public static ClassificationModel Read(string filePath)
{
Stream stream = File.Open(filePath, FileMode.Open);
BinaryFormatter bFormatter = new BinaryFormatter();
ClassificationModel m = (ClassificationModel)bFormatter.Deserialize(stream);
stream.Close();
return(m);
}
public ClassificationModel[] Train(BaseVector[] x, int[][] y, int ngroups, int[] rankedFeatures,
IGroupDataProvider data)
{
int[] o = ranker.Rank(x, y, ngroups, rankerParam, data, 1); //TODO
Array.Copy(o, rankedFeatures, o.Length);
int[] sizes = GetSizes(x[0].Length, reductionFactor, maxFeatures);
ClassificationModel[] result = new ClassificationModel[sizes.Length];
for (int i = 0; i < result.Length; i++){
if (i == 0 && sizes[0] == x[0].Length){
result[0] = classifier.Train(x, y, ngroups, classifierParam, 1);
} else{
int[] inds = ArrayUtils.SubArray(o, sizes[i]);
result[i] =
new ClassificationOnSubFeatures(
classifier.Train(ClassificationWithRanking.ExtractFeatures(x, inds), y, ngroups, classifierParam, 1), inds);
}
}
return result;
}
public GroupWiseClassifier(ClassificationModel[] classifiers)
{
this.classifiers = classifiers;
}
public ClassificationOnSubFeatures(ClassificationModel classifier, int[] featureInds)
{
this.classifier = classifier;
this.featureInds = featureInds;
}
public ClassificationModel TrainGroupWise(BaseVector[] x, int[][] y, int ngroups, IGroupDataProvider data,
int nthreads)
{
ClassificationModel[] c = new ClassificationModel[ngroups];
ThreadDistributor td = new ThreadDistributor(nthreads, ngroups, i => { c[i] = TrainGroupWise(i, y, x, data); });
td.Start();
return new GroupWiseClassifier(c);
}