Esempio n. 1
0
  public double train(DataSet train_data) {
    _n_inputs = train_data._n_cols;
    int n_class = train_data._n_classes;
    _n_outputs = Math.min(_n_inputs, n_class - 1);
    build();

    // train_data.print_dat_format();

    DoubleMatrix Sw = DoubleMatrix.zeros(_n_inputs, _n_inputs);
    DoubleMatrix Sb = DoubleMatrix.zeros(_n_inputs, _n_inputs);

    int[] n_classes = new int[n_class];
    double[][] mean_for_class = new double[n_class][_n_inputs];
    double[] mean = new double[_n_inputs];

    int n_examples = train_data._n_rows, i, j;

    for (i = 0; i < n_examples; i++) {
      double[] X = train_data.get_X(i);
      int label = train_data.get_label(i);

      for (j = 0; j < _n_inputs; j++) {
        mean_for_class[label][j] += X[j];
        mean[j] += X[j];
      }

      n_classes[label]++;
    }

    double[][] diff_class_mean = new double[n_class][_n_inputs];
    for (j = 0; j < _n_inputs; j++) {
      mean[j] /= 1.0 * n_examples; // compute the total mean value;

      for (i = 0; i < n_class; i++) {
        if (n_classes[i] == 0) {
          mean_for_class[i][j] = 0;
        } else {
          mean_for_class[i][j] /= 1.0 * n_classes[i];
        }

        diff_class_mean[i][j] = mean_for_class[i][j] - mean[j];
      }
    }

    // OutFile.printf(mean_for_class);

    for (i = 0; i < n_class; i++) {
      double[] diff = diff_class_mean[i];

      // Sb = Sb + c_i (v_i - mean)(v_i - mean);
      Sb.addi(new DoubleMatrix(Vec.outer_dot(diff, diff)).muli(n_classes[i]));
    }

    //		 for(i = 0; i < _n_inputs; i++)
    //			{
    //				for(j = 0; j < _n_inputs; j++)
    //				{
    //					OutFile.printf("%f ",Sb.get(i,j));
    //				}
    //				OutFile.printf("\n");
    //			}

    for (i = 0; i < n_examples; i++) {
      double[] diff = Vec.minus(train_data.get_X(i), mean_for_class[train_data.get_label(i)]);

      // Sw = Sw + diff * diff
      Sw.addi(new DoubleMatrix(Vec.outer_dot(diff, diff)));
    }

    DoubleMatrix[] eig_sw = Eigen.symmetricEigenvectors(Sw);

    //        for(i = 0; i < _n_inputs; i++)
    //		{
    //			for(j = 0; j < _n_inputs; j++)
    //			{
    //				OutFile.printf("%f ",eig_sw[0].get(i,j));
    //			}
    //			OutFile.printf("\n");
    //		}

    double s;
    for (j = 0; j < _n_inputs; j++) {
      s = eig_sw[1].get(j, j);

      // OutFile.printf("%f\n",s);

      if (s > General.SMALL_CONST) {
        s = 1.0 / Math.sqrt(s);
      } else {
        s = 0;
      }

      for (i = 0; i < _n_inputs; i++) {
        eig_sw[0].put(i, j, eig_sw[0].get(i, j) * s);
      }
    }

    // eig_sw[0].print();

    //        OutFile.printf(" sf.\n");
    //        for(i = 0; i < _n_inputs; i++)
    //		{
    //			for(j = 0; j < _n_inputs; j++)
    //			{
    //				OutFile.printf("%f ",eig_sw[0].get(i,j));
    //			}
    //			OutFile.printf("\n");
    //		}

    // eig_sw[0].print();

    Sb = eig_sw[0].transpose().mmul(Sb).mmul(eig_sw[0]);

    //        OutFile.printf(" sb.\n");
    //        for(i = 0; i < _n_inputs; i++)
    //		{
    //			for(j = 0; j < _n_inputs; j++)
    //			{
    //				OutFile.printf("%f ",Sb.get(i,j));
    //			}
    //			OutFile.printf("\n");
    //		}

    DoubleMatrix[] eig_sb = Eigen.symmetricEigenvectors(Sb);

    double sum = 0;
    for (i = 0; i < _n_inputs; i++) {
      sum += eig_sb[1].get(i, i);
    }
    OutFile.printf("eig value: \n");
    for (i = 0; i < _n_inputs; i++) {
      OutFile.printf("%f ", eig_sb[1].get(i, i) / sum);
    }
    OutFile.printf("\n");

    eig_sw[0].mmuli(eig_sb[0]);

    //		//eig_vec[0].print();
    //		for(i = 0; i < _n_inputs; i++)
    //		{
    //			for(j = 0; j < _n_inputs; j++)
    //			{
    //				OutFile.printf("%f ",eig_sb[1].get(i,j));
    //			}
    //			OutFile.printf("\n");
    //		}
    // OutFile.printf("%d %d outputs: %d\n",eig_vec[0].rows,eig_vec[0].columns, _n_outputs);

    for (i = 0; i < _n_outputs; i++) {
      for (j = 0; j < _n_inputs; j++) {
        _eig_mat[j][i] = eig_sw[0].get(j, _n_inputs - 1 - i);
      }
    }

    return 0.0f;
  }
Esempio n. 2
0
  public void build() {
    OutFile.printf("reduce the data input by LDA to %d dimension\n", _n_outputs);

    _eig_mat = new double[_n_inputs][_n_outputs];
    refresh();
  }