ML - правки аудита 2

bin/Lib/DataAdapters.pas

@@ -62,6 +62,8 @@ const
     'Неподдерживаемый тип столбца "{0}" для EncodeLabels!!Unsupported column type "{0}" for EncodeLabels';
   ER_UNKNOWN_CLASS_IN_TRANSFORM =
     'Неизвестное значение класса "{0}" при преобразовании меток!!Unknown class value "{0}" in TransformLabels';
+  ER_LABEL_INDEX_OUT_OF_RANGE =
+    'Индекс метки {0} вне диапазона [0, {1})!!Label index {0} is out of range [0, {1})';
   
 function LabelsToInts(y: Vector): array of integer;
 begin
@@ -115,7 +117,7 @@ begin
     var lbl := labels[i];
 
     if not map.ContainsKey(lbl) then
-      Error('Unknown class in test');
+      Error(ER_UNKNOWN_CLASS_IN_TRANSFORM, lbl);
 
     res[i] := map[lbl];
   end;
@@ -363,7 +365,7 @@ begin
           var lbl := data[i].ToString;
 
           if not map.ContainsKey(lbl) then
-            Error('Unknown class in TransformLabels');
+            Error(ER_UNKNOWN_CLASS_IN_TRANSFORM, lbl);
 
           res[i] := map[lbl];
         end;
@@ -453,8 +455,15 @@ function DecodeLabels(y: array of integer; classes: array of string): array of s
 begin
   var res := new string[y.Length];
 
-  for var i := 0 to y.Length-1 do
+  for var i := 0 to y.Length - 1 do
-    res[i] := classes[y[i]];
+  begin
+    var idx := y[i];
+  
+    if (idx < 0) or (idx >= classes.Length) then
+      Error(ER_LABEL_INDEX_OUT_OF_RANGE, idx, classes.Length);
+  
+    res[i] := classes[idx];
+  end;
 
   Result := res;
 end;

bin/Lib/DataFrameABC.pas

@@ -504,8 +504,6 @@ const
     'Тип соединения не реализован!!Join kind not implemented';
   ER_JOIN_KEYS_LENGTH_MISMATCH =
     'leftKeys и rightKeys должны иметь одинаковую длину!!leftKeys and rightKeys must have the same length';
-  ER_COLUMN_NOT_FOUND =
-    'Столбец "{0}" не найден!!Column not found: {0}';
   ER_ADD_COLUMN_ROW_MISMATCH =
     'Несоответствие числа строк при добавлении столбца!!Row count mismatch when adding column';  
   ER_COLUMN_VALID_LENGTH_MISMATCH =

bin/Lib/InspectionML.pas

@@ -45,7 +45,7 @@ type
   ///   Вектор важностей признаков длины nFeatures.
   ///   Чем больше значение, тем сильнее признак влияет на качество модели
     static function PermutationImportance(model: IPredictiveModel; X: Matrix; y: Vector;
-      scoreFunc: (Vector, Vector) -> real; seed: integer := 0): Vector;
+      scoreFunc: (Vector, Vector) -> real; nRepeats: integer := 5; seed: integer := 0): Vector;
   end;  
 
 implementation
@@ -53,10 +53,20 @@ implementation
 uses MLExceptions;
 
 const
-  ER_SCORE_FUNC_NULL = 'scoreFunc не может быть nil!!scoreFunc cannot be nil';
+  ER_SCORE_FUNC_NULL = 
+    'scoreFunc не может быть nil!!scoreFunc cannot be nil';
+  ER_ARG_OUT_OF_RANGE =
+    'Аргумент {0} имеет недопустимое значение {1}!!Argument {0} has invalid value {1}';
 
-static function Inspection.PermutationImportance(model: IPredictiveModel; X: Matrix; y: Vector;
+
-  scoreFunc: (Vector, Vector) -> real; seed: integer): Vector;
+static function Inspection.PermutationImportance(
+  model: IPredictiveModel; 
+  X: Matrix; 
+  y: Vector;
+  scoreFunc: (Vector, Vector) -> real; 
+  nRepeats: integer;
+  seed: integer  
+): Vector;
 begin
   if model = nil then
     ArgumentNullError(ER_MODEL_NULL);
@@ -67,6 +77,9 @@ begin
   if X.RowCount <> y.Length then
     DimensionError(ER_DIM_MISMATCH, X.RowCount, y.Length);
 
+  if nRepeats < 1 then
+    ArgumentOutOfRangeError(ER_ARG_OUT_OF_RANGE, 'nRepeats', nRepeats);
+
   var baselinePred := model.Predict(X);
   var baselineScore := scoreFunc(y, baselinePred);
 
@@ -74,25 +87,38 @@ begin
   var p := X.ColCount;
 
   var resultVec := new Vector(p);
-  var rng := new System.Random(seed);
 
-  for var j := 0 to p-1 do
+  for var j := 0 to p - 1 do
   begin
-    var Xperm := X.Clone;
+    var acc := 0.0;
 
-    // Fisher–Yates shuffle столбца j (детерминированно через seed)
+    for var r := 0 to nRepeats - 1 do
-    for var i := n-1 downto 1 do
     begin
-      var k := rng.Next(i+1);
+      var Xperm := X.Clone;
-      var tmp := Xperm[i,j];
+
-      Xperm[i,j] := Xperm[k,j];
+      // --- seed для каждого (признак, повтор)
-      Xperm[k,j] := tmp;
+      var runSeed :=
+        if seed >= 0 then seed + j * 100000 + r
+        else System.Environment.TickCount and integer.MaxValue;
+
+      var rnd := new System.Random(runSeed);
+
+      // --- shuffle столбца j
+      for var i := n - 1 downto 1 do
+      begin
+        var k := rnd.Next(i + 1);
+        var tmp := Xperm[i,j];
+        Xperm[i,j] := Xperm[k,j];
+        Xperm[k,j] := tmp;
+      end;
+
+      var permPred := model.Predict(Xperm);
+      var permScore := scoreFunc(y, permPred);
+
+      acc += (baselineScore - permScore);
     end;
 
-    var permPred := model.Predict(Xperm);
+    resultVec[j] := acc / nRepeats;
-    var permScore := scoreFunc(y, permPred);
-
-    resultVec[j] := baselineScore - permScore;
   end;
 
   Result := resultVec;

bin/Lib/LinearAlgebraML.pas

@@ -152,9 +152,6 @@ type
     static function operator *(A: Matrix; x: Vector): Vector;
     static function operator *(A, B: Matrix): Matrix;
     
-    //static function operator *(alpha: real; A: Matrix): Matrix;
-    //static function operator *(A: Matrix; alpha: real): Matrix;
-    
     // ---------- in-place operators ----------
     static function operator +=(A, B: Matrix): Matrix;
     static function operator -=(A, B: Matrix): Matrix;

bin/Lib/MLDatasets.pas

@@ -18,20 +18,20 @@ type
   /// Предоставляет удобные методы для получения матриц признаков
   /// и целевых значений для обучения моделей
   Dataset = class
-  private    
+  private
-   function ValueLabel(feature, value: string): string;
+    function ValueLabel(feature, value: string): string;
-   function CloneMeta(df: DataFrame): Dataset;
+    function CloneMeta(df: DataFrame): Dataset;
   public
-    Name: string;
-    Data: DataFrame;
-    Features: array of string;
-    Target: string;
-    Task: TaskType;
-    
     FeatureLabels: Dictionary<string,string>;
     ValueLabels: Dictionary<string,Dictionary<string,string>>;
     Description: string;
 
+    auto property Name: string;
+    auto property Data: DataFrame;
+    auto property Features: array of string;
+    auto property Target: string;
+    auto property Task: TaskType;
+    
     /// Возвращает true, если датасет относится к задаче с учителем
     /// (classification или regression).
     function IsSupervised: boolean;

bin/Lib/MLModelsABC.pas

@@ -1271,6 +1271,10 @@ type
     fIterations: integer;
     fHasConverged: boolean;
     
+    fRandomSeed: integer;
+    fUserProvidedSeed: boolean;
+    fRng: System.Random;
+    
     function RunSingle(X: Matrix; rnd: System.Random): (Matrix, real, integer, boolean);
   public
     /// Создаёт модель KMeans.
@@ -1361,9 +1365,6 @@ type
     /// Обучает модель на матрице признаков.
     function Fit(X: Matrix): IUnsupervisedModel;
   
-    /// Возвращает метки кластеров.
-    function Predict(X: Matrix): Vector;
-    
     /// Возвращает метки кластеров.
     function PredictLabels(X: Matrix): array of integer;
 
@@ -3042,16 +3043,24 @@ begin
   dest.fMaxFeatures := fMaxFeatures;
   
   dest.fUserProvidedSeed := fUserProvidedSeed;
-  dest.fRng := new System.Random(fRandomSeed);
 
+  if fUserProvidedSeed then
+    dest.fRng := new System.Random(fRandomSeed)
+  else
+    dest.fRng := new System.Random;
+
+  // MUST be stateless
   if fCriterion <> nil then
-    dest.fCriterion := fCriterion; // можно так, если критерий stateless
+    dest.fCriterion := fCriterion;
 
   if fFeatureImportances <> nil then
     dest.fFeatureImportances := fFeatureImportances.Clone;
 
   if fRoot <> nil then
     dest.fRoot := fRoot.Clone;
+
+  if fRowIndices <> nil then
+    dest.fRowIndices := Copy(fRowIndices);
 end;
 
 function DecisionTreeBase.GetFeatureSubset(nFeatures: integer): array of integer;
@@ -3613,11 +3622,8 @@ begin
     fRandomSeed
   );
 
-  // --- базовое состояние (глубокое копирование дерева!)
   CopyBaseState(m);
 
-  m.fMaxFeatures := fMaxFeatures;
-
   // --- классы
   m.fClassCount := fClassCount;
 
@@ -3825,8 +3831,6 @@ begin
 
   CopyBaseState(m);
   
-  m.fLeafL2 := fLeafL2;  
-
   Result := m;
 end;
 
@@ -4099,7 +4103,15 @@ begin
     fRandomSeed
   );
 
+  // --- seed ---
+  rf.fRandomSeed := fRandomSeed;
   rf.fUserProvidedSeed := fUserProvidedSeed;
+
+  if fUserProvidedSeed then
+    rf.fRng := new System.Random(fRandomSeed)
+  else
+    rf.fRng := new System.Random;
+
   rf.fFitted := fFitted;
   rf.fFeatureCount := fFeatureCount;
 
@@ -4439,9 +4451,16 @@ begin
     fRandomSeed
   );
 
+  // --- seed ---
+  rf.fRandomSeed := fRandomSeed;
   rf.fUserProvidedSeed := fUserProvidedSeed;
-  rf.fFitted := fFitted;
 
+  if fUserProvidedSeed then
+    rf.fRng := new System.Random(fRandomSeed)
+  else
+    rf.fRng := new System.Random;
+
+  rf.fFitted := fFitted;
   rf.fFeatureCount := fFeatureCount;
 
   // --- classes ---
@@ -4467,7 +4486,7 @@ begin
     SetLength(rf.fTrees, fTrees.Length);
     for var i := 0 to fTrees.Length - 1 do
       rf.fTrees[i] := DecisionTreeClassifier(fTrees[i].Clone);
-  end;  
+  end;
 
   Result := rf;
 end;
@@ -5301,31 +5320,35 @@ begin
     fMinSamplesSplit,
     fMinSamplesLeaf,
     fSubsample,
-    seed := fRandomSeed
+    fLoss,
+    fHuberDelta,
+    fEarlyStoppingPatience,
+    fQuantileAlpha,
+    fLeafL2,
+    fUseOOBEarlyStopping,
+    fRandomSeed
   );
 
-  // --- seed policy ---
+  // --- seed ---
+  copy.fRandomSeed := fRandomSeed;
   copy.fUserProvidedSeed := fUserProvidedSeed;
 
+  if fUserProvidedSeed then
+    copy.fRng := new System.Random(fRandomSeed)
+  else
+    copy.fRng := new System.Random;
+
   // --- basic state ---
   copy.fInitValue := fInitValue;
   copy.fFeatureCount := fFeatureCount;
   copy.fFitted := fFitted;
 
-  // --- best iteration state ---
+  // --- best iteration ---
   copy.fBestIteration := fBestIteration;
   copy.fBestTrainLoss := fBestTrainLoss;
   copy.fBestScoreLoss := fBestScoreLoss;
 
-  // --- loss configuration ---
+  // --- histories ---
-  copy.fLoss := fLoss;
-  copy.fHuberDelta := fHuberDelta;
-  copy.fQuantileAlpha := fQuantileAlpha;
-  copy.fLeafL2 := fLeafL2;
-  copy.fEarlyStoppingPatience := fEarlyStoppingPatience;
-  copy.fUseOOBEarlyStopping := fUseOOBEarlyStopping;
-
-  // --- deep copy histories ---
   copy.fTrainLossHistory.Clear;
   copy.fTrainLossHistory.AddRange(fTrainLossHistory);
 
@@ -5335,7 +5358,7 @@ begin
   copy.fOOBLossHistory.Clear;
   copy.fOOBLossHistory.AddRange(fOOBLossHistory);
 
-  // --- deep copy trees ---
+  // --- estimators ---
   copy.fEstimators.Clear;
   foreach var tree in fEstimators do
     copy.fEstimators.Add(tree.Clone as DecisionTreeRegressor);
@@ -6174,9 +6197,15 @@ begin
     fRandomSeed
   );
 
-  // --- seed policy ---
+  // --- seed ---
+  model.fRandomSeed := fRandomSeed;
   model.fUserProvidedSeed := fUserProvidedSeed;
 
+  if fUserProvidedSeed then
+    model.fRng := new System.Random(fRandomSeed)
+  else
+    model.fRng := new System.Random;
+
   // --- fitted state ---
   model.fFitted := fFitted;
   model.fFeatureCount := fFeatureCount;
@@ -6197,26 +6226,19 @@ begin
       model.fClassIndex.Add(kv.Key, kv.Value);
   end;
 
-  // --- estimators (deep copy) ---
+  // --- estimators ---
   foreach var trees in fEstimators do
   begin
     var newTrees := new DecisionTreeRegressor[Length(trees)];
-
     for var cls := 0 to Length(trees) - 1 do
       newTrees[cls] := trees[cls].Clone as DecisionTreeRegressor;
-
     model.fEstimators.Add(newTrees);
   end;
 
   // --- histories ---
-  foreach var v in fTrainLossHistory do
+  foreach var v in fTrainLossHistory do model.fTrainLossHistory.Add(v);
-    model.fTrainLossHistory.Add(v);
+  foreach var v in fValLossHistory do model.fValLossHistory.Add(v);
-
+  foreach var v in fOOBLossHistory do model.fOOBLossHistory.Add(v);
-  foreach var v in fValLossHistory do
-    model.fValLossHistory.Add(v);
-
-  foreach var v in fOOBLossHistory do
-    model.fOOBLossHistory.Add(v);
 
   model.fBestIteration := fBestIteration;
   model.fBestScoreLoss := fBestScoreLoss;
@@ -6938,8 +6960,17 @@ begin
   fMaxIter := maxIter;
   fTol := tol;
   fNInit := nInit;
-  fSeed := seed;
 
+  // --- seed (единый стиль)
+  fRandomSeed := seed;
+  fUserProvidedSeed := seed >= 0;
+
+  if fUserProvidedSeed then
+    fRng := new System.Random(seed)
+  else
+    fRng := new System.Random;
+
+  // --- state
   fFitted := False;
   fFeatureCount := 0;
 
@@ -7077,11 +7108,8 @@ begin
 
   fFeatureCount := p;
 
-  var actualSeed :=
+  // --- базовый RNG (уже создан в конструкторе)
-    if fSeed >= 0 then fSeed
+  var rndBase := fRng;
-    else System.Environment.TickCount and integer.MaxValue;
-
-  var rndBase := new System.Random(actualSeed);
 
   var bestInertia := 1e308;
   var bestCenters: Matrix := nil;
@@ -7090,7 +7118,8 @@ begin
 
   for var run := 1 to fNInit do
   begin
-    var runSeed := rndBase.Next;
+    // --- независимый RNG для каждого запуска
+    var runSeed := rndBase.Next(integer.MaxValue);
     var rnd := new System.Random(runSeed);
 
     var (centers, inertia, iters, converged) := RunSingle(X, rnd);
@@ -7109,7 +7138,7 @@ begin
   fIterations := bestIterations;
   fHasConverged := bestConverged;
   fFitted := True;
-  
+
   Result := Self;
 end;
 
@@ -7188,25 +7217,28 @@ begin
     fMaxIter,
     fTol,
     fNInit,
-    fSeed
+    fRandomSeed
   );
 
+  // --- seed ---
+  km.fRandomSeed := fRandomSeed;
+  km.fUserProvidedSeed := fUserProvidedSeed;
+
+  if fUserProvidedSeed then
+    km.fRng := new System.Random(fRandomSeed)
+  else
+    km.fRng := new System.Random;
+
+  // --- state ---
   km.fFitted := fFitted;
   km.fFeatureCount := fFeatureCount;
   km.fInertia := fInertia;
   km.fIterations := fIterations;
   km.fHasConverged := fHasConverged;
 
+  // --- centers ---
   if fCenters <> nil then
-  begin
+    km.fCenters := fCenters.Clone;
-    var k := fCenters.RowCount;
-    var p := fCenters.ColCount;
-    km.fCenters := new Matrix(k, p);
-
-    for var i := 0 to k - 1 do
-      for var j := 0 to p - 1 do
-        km.fCenters[i,j] := fCenters[i,j];
-  end;
 
   Result := km;
 end;
@@ -7339,25 +7371,6 @@ begin
   Result := Self;
 end;
 
-function DBSCAN.Predict(X: Matrix): Vector;
-begin
-  if not fFitted then
-    NotFittedError(ER_FIT_NOT_CALLED);
-
-  if X = nil then
-    ArgumentNullError(ER_ARG_NULL, 'X');
-
-  // честная защита: Predict работает только для той же выборки
-  if X.RowCount <> Length(fLabels) then
-    ArgumentError(ER_DBSCAN_PREDICT_ONLY_TRAIN_DATA);
-
-  var n := Length(fLabels);
-  Result := new Vector(n);
-
-  for var i := 0 to n - 1 do
-    Result[i] := fLabels[i];
-end;
-
 function DBSCAN.PredictLabels(X: Matrix): array of integer;
 begin
   if X = nil then

bin/Lib/PlotML.pas

@@ -1,4 +1,10 @@
-unit PlotML;
+/// Модуль визуализации на базе InteractiveDataDisplay (WPF).
+/// ВАЖНО:
+/// • Требует Windows и WPF.
+/// • Использует GAC-сборку InteractiveDataDisplay.WPF.
+/// • Не поддерживается на Linux/macOS и в .NET Core без дополнительной настройки.
+/// Рекомендуется использовать только в desktop-сценариях.
+unit PlotML;
 
 {$reference %GAC%\InteractiveDataDisplay.WPF.dll}
 {$reference 'PresentationFramework.dll'}

bin/Lib/PreprocessorABC.pas

@@ -91,7 +91,7 @@ type
     property ColumnName: string read col;
   end;
 
-  ImputeStrategy = (isMean, isConstant);
+  ImputeStrategy = (isMean, isConstant, isMedian);
 
 /// Заполняет пропущенные значения (NA) в числовых столбцах
 /// Поддерживает стратегии isMean и isConstant
@@ -102,6 +102,7 @@ type
     strategy: ImputeStrategy;
     constants: array of object;
     means: array of real;
+    medians: array of real;
     fitted: boolean;
   public
     /// Создаёт Imputer с заполнением средним значением
@@ -189,8 +190,10 @@ const
     'Imputer(constant): value type mismatch for column "{0}"';
   ER_ONEHOT_EMPTY_COLUMN =
     'Столбец "{0}" не содержит категориальных значений!!Column "{0}" contains no categorical values';  
-  ER_COLUMN_NOT_FOUND =
+  ER_IMPUTER_CONSTANTS_INVALID =
-    'Столбец "{0}" не найден!!Column "{0}" not found';    
+    'Массив констант не задан или имеет неверный размер!!Constants array is null or has invalid length';
+  ER_IMPUTER_STRATEGY_NOT_SUPPORTED =
+    'Стратегия импутации {0} не поддерживается!!Imputation strategy {0} is not supported';
   
 //-----------------------------
 //        LabelEncoder
@@ -288,7 +291,13 @@ end;
 
 function OneHotEncoder.Fit(df: DataFrame): IPreprocessor;
 begin
+  if df = nil then
+    ArgumentNullError(ER_ARG_NULL, 'df');
+  
   var idx := df.Schema.IndexOf(col);
+  if idx < 0 then
+    Error(ER_COLUMN_NOT_FOUND, col);
+
   if df.Schema.ColumnTypeAt(idx) <> ColumnType.ctStr then
     Error(ER_ONEHOT_NOT_STRING, col);
 
@@ -299,8 +308,11 @@ begin
   while cur.MoveNext do
   begin
     if not cur.IsValid(idx) then continue;
+
     var s := cur.Str(idx);
-    if not indexByValue.ContainsKey(s) then
+
+    var dummy: integer;
+    if not indexByValue.TryGetValue(s, dummy) then
     begin
       indexByValue[s] := values.Count;
       values.Add(s);
@@ -380,9 +392,6 @@ end;
 
 constructor Imputer.Create(strategy: ImputeStrategy; params columns: array of string);
 begin
-  if strategy <> isMean then
-    ArgumentError(ER_IMPUTER_INVALID_STRATEGY_MEAN);
-
   if (columns = nil) or (columns.Length = 0) then
     ArgumentError(ER_IMPUTER_NO_COLUMNS);
 
@@ -415,35 +424,79 @@ end;
 
 function Imputer.Fit(df: DataFrame): IPreprocessor;
 begin
-  if strategy = isMean then
+  case strategy of
-  begin
+    isMean:
-    SetLength(means, cols.Length);
-
-    for var i := 0 to cols.Length - 1 do
     begin
-      var name := cols[i];
+      SetLength(means, cols.Length);
-      var idx := df.Schema.IndexOf(name);
-      var ct := df.Schema.ColumnTypeAt(idx);
 
-      if not (ct in [ColumnType.ctInt, ColumnType.ctFloat]) then
+      for var i := 0 to cols.Length - 1 do
-        Error(ER_IMPUTER_COLUMN_NOT_NUMERIC, name);
+      begin
+        var name := cols[i];
+        var idx := df.Schema.IndexOf(name);
+        var ct := df.Schema.ColumnTypeAt(idx);
 
-      var sum := 0.0;
+        if not (ct in [ColumnType.ctInt, ColumnType.ctFloat]) then
-      var cnt := 0;
+          Error(ER_IMPUTER_COLUMN_NOT_NUMERIC, name);
 
-      var cur := df.GetCursor;
+        var sum := 0.0;
-      while cur.MoveNext do
+        var cnt := 0;
-        if cur.IsValid(idx) then
-        begin
-          sum += cur.Float(idx);
-          cnt += 1;
-        end;
 
-      if cnt = 0 then
+        var cur := df.GetCursor;
-        Error(ER_IMPUTER_NO_VALID_VALUES, name);
+        while cur.MoveNext do
+          if cur.IsValid(idx) then
+          begin
+            sum += cur.Float(idx);
+            cnt += 1;
+          end;
 
-      means[i] := sum / cnt;
+        if cnt = 0 then
+          Error(ER_IMPUTER_NO_VALID_VALUES, name);
+
+        means[i] := sum / cnt;
+      end;
     end;
+
+    isConstant:
+    begin
+      // ничего делать не нужно
+    end;
+    
+    isMedian:
+    begin
+      SetLength(medians, cols.Length);
+    
+      for var i := 0 to cols.Length - 1 do
+      begin
+        var name := cols[i];
+        var idx := df.Schema.IndexOf(name);
+        var ct := df.Schema.ColumnTypeAt(idx);
+    
+        if not (ct in [ColumnType.ctInt, ColumnType.ctFloat]) then
+          Error(ER_IMPUTER_COLUMN_NOT_NUMERIC, name);
+    
+        // --- собираем значения
+        var values := new List<real>;
+    
+        var cur := df.GetCursor;
+        while cur.MoveNext do
+          if cur.IsValid(idx) then
+            values.Add(cur.Float(idx));
+    
+        if values.Count = 0 then
+          Error(ER_IMPUTER_NO_VALID_VALUES, name);
+    
+        // --- сортируем
+        values.Sort;
+    
+        // --- медиана
+        var n := values.Count;
+        if n mod 2 = 1 then
+          medians[i] := values[n div 2]
+        else
+          medians[i] := (values[n div 2 - 1] + values[n div 2]) / 2.0;
+      end;
+    end;    
+    
   end;
 
   fitted := true;
@@ -455,6 +508,11 @@ begin
   if not fitted then
     NotFittedError(ER_FIT_NOT_CALLED);
 
+  // --- проверка constants
+  if (strategy = isConstant) and 
+     ((constants = nil) or (constants.Length <> cols.Length)) then
+    Error(ER_IMPUTER_CONSTANTS_INVALID);
+
   var res := df;
 
   for var i := 0 to cols.Length - 1 do
@@ -466,50 +524,65 @@ begin
     if not (ct in [ColumnType.ctInt, ColumnType.ctFloat]) then
       Error(ER_IMPUTER_COLUMN_NOT_NUMERIC, name);
 
-    if strategy = isMean then
+    case strategy of
-    begin
+      isMean:
-      var m := means[i];
-      res := res.ReplaceColumnFloat(
-        name,
-        c -> (if c.IsValid(idx) then c.Float(idx) else m)
-      );
-    end
-    else
-    begin
-      var v := constants[i];
-      if v = nil then
-        Error(ER_IMPUTER_CONSTANT_VALUE_NULL, name);
-
-      if ct = ColumnType.ctInt then
       begin
-        var k: integer;
+        var m := means[i];
-        try
-          k := integer(v);
-        except
-          on e: Exception do
-            Error(ER_IMPUTER_CONSTANT_TYPE_MISMATCH, name);
-        end;
-
-        res := res.ReplaceColumnInt(
-          name,
-          c -> (if c.IsValid(idx) then c.Int(idx) else k)
-        );
-      end
-      else
-      begin
-        var r: real;
-        try
-          r := real(v);
-        except
-          on e: Exception do
-            Error(ER_IMPUTER_CONSTANT_TYPE_MISMATCH, name);
-        end;
-
         res := res.ReplaceColumnFloat(
           name,
-          c -> (if c.IsValid(idx) then c.Float(idx) else r)
+          c -> (if c.IsValid(idx) then c.Float(idx) else m)
         );
       end;
+
+      isConstant:
+      begin
+        var v := constants[i];
+        if v = nil then
+          Error(ER_IMPUTER_CONSTANT_VALUE_NULL, name);
+
+        if ct = ColumnType.ctInt then
+        begin
+          var k: integer;
+          try
+            k := integer(v);
+          except
+            on e: Exception do
+              Error(ER_IMPUTER_CONSTANT_TYPE_MISMATCH, name);
+          end;
+
+          res := res.ReplaceColumnInt(
+            name,
+            c -> (if c.IsValid(idx) then c.Int(idx) else k)
+          );
+        end
+        else // ctFloat
+        begin
+          var r: real;
+          try
+            r := real(v);
+          except
+            on e: Exception do
+              Error(ER_IMPUTER_CONSTANT_TYPE_MISMATCH, name);
+          end;
+
+          res := res.ReplaceColumnFloat(
+            name,
+            c -> (if c.IsValid(idx) then c.Float(idx) else r)
+          );
+        end;
+      end;
+      
+      isMedian:
+      begin
+        var m := medians[i];
+        res := res.ReplaceColumnFloat(
+          name,
+          c -> (if c.IsValid(idx) then c.Float(idx) else m)
+        );
+      end;      
+
+      else
+        Error(ER_IMPUTER_STRATEGY_NOT_SUPPORTED, strategy);
     end;
   end;
 
@@ -530,9 +603,23 @@ begin
     isMean:
       Result := 'Imputer(strategy=mean, columns=' + colsStr + ')';
 
+    isMedian:
+      Result := 'Imputer(strategy=median, columns=' + colsStr + ')';
+
     isConstant:
+    begin
+      var valStr :=
+        if (constants <> nil) and (constants.Length > 0) and (constants[0] <> nil) then
+          constants[0].ToString
+        else
+          'null';
+
       Result := 'Imputer(strategy=constant, value=' +
-                constants[0].ToString + ', columns=' + colsStr + ')';
+                valStr + ', columns=' + colsStr + ')';
+    end;
+
+    else
+      Result := 'Imputer(strategy=unknown, columns=' + colsStr + ')';
   end;
 end;
 

bin/Lib/ValidationML.pas

@@ -56,22 +56,26 @@ type
   GridSearch = static class
   public
     /// Выполняет подбор гиперпараметра по заданной сетке значений.
-    /// modelFactory — функция создания модели по значению параметра.
+    /// • modelFactory — функция создания модели по значению параметра (P -> T).
-    /// paramValues — набор тестируемых значений гиперпараметра.
+    /// • paramValues — набор тестируемых значений гиперпараметра типа P.
-    /// X, y — обучающие данные.
+    /// • X, y — обучающие данные.
-    /// k — число фолдов в кросс-валидации.
+    /// • k — число фолдов в кросс-валидации.
-    /// metric — функция оценки качества (yTrue, yPred) → real.
+    /// • metric — функция оценки качества (yTrue, yPred) → real.
-    /// Возвращает кортеж (лучший параметр, лучшее среднее значение метрики,
+    /// • maximize — если true, максимизируется метрика; иначе минимизируется.
-    ///   модель, обученная на всём датасете с лучшим параметром).
+    /// • seed — seed для разбиения на фолды (для воспроизводимости).
-  class function Search<T>(
+    /// Возвращает кортеж:
-    modelFactory: real -> T;
+    /// • лучший параметр,
-    paramValues: array of real;
+    /// • лучшее среднее значение метрики,
-    X: Matrix; y: Vector;
+    /// • модель, обученная на всём датасете с лучшим параметром
-    k: integer;
+    class function Search<T, P>(
-    metric: (Vector, Vector) -> real;
+      modelFactory: P -> T;
-    maximize: boolean := True;
+      paramValues: array of P;
-    seed: integer := -1
+      X: Matrix; y: Vector;
-  ): (real, real, T); where T: class,ISupervisedModel;
+      k: integer;
+      metric: (Vector, Vector) -> real;
+      maximize: boolean := True;
+      seed: integer := -1
+    ): (P, real, T); where T: class, ISupervisedModel;
   end;
 
 implementation
@@ -171,15 +175,15 @@ begin
   if (k < 2) or (k > n) then
     ArgumentError(ER_K_INVALID, k, n);
 
-  var actualSeed := if seed >= 0 then seed
+  // --- RNG (без дублирования логики seed)
-                    else System.Environment.TickCount and integer.MaxValue;
+  var rnd :=
-
+    if seed >= 0 then new System.Random(seed)
-  var rnd := new System.Random(actualSeed);
+    else new System.Random;
 
   // --- 1. Индексы 0..n-1
   var idx := Arr(0..n-1);
 
-  // --- 2. Перемешивание через стандартный Shuffle
+  // --- 2. Перемешивание
   idx.Shuffle(rnd);
 
   var baseSize := n div k;
@@ -224,10 +228,9 @@ begin
   if (k < 2) or (k > n) then
     ArgumentError(ER_K_INVALID_STRATIFIED, k, n);
 
-  var actualSeed := if seed >= 0 then seed
+  var rnd :=
-                    else System.Environment.TickCount and integer.MaxValue;
+    if seed >= 0 then new System.Random(seed)
-
+    else new System.Random;
-  var rnd := new System.Random(actualSeed);
 
   // --- 1. Индексы по классам
   var classMap := new Dictionary<integer, List<integer>>();
@@ -235,7 +238,7 @@ begin
   for var i := 0 to n - 1 do
   begin
     var v := y[i];
-    var cls := integer(v);
+    var cls := Round(v);
 
     if Abs(v - cls) > 1e-12 then
       ArgumentError(ER_STRATIFIED_LABELS_INVALID);
@@ -328,8 +331,12 @@ begin
   var total := 0.0;
   var folds := 0;
   var p := X.ColCount;
+  
+  var baseSeed :=
+    if seed >= 0 then seed
+    else System.Environment.TickCount and integer.MaxValue;
 
-  foreach var (trainIdx, testIdx) in KFold(X.RowCount, k, seed) do
+  foreach var (trainIdx, testIdx) in KFold(X.RowCount, k, baseSeed) do
   begin
     var Xtr := new Matrix(trainIdx.Length, p);
     var ytr := new Vector(trainIdx.Length);
@@ -398,7 +405,11 @@ begin
   var folds := 0;
   var p := X.ColCount;
 
-  foreach var (trainIdx, testIdx) in StratifiedKFold(y, k, seed) do
+  var baseSeed :=
+    if seed >= 0 then seed
+    else System.Environment.TickCount and integer.MaxValue;
+
+  foreach var (trainIdx, testIdx) in StratifiedKFold(y, k, baseSeed) do
   begin
     var Xtr := new Matrix(trainIdx.Length, p);
     var ytr := new Vector(trainIdx.Length);
@@ -441,16 +452,16 @@ end;
 //         GridSearch
 //-----------------------------
 
-class function GridSearch.Search<T>(
+class function GridSearch.Search<T, P>(
-  modelFactory: real -> T;
+  modelFactory: P -> T;
-  paramValues: array of real;
+  paramValues: array of P;
   X: Matrix; 
   y: Vector;
   k: integer;
   metric: (Vector, Vector) -> real;
   maximize: boolean;
   seed: integer
-): (real, real, T); where T: class,ISupervisedModel;
+): (P, real, T); where T: class, ISupervisedModel;
 begin
   if modelFactory = nil then
     ArgumentNullError(ER_ARG_NULL, 'modelFactory');
@@ -474,15 +485,20 @@ begin
     DimensionError(ER_DIM_MISMATCH, X.RowCount, y.Length);
 
   var bestParam := paramValues[0];
-  var bestScore := 
+  var bestScore :=
     if maximize then -1e308 else 1e308;
 
+  var baseSeed :=
+    if seed >= 0 then seed
+    else System.Environment.TickCount and integer.MaxValue;
+
   foreach var param in paramValues do
   begin
     var model := modelFactory(param);
     if model = nil then
       ArgumentError(ER_MODEL_NULL);
-    var avgScore := Validation.CrossValidate(model, X, y, k, metric, seed);
+
+    var avgScore := Validation.CrossValidate(model, X, y, k, metric, baseSeed);
 
     if double.IsNaN(avgScore) or double.IsInfinity(avgScore) then
       ArgumentError(ER_INVALID_VALUE, 'avgScore');
@@ -501,7 +517,7 @@ begin
   var bestModel := modelFactory(bestParam);
   if bestModel = nil then
     ArgumentError(ER_MODEL_NULL);
-  
+
   bestModel := bestModel.Fit(X, y) as T;
 
   Result := (bestParam, bestScore, bestModel);