public svm_node[] buildingBean2SvmNode(BuldingBean buldingBean) {
    // TODO Auto-generated method stub
    svm_node p0 = new svm_node();
    p0.index = 0;
    p0.value = buldingBean.getBaseElectric();

    svm_node p1 = new svm_node();
    p1.index = 1;
    p1.value = buldingBean.getAirElectric();

    svm_node[] p = {p0, p1};
    return p;
  }
Beispiel #2
0
  public static void svmTrain(ArrayList<ArrayList<Double>> train) {
    svm_problem prob = new svm_problem();
    int dataCount = train.size();
    prob.y = new double[dataCount];
    prob.l = dataCount;
    prob.x = new svm_node[dataCount][];

    for (int i = 0; i < dataCount; i++) {
      ArrayList<Double> features = train.get(i);
      prob.x[i] = new svm_node[features.size() - 1];
      for (int j = 1; j < features.size(); j++) {
        svm_node node = new svm_node();
        node.index = j - 1;
        node.value = features.get(j);
        prob.x[i][j - 1] = node;
      }
      prob.y[i] = features.get(0);
    }

    svm_parameter param = new svm_parameter();
    param.probability = 1;
    param.gamma = 0.5;
    param.nu = 0.5;
    param.C = 1;
    param.svm_type = svm_parameter.NU_SVC;
    param.kernel_type = svm_parameter.LINEAR;
    param.cache_size = 20000;
    param.eps = 0.001;

    _model = svm.svm_train(prob, param);
  }
 public void addPrev(ArrayList<MultiPoint> data, svm_node[] buf, int i) {
   int L = data.get(0).values.length;
   for (int j = 0; j < prev; j++) {
     for (int l = 0; l < L; l++) {
       svm_node node = new svm_node();
       node.index = colCnt + j;
       node.value = data.get(i - j - 1).values[l];
       buf[colCnt + j * L + l] = node;
     }
   }
   //		svm_node node = new svm_node();
   //		node.index = colCnt+prev;
   //		node.value = 1.0;
   //		buf[colCnt+prev*L]=node;
   colCnt += prev * L;
 }
Beispiel #4
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  public static LabelAndProbability evaluate(ArrayList<Double> features) {
    svm_node[] nodes = new svm_node[features.size() - 1];
    for (int i = 1; i < features.size(); i++) {
      svm_node node = new svm_node();
      node.index = i;
      node.value = features.get(i);

      nodes[i - 1] = node;
    }

    int[] labels = new int[Globals._numExercises];
    int n = Globals._numExercises;
    Log.d("Number of Exercises", "" + n);
    double[] values = new double[(n * (n - 1) / 2)];
    svm.svm_get_labels(_model, labels);
    for (int i = 0; i < n; i++) {
      Log.d("labels", "" + labels[i]);
    }
    int Label = (int) svm.svm_predict_values(_model, nodes, values);
    double accuracy = 0.0;
    int votes = 0;
    int p = 0;
    for (int i = 0; i < n; i++)
      for (int j = i + 1; j < n; j++) {
        if (Label == labels[i]) {
          accuracy += values[p];
          if (values[p] > 0) votes++;
        } else if (Label == labels[j]) {
          accuracy -= values[p];
          if (values[p] < 0) votes++;
        }
        p++;
      }

    //        int label = 0;
    //        for (int i = 0; i < probabilities.length; i++) {
    //            Log.d("Labels and Probabilites", labels[i] + " : " + probabilities[i]);
    //            if (max < probabilities[i]) {
    //                label = labels[i];
    //                max = probabilities[i];
    //            }
    //        }
    //        return new LabelAndProbability(Label, (accuracy/n));
    return new LabelAndProbability(Label, votes);
  }