index, double[] line) {
        //reverse search through mapping S <-> index
        S s1 = null;
        for(S s : map.keySet())
          if( map.get(s) == index )
          {
            s1 = s;
            break;
          }
        //mapped signature
        T t1 = signatures.get(s1);
       
        //all mapping S <-> T
        for(S s2 : map.keySet())
        {
          //get index of s2
          int j = map.get(s2);
          //get signature of s2
          T t2 = signatures.get(s2);
          //add value of kernel
          line[j] = kernel.valueOf(t1, t2);
        }
      };
    };


    /* do the actuel computing of the matrix */
    matrix = factory.getMatrix(matrix);
   
  }
View Full Code Here
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
  public double[][] getKernelMatrix(final List<TrainingSample<T>> l) {
   
    final List<TrainingSample<T>> e = l;
    double[][] matrix = new double[e.size()][e.size()];
       
    ThreadedMatrixOperator factory = new ThreadedMatrixOperator()
    {
      @Override
      public void doLine(int index, double[] line) {
       
        T xi = l.get(index).sample;
       
        for(int i = line.length-1 ; i >= 0 ; i--)
        {
          line[i] = k.valueOf(xi, l.get(i).sample);
        }
      };
    };

    /* do the actuel computing of the matrix */
    matrix = factory.getMatrix(matrix);
   
    return matrix;
  }
View Full Code Here
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
        continue;
     

      final double[][] m = k.getKernelMatrix(l);
      // specific factory
      ThreadedMatrixOperator tmo = new ThreadedMatrixOperator(){
       
        @Override
        public void doLine(int index, double[] line) {
         
          for(int i = line.length-1 ; i >= 0 ; i--)
          {
            line[i] += m[index][i] * w;
          }
        };
       
      };
     
      tmo.getMatrix(matrix);
    }
    return matrix;
  }
View Full Code Here
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
        continue;
     

      final double[][] m = k.getKernelMatrix(l);
      // specific factory
      ThreadedMatrixOperator tmo = new ThreadedMatrixOperator(){
       
        @Override
        public void doLine(int index, double[] line) {
         
          for(int i = line.length-1 ; i >= 0 ; i--)
          {
            line[i] *= Math.pow(m[index][i], w);
          }
        };
       
      };
     
      tmo.getMatrix(matrix);
    }
    return matrix;
  }
View Full Code Here
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
//        }
//    }
   
    //parallelized
    final double[] resLine = new double[kmatrix.length];
    ThreadedMatrixOperator objFactory = new ThreadedMatrixOperator()
    {
      @Override
      public void doLine(int index, double[] line) {
        if(alp[index] < numPrec)
          return;
        double al1 = -0.5 * alp[index] * l.get(index).label;
        for(int j = line.length-1 ; j != 0 ; j--)
        {
          resLine[index] += al1 * alp[j] * l.get(j).label * kmatrix[index][j];
        }
       
      } 
    };
   
    objFactory.getMatrix(kmatrix);
    double obj1 = 0;
    for(double d : resLine)
      obj1 += d;
   
    double obj2 = 0;
View Full Code Here
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
//        g += -0.5* alp[x]*alp[y]*l.get(x).label*l.get(y).label*kmatrix[x][y];
//      }
     
      //parallelized
      final double[] resLine = new double[kmatrix.length];
      ThreadedMatrixOperator gradFactory = new ThreadedMatrixOperator()
      {
        @Override
        public void doLine(int index, double[] line) {
          if(alp[index] < numPrec)
            return;
          double al1 = -0.5 * alp[index] * l.get(index).label;
          for(int j = line.length-1 ; j != 0 ; j--)
          {
            resLine[index] += al1 * alp[j] * l.get(j).label * kmatrix[index][j];
          }
         
        } 
      };
     
      gradFactory.getMatrix(kmatrix);
      double g = 0;
      for(double d : resLine)
        g += d;
      grad.add(i, g);
    }
View Full Code Here
TOP

Related Classes of JKernelMachines.fr.lip6.kernel.typed.DoubleLinear

  • JKernelMachines.fr.lip6.classifier.DoublePegasosSVM
  • JKernelMachines.fr.lip6.classifier.DoubleSGDQN
  • JKernelMachines.fr.lip6.classifier.SimpleMKL
  • JKernelMachines.fr.lip6.classifier.SOGradPKL
  • JKernelMachines.fr.lip6.classifier.transductive.S3VMLightPegasos
  • JKernelMachines.fr.lip6.classifier.transductive.S3VMLightSGDQN
  • JKernelMachines.fr.lip6.kernel.adaptative.ThreadedProductKernel
  • JKernelMachines.fr.lip6.kernel.adaptative.ThreadedSumKernel
  • JKernelMachines.fr.lip6.kernel.IndexedCacheKernel
  • JKernelMachines.fr.lip6.kernel.IndexedCacheKernelFactory
    • 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.