protected override void Train(IDataset ds)
{
if (!(ds.nSamples() > 0))
throw new Exception("nSamples of IDataset must be > 0");
if (!(ds.nFeatures() > 0))
throw new Exception("nFeatures of IDataset must be > 0");
if (c2i.Length() < 1)
{
Intarray raw_classes = new Intarray();
raw_classes.ReserveTo(ds.nSamples());
for (int i = 0; i < ds.nSamples(); i++)
raw_classes.Push(ds.Cls(i));
ClassMap(c2i, i2c, raw_classes);
/*Intarray classes = new Intarray();
ctranslate(classes, raw_classes, c2i);*/
//debugf("info","[mapped %d to %d classes]\n",c2i.length(),i2c.length());
}
TranslatedDataset mds = new TranslatedDataset(ds, c2i);
TrainDense(mds);
}
public override int nFeatures()
{
return(_ds.nFeatures());
}
public void InitData(IDataset ds, int nhidden, Intarray newc2i = null, Intarray newi2c = null)
{
CHECK_ARG(nhidden > 1 && nhidden < 1000000, "nhidden > 1 && nhidden < 1000000");
int ninput = ds.nFeatures();
int noutput = ds.nClasses();
w1.Resize(nhidden, ninput);
b1.Resize(nhidden);
w2.Resize(noutput, nhidden);
b2.Resize(noutput);
Intarray indexes = new Intarray();
NarrayUtil.RPermutation(indexes, ds.nSamples());
Floatarray v = new Floatarray();
for (int i = 0; i < w1.Dim(0); i++)
{
int row = indexes[i];
ds.Input1d(v, row);
float normv = (float)NarrayUtil.Norm2(v);
v /= normv * normv;
NarrayRowUtil.RowPut(w1, i, v);
}
ClassifierUtil.fill_random(b1, -1e-6f, 1e-6f);
ClassifierUtil.fill_random(w2, -1.0f / nhidden, 1.0f / nhidden);
ClassifierUtil.fill_random(b2, -1e-6f, 1e-6f);
if (newc2i != null)
c2i.Copy(newc2i);
if (newi2c != null)
i2c.Copy(newi2c);
}
