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);
}
}
public static void line_segmentation_sort_x(Intarray segmentation)
{
if (NarrayUtil.Max(segmentation) > 100000)
{
throw new Exception("line_segmentation_merge_small_components: to many segments");
}
Narray <Rect> bboxes = new Narray <Rect>();
ImgLabels.bounding_boxes(ref bboxes, segmentation);
Floatarray x0s = new Floatarray();
unchecked
{
x0s.Push((float)-999999);
}
for (int i = 1; i < bboxes.Length(); i++)
{
if (bboxes[i].Empty())
{
x0s.Push(999999);
}
else
{
x0s.Push(bboxes[i].x0);
}
}
// dprint(x0s,1000); printf("\n");
Narray <int> permutation = new Intarray();
Narray <int> rpermutation = new Intarray();
NarrayUtil.Quicksort(permutation, x0s);
rpermutation.Resize(permutation.Length());
for (int i = 0; i < permutation.Length(); i++)
{
rpermutation[permutation[i]] = i;
}
// dprint(rpermutation,1000); printf("\n");
for (int i = 0; i < segmentation.Length1d(); i++)
{
if (segmentation.At1d(i) == 0)
{
continue;
}
segmentation.Put1d(i, rpermutation[segmentation.At1d(i)]);
}
}
private void achieve(int flag)
{
if (!(flag == SORTED_BY_INPUT ||
flag == SORTED_BY_OUTPUT ||
flag == HAS_HEURISTICS))
{
throw new Exception("CHECK_ARG: flag == SORTED_BY_INPUT || flag == SORTED_BY_OUTPUT || flag == HAS_HEURISTICS");
}
if (flags > 0 & flag > 0)
{
return;
}
if (flag == HAS_HEURISTICS)
{
AStarUtil.a_star_backwards(m_heuristics, this);
return;
}
for (int node = 0; node < nStates(); node++)
{
Intarray permutation = new Intarray();
if (flag == OcroFST.SORTED_BY_INPUT)
{
NarrayUtil.Quicksort(permutation, m_inputs[node]);
}
else
{
NarrayUtil.Quicksort(permutation, m_outputs[node]);
}
NarrayUtil.Permute(m_inputs[node], permutation);
NarrayUtil.Permute(m_outputs[node], permutation);
NarrayUtil.Permute(m_targets[node], permutation);
NarrayUtil.Permute(m_costs[node], permutation);
}
flags |= flag;
}
public override void Arcs(Intarray ids, Intarray targets, Intarray outputs, Floatarray costs, int node)
{
int n1 = node / l2.nStates();
int n2 = node % l2.nStates();
Intarray ids1 = new Intarray();
Intarray ids2 = new Intarray();
Intarray t1 = new Intarray();
Intarray t2 = new Intarray();
Intarray o1 = new Intarray();
Intarray o2 = new Intarray();
Floatarray c1 = new Floatarray();
Floatarray c2 = new Floatarray();
l1.Arcs(ids1, t1, o1, c1, n1);
l2.Arcs(ids2, t2, o2, c2, n2);
// sort & permute
Intarray p1 = new Intarray();
Intarray p2 = new Intarray();
NarrayUtil.Quicksort(p1, o1);
NarrayUtil.Permute(ids1, p1);
NarrayUtil.Permute(t1, p1);
NarrayUtil.Permute(o1, p1);
NarrayUtil.Permute(c1, p1);
NarrayUtil.Quicksort(p2, ids2);
NarrayUtil.Permute(ids2, p2);
NarrayUtil.Permute(t2, p2);
NarrayUtil.Permute(o2, p2);
NarrayUtil.Permute(c2, p2);
int k1, k2;
// l1 epsilon moves
for (k1 = 0; k1 < o1.Length() && o1.At1d(k1) == 0; k1++)
{
ids.Push(ids1.At1d(k1));
targets.Push(Combine(t1.At1d(k1), n2));
outputs.Push(0);
costs.Push(c1.At1d(k1));
}
// l2 epsilon moves
for (k2 = 0; k2 < o2.Length() && ids2.At1d(k2) == 0; k2++)
{
ids.Push(0);
targets.Push(Combine(n1, t2.At1d(k2)));
outputs.Push(o2.At1d(k2));
costs.Push(c2.At1d(k2));
}
// non-epsilon moves
while (k1 < o1.Length() && k2 < ids2.Length())
{
while (k1 < o1.Length() && o1.At1d(k1) < ids2.At1d(k2))
{
k1++;
}
if (k1 >= o1.Length())
{
break;
}
while (k2 < ids2.Length() && o1.At1d(k1) > ids2.At1d(k2))
{
k2++;
}
while (k1 < o1.Length() && k2 < ids2.Length() && o1.At1d(k1) == ids2.At1d(k2))
{
for (int j = k2; j < ids2.Length() && o1.At1d(k1) == ids2.At1d(j); j++)
{
ids.Push(ids1.At1d(k1));
targets.Push(Combine(t1.At1d(k1), t2.At1d(j)));
outputs.Push(o2.At1d(j));
costs.Push(c1.At1d(k1) + c2.At1d(j));
}
k1++;
}
}
}
public virtual void TrainBatch(IDataset ds, IDataset ts)
{
Stopwatch sw = Stopwatch.StartNew();
bool parallel = PGetb("parallel");
float eta_init = PGetf("eta_init"); // 0.5
float eta_varlog = PGetf("eta_varlog"); // 1.5
float hidden_varlog = PGetf("hidden_varlog"); // 1.2
int hidden_lo = PGeti("hidden_lo");
int hidden_hi = PGeti("hidden_hi");
int rounds = PGeti("rounds");
int mlp_noopt = PGeti("noopt");
int hidden_min = PGeti("hidden_min");
int hidden_max = PGeti("hidden_max");
CHECK_ARG(hidden_min > 1 && hidden_max < 1000000, "hidden_min > 1 && hidden_max < 1000000");
CHECK_ARG(hidden_hi >= hidden_lo, "hidden_hi >= hidden_lo");
CHECK_ARG(hidden_max >= hidden_min, "hidden_max >= hidden_min");
CHECK_ARG(hidden_lo >= hidden_min && hidden_hi <= hidden_max, "hidden_lo >= hidden_min && hidden_hi <= hidden_max");
int nn = PGeti("nensemble");
ObjList <MlpClassifier> nets = new ObjList <MlpClassifier>();
nets.Resize(nn);
for (int i = 0; i < nn; i++)
{
nets[i] = new MlpClassifier(i);
}
Floatarray errs = new Floatarray(nn);
Floatarray etas = new Floatarray(nn);
Intarray index = new Intarray();
float best = 1e30f;
if (PExists("%error"))
{
best = PGetf("%error");
}
int nclasses = ds.nClasses();
/*Floatarray v = new Floatarray();
* for (int i = 0; i < ds.nSamples(); i++)
* {
* ds.Input1d(v, i);
* CHECK_ARG(NarrayUtil.Min(v) > -100 && NarrayUtil.Max(v) < 100, "min(v)>-100 && max(v)<100");
* }*/
CHECK_ARG(ds.nSamples() >= 10 && ds.nSamples() < 100000000, "ds.nSamples() >= 10 && ds.nSamples() < 100000000");
for (int i = 0; i < nn; i++)
{
// nets(i).init(data.dim(1),logspace(i,nn,hidden_lo,hidden_hi),nclasses);
if (w1.Length() > 0)
{
nets[i].Copy(this);
etas[i] = ClassifierUtil.rLogNormal(eta_init, eta_varlog);
}
else
{
nets[i].InitData(ds, (int)(logspace(i, nn, hidden_lo, hidden_hi)), c2i, i2c);
etas[i] = PGetf("eta");
}
}
etas[0] = PGetf("eta"); // zero position is identical to itself
Global.Debugf("info", "mlp training n {0} nc {1}", ds.nSamples(), nclasses);
for (int round = 0; round < rounds; round++)
{
Stopwatch swRound = Stopwatch.StartNew();
errs.Fill(-1);
if (parallel)
{
// For each network i
Parallel.For(0, nn, i =>
{
nets[i].PSet("eta", etas[i]);
nets[i].TrainDense(ds); // было XTrain
errs[i] = ClassifierUtil.estimate_errors(nets[i], ts);
});
}
else
{
for (int i = 0; i < nn; i++)
{
nets[i].PSet("eta", etas[i]);
nets[i].TrainDense(ds); // было XTrain
errs[i] = ClassifierUtil.estimate_errors(nets[i], ts);
//Global.Debugf("detail", "net({0}) {1} {2} {3}", i,
// errs[i], nets[i].Complexity(), etas[i]);
}
}
NarrayUtil.Quicksort(index, errs);
if (errs[index[0]] < best)
{
best = errs[index[0]];
cv_error = best;
this.Copy(nets[index[0]]);
this.PSet("eta", etas[index[0]]);
Global.Debugf("info", " best mlp[{0}] update errors={1} {2}", index[0], best, crossvalidate ? "cv" : "");
}
if (mlp_noopt == 0)
{
for (int i = 0; i < nn / 2; i++)
{
int j = i + nn / 2;
nets[index[j]].Copy(nets[index[i]]);
int n = nets[index[j]].nHidden();
int nm = Math.Min(Math.Max(hidden_min, (int)(ClassifierUtil.rLogNormal(n, hidden_varlog))), hidden_max);
nets[index[j]].ChangeHidden(nm);
etas[index[j]] = ClassifierUtil.rLogNormal(etas[index[i]], eta_varlog);
}
}
Global.Debugf("info", " end mlp round {0} err {1} nHidden {2}", round, best, nHidden());
swRound.Stop();
int totalTest = ts.nSamples();
int errCnt = Convert.ToInt32(best * totalTest);
OnTrainRound(this, new TrainEventArgs(
round, best, totalTest - errCnt, totalTest, best, swRound.Elapsed, TimeSpan.Zero
));
}
sw.Stop();
Global.Debugf("info", String.Format(" training time: {0} minutes, {1} seconds",
(int)sw.Elapsed.TotalMinutes, sw.Elapsed.Seconds));
PSet("%error", best);
int nsamples = ds.nSamples() * rounds;
if (PExists("%nsamples"))
{
nsamples += PGeti("%nsamples");
}
PSet("%nsamples", nsamples);
}