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;
}
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();
}
