public override void TrainDense(IDataset ds)
{
//PSet("%nsamples", ds.nSamples());
float split = PGetf("cv_split");
int mlp_cv_max = PGeti("cv_max");
if (crossvalidate)
{
// perform a split for cross-validation, making sure
// that we don't have the same sample in both the
// test and the training set (even if the data set
// is the result of resampling)
Intarray test_ids = new Intarray();
Intarray ids = new Intarray();
for (int i = 0; i < ds.nSamples(); i++)
{
ids.Push(ds.Id(i));
}
NarrayUtil.Uniq(ids);
Global.Debugf("cvdetail", "reduced {0} ids to {1} ids", ds.nSamples(), ids.Length());
NarrayUtil.Shuffle(ids);
int nids = (int)((1.0 - split) * ids.Length());
nids = Math.Min(nids, mlp_cv_max);
for (int i = 0; i < nids; i++)
{
test_ids.Push(ids[i]);
}
NarrayUtil.Quicksort(test_ids);
Intarray training = new Intarray();
Intarray testing = new Intarray();
for (int i = 0; i < ds.nSamples(); i++)
{
int id = ds.Id(i);
if (ClassifierUtil.Bincontains(test_ids, id))
{
testing.Push(i);
}
else
{
training.Push(i);
}
}
Global.Debugf("cvdetail", "#training {0} #testing {1}",
training.Length(), testing.Length());
PSet("%ntraining", training.Length());
PSet("%ntesting", testing.Length());
Datasubset trs = new Datasubset(ds, training);
Datasubset tss = new Datasubset(ds, testing);
TrainBatch(trs, tss);
}
else
{
TrainBatch(ds, ds);
}
}
protected override void Train(IDataset ds)
{
bool use_junk = PGetb("junk") && !DisableJunk;
int nsamples = ds.nSamples();
if (PExists("%nsamples"))
{
nsamples += PGeti("%nsamples");
}
Global.Debugf("info", "Training content classifier");
if (CharClass.IsEmpty)
{
Initialize(CreateClassesFromDataset(ds));
}
if (use_junk /*&& !JunkClass.IsEmpty*/)
{
Intarray nonjunk = new Intarray();
for (int i = 0; i < ds.nSamples(); i++)
{
if (ds.Cls(i) != jc())
{
nonjunk.Push(i);
}
}
Datasubset nonjunkds = new Datasubset(ds, nonjunk);
CharClass.TrainDense(nonjunkds, PGeti("epochs"));
}
else
{
CharClass.TrainDense(ds, PGeti("epochs"));
}
if (use_junk /*&& !JunkClass.IsEmpty*/)
{
Global.Debugf("info", "Training junk classifier");
Intarray isjunk = new Intarray();
int njunk = 0;
for (int i = 0; i < ds.nSamples(); i++)
{
bool j = (ds.Cls(i) == jc());
isjunk.Push(JunkClass.Classes[Convert.ToInt32(j)]);
if (j)
{
njunk++;
}
}
if (njunk > 0)
{
MappedDataset junkds = new MappedDataset(ds, isjunk);
JunkClass.TrainDense(junkds, PGeti("epochs"));
}
else
{
Global.Debugf("warn", "you are training a junk class but there are no samples to train on");
JunkClass.DeleteLenet();
}
}
PSet("%nsamples", nsamples);
}
protected override void Train(IDataset ds)
{
bool use_junk = PGetb("junk") && !DisableJunk;
if (charclass.IsEmpty)
{
charclass.SetComponent(ComponentCreator.MakeComponent(PGet("charclass")));
TryAttachCharClassifierEvent(charclass.Object);
}
if (junkclass.IsEmpty)
{
junkclass.SetComponent(ComponentCreator.MakeComponent(PGet("junkclass")));
TryAttachJunkClassifierEvent(junkclass.Object);
}
if (ulclass.IsEmpty)
{
ulclass.SetComponent(ComponentCreator.MakeComponent(PGet("ulclass")));
}
Global.Debugf("info", "Training content classifier");
if (use_junk && !junkclass.IsEmpty)
{
Intarray nonjunk = new Intarray();
for (int i = 0; i < ds.nSamples(); i++)
{
if (ds.Cls(i) != jc())
{
nonjunk.Push(i);
}
}
Datasubset nonjunkds = new Datasubset(ds, nonjunk);
charclass.Object.XTrain(nonjunkds);
}
else
{
charclass.Object.XTrain(ds);
}
if (use_junk && !junkclass.IsEmpty)
{
Global.Debugf("info", "Training junk classifier");
Intarray isjunk = new Intarray();
int njunk = 0;
for (int i = 0; i < ds.nSamples(); i++)
{
bool j = (ds.Cls(i) == jc());
isjunk.Push(Convert.ToInt32(j));
if (j)
{
njunk++;
}
}
if (njunk > 0)
{
MappedDataset junkds = new MappedDataset(ds, isjunk);
junkclass.Object.XTrain(junkds);
}
else
{
Global.Debugf("warn", "you are training a junk class but there are no samples to train on");
junkclass.SetComponent(null);
}
if (PGeti("ul") > 0 && !ulclass.IsEmpty)
{
throw new Exception("ulclass not implemented");
}
}
}
