private void bwLoadData_DoWork(object sender, DoWorkEventArgs e)
{
var args = e.Argument as ToBackgroundWorkerArgs;
var mat = Matrix <float> .Build.DenseOfColumns(this.GetForMatrix(
args._PreProcessTransform._Transform,
args._Count));
var du = new Datas.Useable[args._PreProcessTransform._Data.Length];
for (int i = 0; i < du.Length; i++)
{
du[i] = new Datas.Useable(
args._PreProcessTransform._Data[i]._Data * mat,
args._PreProcessTransform._Data[i]._Labels);
}
if (this.bwLoadData.CancellationPending)
{
e.Result = null;
}
else
{
e.Result = du;
}
}
public void SetData(Datas.Useable train, Datas.Useable test)
{
this._Train = train;
this._Test = test;
this._Loaded = true;
this.LoadData();
}
private void bwLoadData_DoWork(object sender, DoWorkEventArgs e)
{
try
{
var args = e.Argument as ToBackgroundWorkerArgs;
var train = args._Data[0];
var label_counts = train.getLabelCounts();
var max_labels = label_counts.Values.Max();
var total_rows = max_labels * label_counts.Count;
if (total_rows > 5 * train._CountRows)
{
max_labels = 5 * train._CountRows / label_counts.Count;
total_rows = max_labels * label_counts.Count;
}
var new_train_data = Matrix <float> .Build.Dense(total_rows, train._CountColumns);
var new_train_labels = Vector <float> .Build.Dense(total_rows);
int new_dex = 0;
foreach (var key in label_counts.Keys)
{
int used = 0;
int old_dex = 0;
while (used < max_labels)
{
if (train._Labels[old_dex] == key)
{
new_train_labels[new_dex] = key;
new_train_data.SetRow(new_dex, train._Data.Row(old_dex));
new_dex++;
used++;
}
old_dex = (old_dex + 1) % train._CountRows;
}
}
if (this.bwLoadData.CancellationPending)
{
e.Result = null;
}
else
{
var ret = args._Data.Clone() as Datas.Useable[];
ret[0] = new Datas.Useable(new_train_data, new_train_labels);
e.Result = ret;
}
}
catch (Exception exc)
{
e.Result = exc.ToString();
}
}
public ConfusionMatrix(Func <float[], float> func, Datas.Useable data)
{
// Point.x is actual
// Point.y is predicted
var counts = new Dictionary <PointF, int>();
int rows = data._CountRows;
int cols = data._CountColumns;
int count = 0;
var parameters = new float[cols];
for (int r = 0; r < rows; r++)
{
for (int c = 0; c < cols; c++)
{
parameters[c] = data._Data[r, c];
}
var key = new PointF(data._Labels[r], func(parameters));
if (!counts.TryGetValue(key, out count))
{
count = 0;
}
counts[key] = ++count;
}
var hs = new HashSet <float>();
foreach (var pt in counts.Keys)
{
hs.Add(pt.X);
hs.Add(pt.Y);
}
this._KeysIndexToValues = hs.ToArray();
Array.Sort(this._KeysIndexToValues);
this._KeysValuesToIndex = new Dictionary <float, int>();
count = 0;
foreach (var value in this._KeysIndexToValues)
{
this._KeysValuesToIndex[value] = count++;
}
this._Matrix = Matrix <float> .Build.Dense(this._KeysIndexToValues.Length, this._KeysIndexToValues.Length, 0);
foreach (var kvp in counts)
{
this._Matrix[this._KeysValuesToIndex[kvp.Key.X],
this._KeysValuesToIndex[kvp.Key.Y]] = kvp.Value;
}
this._TotalPoints = rows;
}
public ToBackgroundWorkerArgsTree(
Datas.Useable train,
Datas.Useable test,
int max_depth)
{
this._Train = train;
this._Test = test;
this._MaxDepth = max_depth;
}
public ToBackgroundWorkerkNN(
Datas.Useable train,
Datas.Useable test,
int kNN)
{
this._Train = train;
this._Test = test;
this._kNN = kNN;
}
public ToBackgroundWorkerArgsForest(
Datas.Useable train,
Datas.Useable test,
int max_depth,
int tree_count)
{
this._Train = train;
this._Test = test;
this._MaxDepth = max_depth;
this._TreeCount = tree_count;
}
public ToBackgroundWorkerArgsAdaBoost(
Datas.Useable train,
Datas.Useable test,
Func <Classifiers.BoostableClassifiers.BoostableClassifier> f,
int boosts)
{
this._Train = train;
this._Test = test;
this._Factory = f;
this._Boosts = boosts;
}
/// <summary>
/// Split as in a decision tree
/// </summary>
/// <param name="split_column"></param>
/// <param name="split_value"></param>
/// <param name="less"></param>
/// <param name="more"></param>
internal void Split(
int split_column,
float split_value,
out Datas.Useable less,
out Datas.Useable more)
{
int count_less = 0;
int rows = this._CountRows;
int cols = this._CountColumns;
for (int r = 0; r < rows; r++)
{
if (this._Data[r, split_column] < split_value)
{
count_less++;
}
}
int count_more = rows - count_less;
Vector <float> labels_less = Vector <float> .Build.Dense(count_less);
Matrix <float> data___less = Matrix <float> .Build.Dense(count_less, cols);
Vector <float> labels_more = Vector <float> .Build.Dense(count_more);
Matrix <float> data___more = Matrix <float> .Build.Dense(count_more, cols);
int dex_less = 0;
int dex_more = 0;
for (int r = 0; r < rows; r++)
{
if (this._Data[r, split_column] < split_value)
{
data___less.SetRow(dex_less, this._Data.Row(r));
labels_less[dex_less] = this._Labels[r];
dex_less++;
}
else
{
data___more.SetRow(dex_more, this._Data.Row(r));
labels_more[dex_more] = this._Labels[r];
dex_more++;
}
}
less = new Datas.Useable(data___less, labels_less);
more = new Datas.Useable(data___more, labels_more);
}
/// <summary>
/// Randomly split data to percentage.
/// </summary>
/// <param name="percent_first"></param>
/// <param name="first"></param>
/// <param name="second"></param>
public void Split(
float percent_first,
out Datas.Useable first,
out Datas.Useable second)
{
int rows = this._CountRows;
int cols = this._CountColumns;
int count_first = Math.Max(1, (int)Math.Round(percent_first * this._CountRows));
this.Split(
count_first,
out first,
out second);
}
/// <summary>
/// Randomly split data to percentage.
/// </summary>
/// <param name="percent_first"></param>
/// <param name="first"></param>
/// <param name="second"></param>
public void Split(
int count_first,
out Datas.Useable first,
out Datas.Useable second)
{
int rows = this._CountRows;
int cols = this._CountColumns;
int count_second = rows - count_first;
var bools = Util.PickRandom(count_first, count_second);
Vector <float> labels_first = Vector <float> .Build.Dense(count_first);
Vector <float> labels_second = Vector <float> .Build.Dense(count_second);
Matrix <float> data_first = Matrix <float> .Build.Dense(count_first, cols);
Matrix <float> data_second = Matrix <float> .Build.Dense(count_second, cols);
int dex_less = 0;
int dex_more = 0;
for (int r = 0; r < rows; r++)
{
if (bools[r])
{
data_first.SetRow(dex_less, this._Data.Row(r));
labels_first[dex_less] = this._Labels[r];
dex_less++;
}
else
{
data_second.SetRow(dex_more, this._Data.Row(r));
labels_second[dex_more] = this._Labels[r];
dex_more++;
}
}
first = new Datas.Useable(data_first, labels_first);
second = new Datas.Useable(data_second, labels_second);
}
private void bwLoadData_DoWork(object sender, DoWorkEventArgs e)
{
var args = e.Argument as ToBackgroundWorkerArgs;
var data = new Datas.Useable[2];
args._Data.Split(
args._PercentTest,
out data[1],
out data[0]);
if (this.bwLoadData.CancellationPending)
{
e.Result = null;
}
else
{
e.Result = data;
}
}
public void Train(Datas.Useable train)
{
int rows = train._CountRows;
int cols = train._CountColumns;
int count = Math.Max(2, (int)Math.Round(this._HoldOut * train._CountRows));
Matrix <float> data = Matrix <float> .Build.Dense(count, cols);
Vector <float> labels = Vector <float> .Build.Dense(count);
var subset = new Datas.Useable(data, labels);
Boolean[] bools = null;
for (int i = 0; i < this._TreeCount; i++)
{
int good_dex = 0;
int main_dex = 0;
Util.PickRandom(count, rows - count, ref bools);
foreach (var b in bools)
{
if (b)
{
data.SetRow(good_dex, train._Data.Row(main_dex));
labels[good_dex] = train._Labels[main_dex];
good_dex++;
}
main_dex++;
}
this._Trees[i] = new DecisionTree(this._MaxDepth);
this._Trees[i].Train(subset);
}
}
public void Train(Datas.Useable train)
{
int rows = train._CountRows;
int cols = train._CountColumns;
var weights = new float[rows];
var predictions = new bool[rows];
var parameters = new float[cols];
for (int r = 0; r < rows; r++)
{
weights[r] = 1.0f / rows;
}
this._Classifiers = new BoostableClassifier[this._Boosts];
this._ClassifierWeights = new float[this._Boosts];
for (int i = 1; i <= this._Boosts; i++)
{
var classy = this._Factory();
classy.Train(train, weights);
float error = 0;
for (int r = 0; r < rows; r++)
{
for (int c = 0; c < cols; c++)
{
parameters[c] = train._Data[r, c];
}
predictions[r] = train._Labels[r] == classy.Predict(parameters);
if (!predictions[r])
{
error += weights[r];
}
}
if (error == 0)
{
String err = "Adaboost Error is 0";
Console.WriteLine(err);
throw new Exception(err);
}
float alpha = 0.5f * (float)Math.Log((1 - error) / error);
float sum_weights = 0;
for (int r = 0; r < rows; r++)
{
float learning = (predictions[r] ? 1 : -1);
float new_weight = weights[r] * (float)Math.Exp(-alpha * learning);
if (float.IsInfinity(new_weight) ||
float.IsNaN(new_weight) ||
(new_weight == 0))
{
// Don't update
}
else
{
weights[r] = new_weight;
}
sum_weights += weights[r];
}
for (int r = 0; r < rows; r++)
{
weights[r] /= sum_weights;
}
// Console.WriteLine("\tAlpha: " + alpha);
// Console.WriteLine("\tMin: " + weights.Min());
// Console.Write("\tMax: " + weights.Max() + '\t');
// Console.WriteLine();
this._ClassifierWeights[i - 1] = alpha;
this._Classifiers[i - 1] = classy;
}
}
public void Train(Datas.Useable train)
{
this.Train(train, 0);
}
private void bwLoadData_DoWork(object sender, DoWorkEventArgs e)
{
try
{
Classifier classif = null;
Datas.Useable train = null;
Datas.Useable test = null;
ConfusionMatrix conf_train, conf_test;
if (e.Argument is ToBackgroundWorkerArgsTree)
{
var args = e.Argument as ToBackgroundWorkerArgsTree;
train = args._Train;
test = args._Test;
classif = new DecisionTree(args._MaxDepth);
classif.Train(train);
conf_train = new ConfusionMatrix(classif.Compile, train);
conf_test = new ConfusionMatrix(classif.Compile, test);
}
else if (e.Argument is ToBackgroundWorkerArgsForest)
{
var args = e.Argument as ToBackgroundWorkerArgsForest;
train = args._Train;
test = args._Test;
classif = new RandomForest(args._MaxDepth, args._TreeCount);
classif.Train(train);
conf_train = new ConfusionMatrix(classif.Compile, train);
conf_test = new ConfusionMatrix(classif.Compile, test);
}
else if (e.Argument is ToBackgroundWorkerArgsAdaBoost)
{
var args = e.Argument as ToBackgroundWorkerArgsAdaBoost;
train = args._Train;
test = args._Test;
classif = new AdaBoost(args._Factory, args._Boosts);
classif.Train(train);
conf_train = new ConfusionMatrix(classif.Compile, train);
conf_test = new ConfusionMatrix(classif.Compile, test);
}
else if (e.Argument is ToBackgroundWorkerkNN)
{
var args = e.Argument as ToBackgroundWorkerkNN;
train = args._Train;
test = args._Test;
classif = new kNN(args._kNN);
classif.Train(train);
conf_train = null;
conf_test = new ConfusionMatrix(classif.Compile, test);
}
else
{
throw new Exception("Not recognized stuff");
}
if (this.bwLoadData.CancellationPending)
{
e.Result = null;
}
else
{
e.Result = new TrainerReturn(
conf_train,
conf_test,
classif);
}
}
catch (Exception exc)
{
e.Result = exc.ToString();
}
}
public void Train(Datas.Useable train, int current_depth)
{
var branch_score = train.getLabelCounts();
int max_correct_branch = branch_score.Values.Max();
if ((branch_score.Values.Sum() != max_correct_branch) && // All children are in one cluster.
(this._MaxDepth != current_depth)) // Limit Levels
{
var tups = new Tuple <float, float> [train._Labels.Count];
int cols = train._CountColumns;
int rows = train._CountRows;
double best_entropy = double.MaxValue;
int best_column = -1;
float best_split = -1;
for (int c = 0; c < cols; c++)
{
for (int r = 0; r < rows; r++)
{
tups[r] = new Tuple <float, float>(train._Labels[r], train._Data[r, c]);
}
Array.Sort(tups, (Tuple <float, float> a, Tuple <float, float> b) =>
{ return(a.Item2.CompareTo(b.Item2)); });
var branch_less_data = new Dictionary <float, int>();
var branch_more_data = new Dictionary <float, int>();
foreach (var kvp in branch_score)
{
branch_less_data[kvp.Key] = 0;
branch_more_data[kvp.Key] = kvp.Value;
}
for (int split_point = 0; split_point < rows - 1; split_point++)
{
var tup = tups[split_point];
float this_label = tup.Item1;
float this_value = tup.Item2;
branch_less_data[this_label]++;
branch_more_data[this_label]--;
float next_value = tups[split_point + 1].Item2;
// Skip identical values.
float split_value = (this_value + next_value) / 2;
if ((this_value < split_value) == (next_value < split_value))
{
continue;
}
double p_less = (split_point + 1.0) / rows;
double p_more = 1 - p_less;
double entropy = p_less * branch_less_data.Values.Entropy() +
p_more * branch_more_data.Values.Entropy();
if (entropy < best_entropy)
{
best_entropy = entropy;
best_split = split_value;
best_column = c;
}
}
}
if (best_column != -1)
{
this._BranchSplitValue = best_split;
this._BranchColumn = best_column;
Datas.Useable less, more;
train.Split(
this._BranchColumn,
this._BranchSplitValue,
out less,
out more);
this._BranchLess = new DecisionTree(this._MaxDepth);
this._BranchMore = new DecisionTree(this._MaxDepth);
this._BranchLess.Train(less, current_depth + 1);
this._BranchMore.Train(more, current_depth + 1);
return;
}
}
this._LeafClassification = branch_score.ArgMax();
this._IsLeaf = true;
}
public void Train(Datas.Useable train)
{
var branch_score = train.getLabelCounts();
int max_correct_branch = branch_score.Values.Max();
if (branch_score.Values.Sum() != max_correct_branch) // All children in one levels
{
var tups = new Tuple <float, float> [train._Labels.Count];
int cols = train._CountColumns;
int rows = train._CountRows;
int best_correct = int.MinValue;
for (int c = 0; c < cols; c++)
{
for (int r = 0; r < rows; r++)
{
tups[r] = new Tuple <float, float>(train._Labels[r], train._Data[r, c]);
}
Array.Sort(tups, (Tuple <float, float> a, Tuple <float, float> b) =>
{ return(a.Item2.CompareTo(b.Item2)); });
var branch_less_data = new Dictionary <float, int>();
var branch_more_data = new Dictionary <float, int>();
foreach (var kvp in branch_score)
{
branch_less_data[kvp.Key] = 0;
branch_more_data[kvp.Key] = kvp.Value;
}
for (int split_point = 0; split_point < rows - 1; split_point++)
{
var tup = tups[split_point];
float this_label = tup.Item1;
float this_value = tup.Item2;
branch_less_data[this_label]++;
branch_more_data[this_label]--;
float next_value = tups[split_point + 1].Item2;
// Skip identical values.
float split_value = (this_value + next_value) / 2;
if ((this_value < split_value) == (next_value < split_value))
{
continue;
}
int correct = branch_less_data.Values.Max() + branch_more_data.Values.Max();
if (correct > best_correct)
{
best_correct = correct;
this._BranchSplitValue = split_value;
this._BranchColumn = c;
this._BranchLessClassification = branch_less_data.ArgMax();
this._BranchMoreClassification = branch_more_data.ArgMax();
}
}
}
if (best_correct != int.MinValue)
{
return;
}
}
this._LeafClassification = branch_score.ArgMax();
this._IsLeaf = true;
}
public ToBackgroundWorkerArgs(Datas.Useable data, float percent_test)
{
this._Data = data;
this._PercentTest = percent_test;
}
public void Train(Datas.Useable data, float[] weights)
{
var branch_score = new Dictionary <float, float>();
for (int i = 0; i < weights.Length; i++)
{
float f = data._Labels[i];
float count;
if (!branch_score.TryGetValue(f, out count))
{
count = 0;
}
branch_score[f] = count + weights[i];
}
float max_correct_branch = branch_score.Values.Max();
if (branch_score.Values.Sum() != max_correct_branch) // All children are in one cluster.
{
int cols = data._CountColumns;
int rows = data._CountRows;
var tups = new Tuple <float, float, float> [rows];
float max_correct = max_correct_branch;
for (int c = 0; c < cols; c++)
{
for (int r = 0; r < rows; r++)
{
tups[r] = new Tuple <float, float, float>(
data._Labels[r],
data._Data[r, c],
weights[r]);
}
Array.Sort(tups, (Tuple <float, float, float> a, Tuple <float, float, float> b) =>
{ return(a.Item2.CompareTo(b.Item2)); });
var branch_less_data = new Dictionary <float, float>();
var branch_more_data = new Dictionary <float, float>();
foreach (var kvp in branch_score)
{
branch_less_data[kvp.Key] = 0;
branch_more_data[kvp.Key] = kvp.Value;
}
for (int split_point = 0; split_point < rows - 1; split_point++)
{
var tup = tups[split_point];
float this_label = tup.Item1;
float this_value = tup.Item2;
branch_less_data[this_label] += tup.Item3;
branch_more_data[this_label] -= tup.Item3;
float next_value = tups[split_point + 1].Item2;
// Skip identical values.
float split_value = (this_value + next_value) / 2;
if ((this_value < split_value) == (next_value < split_value))
{
continue;
}
float correct = branch_less_data.Values.Max() + branch_more_data.Values.Max();
if (correct > max_correct)
{
max_correct = correct;
this._BranchSplitValue = split_value;
this._BranchColumn = c;
this._BranchLess = branch_less_data.ArgMax();
this._BranchMore = branch_more_data.ArgMax();
}
}
}
// Better options exist. We should split the branch!
if (max_correct != max_correct_branch)
{
return;
}
}
this._LeafClassification = branch_score.ArgMax();
this._IsLeaf = true;
}
private void bwLoadData_DoWork(object sender, DoWorkEventArgs e)
{
var args = e.Argument as ToBackgroundWorkerArgs;
var ret = new Datas.Useable[args._Data.Length];
int ret_dex = 0;
int new_colmns = 0;
foreach (var b in args._PassThrough)
{
if (b)
{
new_colmns++;
}
}
int new_dex = 0;
int old_dex = 0;
var new_columns_indices = new int[new_colmns];
foreach (var b in args._PassThrough)
{
if (b)
{
new_columns_indices[new_dex++] = old_dex++;
}
else
{
old_dex++;
}
}
foreach (var di in args._Data)
{
int rows = di._Rows;
var data = Matrix <float> .Build.Dense(rows, new_colmns, 0);
var data_labels = Vector <float> .Build.Dense(rows);
for (int r = 0; r < rows; r++)
{
var row = di._DataPoints[r];
for (int c = 0; c < new_colmns; c++)
{
data[r, c] = row[new_columns_indices[c]];
}
data_labels[r] = row[args._LabelIndex];
}
ret[ret_dex++] = new Datas.Useable(data, data_labels);
}
if (this.bwLoadData.CancellationPending)
{
e.Result = null;
}
else
{
e.Result = ret;
}
}
public void Train(Datas.Useable data, float[] weights)
{
var branch_score = new Dictionary <float, float>();
for (int i = 0; i < weights.Length; i++)
{
float f = data._Labels[i];
float count;
if (!branch_score.TryGetValue(f, out count))
{
count = 0;
}
branch_score[f] = count + weights[i];
}
float max_correct_branch = branch_score.Values.Max();
if ((branch_score.Values.Sum() != max_correct_branch) && // All children are in one cluster.
(this._Depth != _MaxDepth)) // Limit Levels
{
int cols = data._CountColumns;
int rows = data._CountRows;
var tups = new Tuple <float, float, float> [rows];
double best_entropy = double.MaxValue;
int best_column = -1;
float best_split = -1;
for (int c = 0; c < cols; c++)
{
for (int r = 0; r < rows; r++)
{
tups[r] = new Tuple <float, float, float>(
data._Labels[r],
data._Data[r, c],
weights[r]);
}
Array.Sort(tups, (Tuple <float, float, float> a, Tuple <float, float, float> b) =>
{ return(a.Item2.CompareTo(b.Item2)); });
var branch_less_data = new Dictionary <float, float>();
var branch_more_data = new Dictionary <float, float>();
foreach (var kvp in branch_score)
{
branch_less_data[kvp.Key] = 0;
branch_more_data[kvp.Key] = kvp.Value;
}
for (int split_point = 0; split_point < rows - 1; split_point++)
{
var tup = tups[split_point];
float this_label = tup.Item1;
float this_value = tup.Item2;
branch_less_data[this_label] += tup.Item3;
branch_more_data[this_label] -= tup.Item3;
float next_value = tups[split_point + 1].Item2;
// Skip identical values.
float split_value = (this_value + next_value) / 2;
if ((this_value < split_value) == (next_value < split_value))
{
continue;
}
double p_less = (split_point + 1.0) / rows;
double p_more = 1 - p_less;
double entropy = p_less * branch_less_data.Values.Entropy() +
p_more * branch_more_data.Values.Entropy();
if (entropy < best_entropy)
{
best_entropy = entropy;
best_split = split_value;
best_column = c;
}
}
}
// Better options exist. We should split the branch!
if (best_entropy != double.MaxValue)
{
this._BranchSplitValue = best_split;
this._BranchColumn = best_column;
// Console.WriteLine("Best Split: "+ this._BranchSplitValue+ " on column:"+ this._BranchColumn);
int count_less = 0;
int count_more = 0;
var branch_less_data = new Dictionary <float, float>();
var branch_more_data = new Dictionary <float, float>();
for (int r = 0; r < rows; r++)
{
if (data._Data[r, this._BranchColumn] < this._BranchSplitValue)
{
count_less++;
float f = data._Labels[r];
float count;
if (!branch_less_data.TryGetValue(f, out count))
{
count = 0;
}
branch_less_data[f] = count + weights[r];
}
else
{
count_more++;
float f = data._Labels[r];
float count;
if (!branch_more_data.TryGetValue(f, out count))
{
count = 0;
}
branch_more_data[f] = count + weights[r];
}
}
// Only split data less if we need too!
if ((this._Depth == this._MaxDepth - 1) ||
(branch_less_data.Values.NumberOfNonZeros() == branch_less_data.Count - 1))
{
this._BranchLess = new DecisionTree(branch_less_data.ArgMax());
}
else
{
var labels_less = Vector <float> .Build.Dense(count_less);
var weights_less = new float[count_less];
var data_less = Matrix <float> .Build.Dense(count_less, cols);
int dex_less = 0;
for (int r = 0; r < rows; r++)
{
if (data._Data[r, this._BranchColumn] < this._BranchSplitValue)
{
data_less.SetRow(dex_less, data._Data.Row(r));
labels_less[dex_less] = data._Labels[r];
weights_less[dex_less] = weights[r];
dex_less++;
}
}
Datas.Useable less = new Datas.Useable(data_less, labels_less);
this._BranchLess = new DecisionTree(this._Depth + 1, this._MaxDepth);
this._BranchLess.Train(less, weights_less);
}
// Only split data more if we need too!
if ((this._Depth == this._MaxDepth - 1) ||
(branch_more_data.Values.NumberOfNonZeros() == branch_more_data.Count - 1))
{
this._BranchMore = new DecisionTree(branch_more_data.ArgMax());
}
else
{
var labels_more = Vector <float> .Build.Dense(count_more);
var weights_more = new float[count_more];
var data_more = Matrix <float> .Build.Dense(count_more, cols);
int dex_more = 0;
for (int r = 0; r < rows; r++)
{
if (data._Data[r, this._BranchColumn] >= this._BranchSplitValue)
{
data_more.SetRow(dex_more, data._Data.Row(r));
labels_more[dex_more] = data._Labels[r];
weights_more[dex_more] = weights[r];
dex_more++;
}
}
Datas.Useable more = new Datas.Useable(data_more, labels_more);
this._BranchMore = new DecisionTree(this._Depth + 1, this._MaxDepth);
this._BranchMore.Train(more, weights_more);
}
return;
}
}
this._LeafClassification = branch_score.ArgMax();
this._IsLeaf = true;
}
