/**
* sdf The test main
*
* @param args ignored
*/
public static void main(String[] args) {
// Instance[] instances = new Instance[100];
// for (int i = 0; i < instances.length; i++) {
// double[] data = new double[2];
// data[0] = Math.sin(i/2.0);
// data[1] = (Math.random() - .5)*2;
// instances[i] = new Instance(data);
// }
DataSet set = new DataSet(trainInstances);
System.out.println("Before randomizing");
System.out.println(set);
// Matrix projection = new RectangularMatrix(new double[][]{ {.6, .6}, {.4, .6}});
Matrix projection = new RectangularMatrix(new double[][] {{.1, .1}, {.1, .1}});
for (int i = 0; i < set.size(); i++) {
Instance instance = set.get(i);
instance.setData(projection.times(instance.getData()));
}
System.out.println("Before ICA");
System.out.println(set);
IndependentComponentAnalysis filter = new IndependentComponentAnalysis(set, 1);
filter.filter(set);
System.out.println("After ICA");
System.out.println(set);
}
private static void computeReconstructionError(DataSet original, DataSet reconstructed) {
EuclideanDistance distanceFunction = new EuclideanDistance();
double totalDataSetsDistance = distanceFunction.value(original, reconstructed);
double averageInstanceDistane = totalDataSetsDistance / original.size();
System.out.println(
"Total reconstruction error as Euclidean distance between data sets: "
+ totalDataSetsDistance);
System.out.println("Average Instance reconstruction error: " + averageInstanceDistane);
}
public void filter(DataSet data) {
int foldSize = data.size() / foldCount;
Random rand = new Random();
for (int currentFold = 0; currentFold < foldCount; currentFold++) {
DataSet currentSet = new DataSet(new Instance[foldSize], data.getDescription());
int i = 0;
while (i < foldSize) {
int position = rand.nextInt(data.size());
Instance instance = data.get(position);
if (instance != null && instance.getData() != null) {
currentSet.set(i, instance);
data.set(position, null);
i++;
}
}
this.folds.add(currentSet);
}
}
