//+------------------------------------------------------------------+
//|                                                   ScalerBase.mqh |
//|                                             Copyright 2025, Leo. |
//|                                             https://www.mql5.com |
//+------------------------------------------------------------------+
#property copyright "Copyright 2025, Leo."
#property link      "https://www.mql5.com/en/users/nique_372"
#property strict

#ifndef SCALERBYLEO_SCALER_BASE_MQH
#define SCALERBYLEO_SCALER_BASE_MQH

//+------------------------------------------------------------------+
//|                                                                  |
//+------------------------------------------------------------------+
#include "..\\..\\MQLArticles\\Utils\\Basic.mqh"
#include "..\\..\\MQLArticles\\Utils\\File.mqh"

//+------------------------------------------------------------------+
//|                                                                  |
//+------------------------------------------------------------------+
#define SCALER_BASE_FINAL_LINE -1
#define SCALER_BASE_MIN_VALUE 1e-10

//+------------------------------------------------------------------+
//|                         Scaler Base                              |
//+------------------------------------------------------------------+
class ScalerBase : public CLoggerBase
 {
protected:
  string             file_name_out;
  string             prefix_file;
  bool               loaded_scaler;

  bool               use_custom; //Bandera para saber si se usa custom (true) o excluyed (false)
  ulong              start_col;
  ulong              count_cols;
  ulong              excluyed_cols;

  virtual bool       Save() = 0;
  virtual bool       Load(string prefix_name) = 0;

  //--- Métodos auxiliares
  bool               CheckSizeCustom(const matrix &mtx) const;
  bool               CheckSizeExcluded(const matrix &mtx) const;
  bool               CheckSizeCustom(const vector &v) const;
  bool               CheckSizeExcluded(const vector &v) const;

  //---
  matrix             ExtractMatrixToScale(const matrix &X) const;
  matrix             ReconstructMatrix(const matrix &X_original, const matrix &X_scaled) const;
  vector             ExtractVectorToScale(const vector &X) const;
  vector             ReconstructVector(const vector &X_original, const vector &X_scaled) const;

public:
                     ScalerBase(void);
                    ~ScalerBase(void) {}

  //---
  inline void        SetRangeEscaler(ulong start_col_, ulong count_col_); //Custom
  inline void        SetRangeEscaler(ulong excluyed_cols_ = 1); //Mas simple, el usuario decide cuantas columnas empezando por atras se excluyen

  //---
  inline bool        save(string prefix_name);
  inline bool        load(string prefix_name);

  //--- Transform
  //- Fit
  // Matrix
  virtual matrix     fit_transform(const matrix &X, bool save_data) = 0;

  // Transorfmra un archivo
  bool               fit_transform_file(const string &file_name, ushort sep, int extra_file_flags, bool save_data, uint file_code_page = CP_ACP, int start_line = 1);

  // Transforma un archivo y devuelve la matriz
  matrix             fit_transform_by_file(const string& file_name, ushort sep, int file_flags, bool save_data, uint file_code_page = CP_ACP,
      int start_line = 1, int end_line = SCALER_BASE_FINAL_LINE);

  // Transforma un string
  matrix             fit_transform_by_src(const string& src, ushort sep, bool save_data, int str_start_line = 1, int str_end_line = SCALER_BASE_FINAL_LINE); // Transforma string

  // Vector
  virtual vector     fit_transform(const vector &X) = 0; //Para vectores no se guarda data


  //- Normal
  virtual matrix     transform(const matrix &X,  bool solo_escalar_lo_previsto = false, bool reconstruir = false) = 0;
  virtual vector     transform(const vector &X,  bool solo_escalar_lo_previsto = false, bool reconstruir = false) = 0;
  virtual matrix     inverse_transform(const matrix &X_scaled,  bool solo_escalar_lo_previsto = false, bool reconstruir = false) = 0;
  virtual vector     inverse_transform(const vector &X_scaled,  bool solo_escalar_lo_previsto = false, bool reconstruir = false) = 0;

  // --- Métodos comunes ---
  virtual inline string  GetOutputFile() const final { return this.file_name_out; }
 };

//+------------------------------------------------------------------+
//|                                                                  |
//+------------------------------------------------------------------+
ScalerBase::ScalerBase(void)
  : start_col(0), count_cols(0), use_custom(false), excluyed_cols(1), file_name_out(NULL), loaded_scaler(false)
 {
 }

//+------------------------------------------------------------------+
//|                                                                  |
//+------------------------------------------------------------------+
inline void ScalerBase::SetRangeEscaler(ulong start_col_, ulong count_col_)
 {
  this.use_custom = true;
  this.count_cols = count_col_;
  this.start_col = start_col_;
  LogInfo(StringFormat("Configurado escalado CUSTOM: columnas %I64u a %I64u (%I64u columnas)", start_col_, start_col_ + count_col_ - 1, count_col_), FUNCION_ACTUAL);
 }

//+------------------------------------------------------------------+
inline void ScalerBase::SetRangeEscaler(ulong excluyed_cols_ = 1)
 {
  this.excluyed_cols = excluyed_cols_;
  this.use_custom = false;
  LogInfo(StringFormat("Configurado escalado EXCLUDED: excluir últimas %I64u columnas", excluyed_cols_), FUNCION_ACTUAL);
 }

//+------------------------------------------------------------------+
//|                                                                  |
//+------------------------------------------------------------------+
bool ScalerBase::CheckSizeCustom(const matrix &mtx) const
 {
  if(start_col >= mtx.Cols())
   {
    LogError(StringFormat("Columna de inicio %I64u >= total columnas %I64u", start_col, mtx.Cols()), FUNCION_ACTUAL);
    return false;
   }

  if(start_col + count_cols > mtx.Cols())
   {
    LogError(StringFormat("Rango [%I64u:%I64u] excede columnas disponibles %I64u", start_col, start_col + count_cols - 1, mtx.Cols()), FUNCION_ACTUAL);
    return false;
   }

  return true;
 }

//+------------------------------------------------------------------+
bool ScalerBase::CheckSizeExcluded(const matrix &mtx) const
 {
  if(mtx.Cols() < excluyed_cols)
   {
    LogError(StringFormat("Columnas a excluir %I64u >= total columnas %I64u", excluyed_cols, mtx.Cols()), FUNCION_ACTUAL);
    return false;
   }

  return true;
 }


//+------------------------------------------------------------------+
bool ScalerBase::CheckSizeCustom(const vector &v) const
 {
  if(start_col >= v.Size())
   {
    LogError(StringFormat("Columna de inicio %I64u >= tamaño total del vector%I64u", start_col, v.Size()), FUNCION_ACTUAL);
    return false;
   }

  if(start_col + count_cols > v.Size())
   {
    LogError(StringFormat("Rango [%I64u:%I64u] excede el tamaño del vector %I64u", start_col, start_col + count_cols - 1, v.Size()), FUNCION_ACTUAL);
    return false;
   }

  return true;
 }

//+------------------------------------------------------------------+
bool ScalerBase::CheckSizeExcluded(const vector &v) const
 {
  if(v.Size() < excluyed_cols)
   {
    LogError(StringFormat("Columnas a excluir %I64u >= tamaño del vector: %I64u", excluyed_cols, v.Size()), FUNCION_ACTUAL);
    return false;
   }

  return true;
 }


//+------------------------------------------------------------------+
//|                                                                  |
//+------------------------------------------------------------------+
matrix ScalerBase::ExtractMatrixToScale(const matrix &X) const
 {
  matrix result;

  if(use_custom)
   {
    if(X.Cols() == count_cols)
      return X;

    result.Init(X.Rows(), count_cols);

    for(ulong row = 0; row < X.Rows(); row++)
      for(ulong col = 0; col < count_cols; col++)
        result[row][col] = X[row][start_col + col];
   }
  else
   {
    if(excluyed_cols == 0)
      return X;

    ulong cols_to_scale = X.Cols() - excluyed_cols;
    result.Init(X.Rows(), cols_to_scale);

    for(ulong row = 0; row < X.Rows(); row++)
      for(ulong col = 0; col < cols_to_scale; col++)
        result[row][col] = X[row][col];
   }

  return result;
 }

//+------------------------------------------------------------------+
matrix ScalerBase::ReconstructMatrix(const matrix &X_original, const matrix &X_scaled) const
 {
  if(X_original.Rows() == X_scaled.Rows() && X_original.Cols() == X_scaled.Cols())
    return X_scaled;

  matrix result = X_original; // Copia completa

  if(use_custom)
   {
    for(ulong row = 0; row < X_original.Rows(); row++)
      for(ulong col = 0; col < count_cols; col++)
        result[row][start_col + col] = X_scaled[row][col];
   }
  else
   {
    for(ulong row = 0; row < X_original.Rows(); row++)
      for(ulong col = 0; col < X_scaled.Cols(); col++)
        result[row][col] = X_scaled[row][col];
   }

  return result;
 }

//+------------------------------------------------------------------+
vector ScalerBase::ExtractVectorToScale(const vector &X) const
 {
  vector result;

  if(use_custom)
   {
    if(X.Size() == count_cols)
      return X;

    // Extraer rango específico
    result.Resize(count_cols);
    for(ulong i = 0; i < count_cols; i++)
      result[i] = X[start_col + i];
   }
  else
   {
    if(excluyed_cols == 0)
      return X;

    // Extraer todas excepto las últimas N
    ulong size_to_scale = X.Size() - excluyed_cols;
    result.Resize(size_to_scale);
    for(ulong i = 0; i < size_to_scale; i++)
      result[i] = X[i];
   }

  return result;
 }

//+------------------------------------------------------------------+
vector ScalerBase::ReconstructVector(const vector &X_original, const vector &X_scaled) const
 {
  if(X_original.Size() == X_scaled.Size())
    return X_scaled;

  vector result = X_original;
  if(use_custom)
   {
    // Reemplazar rango específico
    for(ulong i = 0; i < count_cols; i++)
      result[start_col + i] = X_scaled[i];
   }
  else
   {
    // Reemplazar todas excepto las últimas N
    for(ulong i = 0; i < X_scaled.Size(); i++)
      result[i] = X_scaled[i];
   }

  return result;
 }

//+------------------------------------------------------------------+
//|                                                                  |
//+------------------------------------------------------------------+
inline bool ScalerBase::load(string prefix_name)
 {
  loaded_scaler = true;
  return this.Load(prefix_name);
 }

//+------------------------------------------------------------------+
inline bool ScalerBase::save(string prefix_name)
 {
  this.file_name_out = prefix_name + this.prefix_file;
  return this.Save();
 }


//+------------------------------------------------------------------+
//|                                                                  |
//+------------------------------------------------------------------+
/*
Notas:
- file_flags: puede tener los valores de FILE_ANSI, FILE_COMON, etc.. las banderas (FILE_READ) ya bienen por derfecto
*/

//+------------------------------------------------------------------+
matrix ScalerBase::fit_transform_by_file(const string& file_name, ushort sep, int file_flags, bool save_data, uint file_code_page = CP_ACP,
    int start_line = 1, int end_line = SCALER_BASE_FINAL_LINE)
 {
//---
  ResetLastError();

//---
  matrix m = {};
  const int file_handle = FileOpen(file_name, (file_flags | FILE_READ | FILE_TXT), '\n', file_code_page);
  if(file_handle == INVALID_HANDLE)
   {
    LogCriticalError(StringFormat("Error al abrir el archivo = %s, ultimo erorr = %d", file_name, GetLastError()), FUNCION_ACTUAL);
    return m;
   }

//---
  int curr_line = 0;
  string arr[];
  int dt = 0;
  int curr_row = 0;
  string line = "";

//--- Se lee hasta el final
  if(end_line == SCALER_BASE_FINAL_LINE)
   {
    //---
    while(!FileIsEnding(file_handle))
     {
      //---
      line = FileReadString(file_handle);

      //---
      if(curr_line >= start_line)
       {
        //---
        if((dt = StringSplit(line, sep, arr)) < 1)
         {
          LogError(StringFormat("Linea mal formada = %d:\n%s", curr_line, line), FUNCION_ACTUAL);
          FileClose(file_handle);
          return m;
         }

        //---
        m.Resize(curr_row + 1, dt);
        for(int i = 0; i < dt; i++)
         {
          m[curr_row][i] = double(arr[i]);
         }

        //---
        curr_row++;
       }

      //---
      curr_line++;
     }
   }
  else
   {
    //---
    while(!FileIsEnding(file_handle))
     {
      //---
      line = FileReadString(file_handle);

      //---
      if(curr_line >= start_line)
       {
        //---
        if((dt = StringSplit(line, sep, arr)) < 1)
         {
          LogError(StringFormat("Linea mal formada = %d:\n%s", curr_line, line), FUNCION_ACTUAL);
          FileClose(file_handle);
          return m;
         }

        //---
        m.Resize(curr_row + 1, dt);
        for(int i = 0; i < dt; i++)
         {
          m[curr_row][i] = double(arr[i]);
         }

        //---
        curr_row++;
       }

      //---
      curr_line++;
      if(curr_line > end_line)
        break;
     }
   }

//---
  FileClose(file_handle);

//---
  return fit_transform(m, save_data);
 }

//+------------------------------------------------------------------+
bool ScalerBase::fit_transform_file(const string &file_name, ushort sep, int extra_file_flags, bool save_data, uint file_code_page = CP_ACP, int start_line = 1)
 {
//---
  ResetLastError();

//---
  matrix m = {};
  int file_handle = FileOpen(file_name, (extra_file_flags | FILE_TXT | FILE_READ), '\n', file_code_page);
  if(file_handle == INVALID_HANDLE)
   {
    LogCriticalError(StringFormat("Error al abrir el archivo = %s, ultimo erorr = %d", file_name, GetLastError()), FUNCION_ACTUAL);
    return false;
   }

//---
  int curr_line = 0;
  string arr[];
  int dt = 0;
  int curr_row = 0;
  string line = "";
  string extra[];
  ArrayResize(extra, 0);


//--- Se lee hasta el final
  while(!FileIsEnding(file_handle))
   {
    //---
    line = FileReadString(file_handle);
    //PrintFormat("'%s'", line);

    //---
    if(curr_line >= start_line)
     {
      //---
      if((dt = StringSplit(line, sep, arr)) < 1)
       {
        LogError(StringFormat("Linea mal formada = %d:\n%s", curr_line, line), FUNCION_ACTUAL);
        FileClose(file_handle);
        return false;
       }

      //---
      m.Resize(curr_row + 1, dt);
      for(int i = 0; i < dt; i++)
       {
        m[curr_row][i] = double(arr[i]);
       }

      //---
      curr_row++;
     }
    else
     {
      extra[ArrayResize(extra, ArraySize(extra) + 1) - 1] = line;
     }

    //---
    curr_line++;
   }


//--- Transformamos y cerramos
  m = fit_transform(m, save_data);
  FileClose(file_handle);

//--- Reescribir
// Abrimos pero en escritura
  file_handle = FileOpen(file_name, (extra_file_flags | FILE_TXT | FILE_WRITE), 0, file_code_page);
  if(file_handle == INVALID_HANDLE)
   {
    LogCriticalError(StringFormat("Error al abrir el archivo = %s, ultimo erorr = %d", file_name, GetLastError()), FUNCION_ACTUAL);
    return false;
   }

// Init line
  for(int i = 0; i < ArraySize(extra); i++)
    FileWrite(file_handle, extra[i]);

// Matrix
  const ulong rows = m.Rows();
  const ulong cols = m.Cols();
  for(ulong row = 0; row < rows; row++)
   {
    //---
    line = "";
    for(ulong col = 0; col <  cols; col++)
     {
      line += string(m[row][col]) + ", ";
     }
    StringSetLength(line, StringLen(line) - 2);

    //---
    FileWrite(file_handle, line);
   }

//--- Ceramos el archivo
  FileClose(file_handle);

//---
  return true;
 }

//+------------------------------------------------------------------+
matrix ScalerBase::fit_transform_by_src(const string& src, ushort sep, bool save_data, int str_start_line = 1, int str_end_line = SCALER_BASE_FINAL_LINE)
 {
//---
  matrix m = {};

//---
  int curr_line = 0;
  string arr[];
  int dt = 0;
  int curr_row = 0;
  string general_arr[];
  string line = "";

//---
  const int total_row = StringSplit(src, '\n', general_arr);
  if(total_row <= str_start_line)
   {
    LogError(StringFormat("Numero de filas del string %d es invalida", total_row), FUNCION_ACTUAL);
    return m;
   }

//--- Se lee hasta el final
  if(str_end_line == SCALER_BASE_FINAL_LINE)
   {
    //---
    for(int r = 0; r < total_row; r++)
     {
      //---
      line = general_arr[r];

      //---
      if(curr_line >= str_start_line)
       {
        //---
        if((dt = StringSplit(line, sep, arr)) < 1)
         {
          LogError(StringFormat("Linea mal formada = %d:\n%s", curr_line, line), FUNCION_ACTUAL);
          return m;
         }

        //---
        m.Resize(curr_row + 1, dt);
        for(int i = 0; i < dt; i++)
         {
          m[curr_row][i] = double(arr[i]);
         }

        //---
        curr_row++;
       }

      //---
      curr_line++;
     }
   }
  else
   {
    //---
    for(int r = 0; r < total_row; r++)
     {
      //---
      line = general_arr[r];

      //---
      if(curr_line >= str_start_line)
       {
        //---
        if((dt = StringSplit(line, sep, arr)) < 1)
         {
          LogError(StringFormat("Linea mal formada = %d:\n%s", curr_line, line), FUNCION_ACTUAL);
          return m;
         }

        //---
        m.Resize(curr_row + 1, dt);
        for(int i = 0; i < dt; i++)
         {
          m[curr_row][i] = double(arr[i]);
         }

        //---
        curr_row++;
       }

      //---
      curr_line++;
      if(curr_line > str_end_line)
        break;
     }
   }

//---
  return fit_transform(m, save_data);
 }


//+------------------------------------------------------------------+
//|               Standardization Scaler                             |
//+------------------------------------------------------------------+
class StandardizationScaler : public ScalerBase
 {
protected:
  vector             mean, std;
  bool               Save() override;
  bool               Load(string prefix_name) override;

public:
                     StandardizationScaler() : ScalerBase() { this.prefix_file = "_mean_std.csv"; }

  matrix             fit_transform(const matrix &X, bool save_data) override;
  vector             fit_transform(const vector &X) override; //Para vectores no se guarda data
  matrix             transform(const matrix &X, bool solo_escalar_lo_previsto = false, bool reconstruir = false) override;
  vector             transform(const vector &X, bool solo_escalar_lo_previsto = false, bool reconstruir = false) override;
  matrix             inverse_transform(const matrix &X_scaled, bool solo_escalar_lo_previsto = false, bool reconstruir = false) override;
  vector             inverse_transform(const vector &X_scaled, bool solo_escalar_lo_previsto = false, bool reconstruir = false) override;
 };

//+------------------------------------------------------------------+
//|                                                                  |
//+------------------------------------------------------------------+
bool StandardizationScaler::Save()
 {
  FileDelete(this.file_name_out);
  ResetLastError();

  int handle = FileOpen(this.file_name_out, FILE_WRITE | FILE_CSV | FILE_COMMON);
  if(handle == INVALID_HANDLE)
   {
    LogFatalError(StringFormat("Invalid handle Err= %d >> Filename= %s", GetLastError(), this.file_name_out), FUNCION_ACTUAL);
    return false;
   }

  FileWrite(handle, vector_to_string(this.mean));
  FileWrite(handle, vector_to_string(this.std));
  FileWrite(handle, count_cols);
  FileWrite(handle, start_col);
  FileWrite(handle, excluyed_cols);
  FileWrite(handle, (int)use_custom);

  FileClose(handle);
  return true;
 }

//+------------------------------------------------------------------+
bool StandardizationScaler::Load(string prefix_name)
 {
  this.file_name_out = prefix_name + this.prefix_file;

  ResetLastError();
  int handle = FileOpen(file_name_out, FILE_READ | FILE_CSV | FILE_COMMON, "\n");
  if(handle == INVALID_HANDLE)
   {
    LogFatalError(StringFormat("Invalid handle Err= %d >> Filename= %s", GetLastError(), this.file_name_out), FUNCION_ACTUAL);
    return false;
   }

  this.mean = string_to_vector(FileReadString(handle));
  this.std = string_to_vector(FileReadString(handle));
  this.count_cols = (ulong)StringToInteger(FileReadString(handle));
  this.start_col = (ulong)StringToInteger(FileReadString(handle));
  this.excluyed_cols = (ulong)StringToInteger(FileReadString(handle));
  this.use_custom = (bool)StringToInteger(FileReadString(handle));

  FileClose(handle);
  return true;
 }

//+------------------------------------------------------------------+
//|                                                                  |
//+------------------------------------------------------------------+
vector StandardizationScaler::fit_transform(const vector &X)
 {
  if(loaded_scaler)
   {
    LogWarning("Este es un escalador cargado >> no es necesario ajustarlo a los nuevos datos, llame a otra instancia de una clase", FUNCION_ACTUAL);
    return transform(X);
   }

//---
  if(use_custom)
   {
    if(!CheckSizeCustom(X))
      return X;
   }
  else
   {
    if(!CheckSizeExcluded(X))
      return X;
   }

//---
  vector X_to_scaled = ExtractVectorToScale(X);

//---
  double mean_val = X_to_scaled.Mean();
  double std_val = X_to_scaled.Std();

  if(std_val < SCALER_BASE_MIN_VALUE)
    std_val = 1.0;

//---
  for(ulong i = 0; i < X_to_scaled.Size(); i++)
    X_to_scaled[i] = (X_to_scaled[i] - mean_val) / std_val;

//---
  return ReconstructVector(X, X_to_scaled);
 }

//+------------------------------------------------------------------+
matrix StandardizationScaler::fit_transform(const matrix &X, bool save_data)
 {
  if(loaded_scaler)
   {
    LogWarning("Este es un escalador cargado >> no es necesario ajustarlo a los nuevos datos, llame a otra instancia de una clase", FUNCION_ACTUAL);
    return transform(X);
   }

  LogInfo(StringFormat("Numero de columnas de entrada: %I64u", X.Cols()), FUNCION_ACTUAL);

//---
  if(use_custom)
   {
    if(!CheckSizeCustom(X))
      return X;
   }
  else
   {
    if(!CheckSizeExcluded(X))
      return X;
   }

//---
  matrix X_to_scale = ExtractMatrixToScale(X);
  const ulong X_to_scale_cols = X_to_scale.Cols();
  const ulong X_to_scale_rows = X_to_scale.Rows();

  LogInfo(StringFormat("Columnas a escalar: %I64u", X_to_scale.Cols()), FUNCION_ACTUAL);

//---
  vector mean_cts(X_to_scale_cols);
  vector std_cts(X_to_scale_cols);

//---
  for(ulong i = 0; i < X_to_scale_cols; i++)
   {
    mean_cts[i] = X_to_scale.Col(i).Mean();
    std_cts[i] = X_to_scale.Col(i).Std();

    // Evitar división por cero
    if(std_cts[i] < SCALER_BASE_MIN_VALUE)
     {
      LogWarning(StringFormat("Columna %I64u tiene std muy pequeño (%.2e), usando 1.0", i, std_cts[i]), FUNCION_ACTUAL);
      std_cts[i] = 1.0;
     }
   }

//---
  for(ulong row = 0; row < X_to_scale_rows; row++)
    for(ulong col = 0; col < X_to_scale_cols; col++)
      X_to_scale[row][col] = (X_to_scale[row][col] - mean_cts[col]) / std_cts[col];

//---
  if(save_data)
   {
    this.mean = mean_cts;
    this.std = std_cts;
   }

//--- Aqui siempre se reconstruye
  return ReconstructMatrix(X, X_to_scale);
 }

//+------------------------------------------------------------------+
//|                                                                  |
//+------------------------------------------------------------------+
matrix StandardizationScaler::transform(const matrix &X, bool solo_escalar_lo_previsto = false, bool reconstruir = false)
 {
  if(!loaded_scaler)
   {
    LogError("Primero llame a fit_transform o load() antes de transform", FUNCION_ACTUAL);
    return X;
   }

//---
  if(use_custom)
   {
    if(!CheckSizeCustom(X))
      return X;
   }
  else
   {
    if(!CheckSizeExcluded(X))
      return X;
   }

//---
  matrix X_to_scale = solo_escalar_lo_previsto ? ExtractMatrixToScale(X) : X;
  const ulong X_to_scale_cols = X_to_scale.Cols();
  const ulong X_to_scale_rows = X_to_scale.Rows();

//---
  if(X_to_scale_cols != this.mean.Size())
   {
    LogError(StringFormat("Columnas a escalar %I64u != columnas entrenadas %u", X_to_scale_cols, this.mean.Size()), FUNCION_ACTUAL);
    return X;
   }

//---
  for(ulong row = 0; row < X_to_scale_rows; row++)
    for(ulong col = 0; col < X_to_scale_cols; col++)
      X_to_scale[row][col] = (X_to_scale[row][col] - this.mean[col]) / this.std[col];

//---
  return (reconstruir) ?  ReconstructMatrix(X, X_to_scale) : X_to_scale;
 }

//+------------------------------------------------------------------+
vector StandardizationScaler::transform(const vector &X, bool solo_escalar_lo_previsto = false, bool reconstruir = false)
 {
  if(!loaded_scaler)
   {
    LogError("Primero llame a fit_transform o load() antes de transform", FUNCION_ACTUAL);
    return X;
   }

//---
  vector X_to_scale = solo_escalar_lo_previsto ? ExtractVectorToScale(X) : X;
  const ulong X_to_scale_size = X_to_scale.Size();

  if(X_to_scale_size != this.mean.Size())
   {
    LogError(StringFormat("Elementos a escalar %I64 != elementos entrenados %u", X_to_scale_size, this.mean.Size()), FUNCION_ACTUAL);
    return X;
   }

//---
  for(ulong i = 0; i < X_to_scale_size; i++)
    X_to_scale[i] = (X_to_scale[i] - this.mean[i]) / this.std[i];

//---
  return (reconstruir) ? ReconstructVector(X, X_to_scale) : X_to_scale;
 }

//+------------------------------------------------------------------+
//|                                                                  |
//+------------------------------------------------------------------+
matrix StandardizationScaler::inverse_transform(const matrix &X_scaled, bool solo_escalar_lo_previsto = false, bool reconstruir = false)
 {
  if(!loaded_scaler)
   {
    LogError("Escalador no entrenado. Llame primero a fit_transform", FUNCION_ACTUAL);
    return X_scaled;
   }

//---
  matrix X_to_unscale = (solo_escalar_lo_previsto) ? ExtractMatrixToScale(X_scaled) : X_scaled;
  const ulong X_to_unscale_cols = X_to_unscale.Cols();
  const ulong X_to_unscale_rows = X_to_unscale.Rows();

//---
  if(X_to_unscale_cols != this.mean.Size())
   {
    LogError(StringFormat("Columnas escaladas %I64u != columnas entrenadas %u", X_to_unscale_cols, this.mean.Size()), FUNCION_ACTUAL);
    return X_scaled;
   }

//---
  for(ulong row = 0; row < X_to_unscale_rows; row++)
    for(ulong col = 0; col < X_to_unscale_cols; col++)
      X_to_unscale[row][col] = X_to_unscale[row][col] * this.std[col] + this.mean[col];

//---
  return (reconstruir) ?  ReconstructMatrix(X_scaled, X_to_unscale) : X_to_unscale;
 }

//+------------------------------------------------------------------+
vector StandardizationScaler::inverse_transform(const vector &X_scaled, bool solo_escalar_lo_previsto = false, bool reconstruir = false)
 {
  if(!loaded_scaler)
   {
    LogError("Escalador no entrenado. Llame primero a fit_transform", FUNCION_ACTUAL);
    return X_scaled;
   }

//---
  vector X_to_unscale = (solo_escalar_lo_previsto) ?  ExtractVectorToScale(X_scaled) : X_scaled;
  const ulong X_to_unscale_size = X_to_unscale.Size();

//---
  if(X_to_unscale_size != this.mean.Size())
   {
    LogError(StringFormat("Elementos escalados %I64u != elementos entrenados %u", X_to_unscale_size, this.mean.Size()), FUNCION_ACTUAL);
    return X_scaled;
   }

//---
  for(ulong i = 0; i < X_to_unscale_size; i++)
    X_to_unscale[i] = X_to_unscale[i] * this.std[i] + this.mean[i];

//---
  return (reconstruir) ?  ReconstructVector(X_scaled, X_to_unscale) : X_to_unscale;
 }



//+------------------------------------------------------------------+
//|                    MaxMin Scaler                                 |
//+------------------------------------------------------------------+
class MaxMinScaler : public ScalerBase
 {
protected:
  vector             min_vals, max_vals;

  bool               Save() override;
  bool               Load(string prefix_name) override;

public:
                     MaxMinScaler() : ScalerBase() { this.prefix_file = "_min_max.csv"; }

  vector             fit_transform(const vector &X) override;
  matrix             fit_transform(const matrix &X, bool save_data) override;
  matrix             transform(const matrix &X, bool solo_escalar_lo_previsto = false, bool reconstruir = false) override;
  vector             transform(const vector &X, bool solo_escalar_lo_previsto = false, bool reconstruir = false) override;
  matrix             inverse_transform(const matrix &X_scaled, bool solo_escalar_lo_previsto = false, bool reconstruir = false) override;
  vector             inverse_transform(const vector &X_scaled, bool solo_escalar_lo_previsto = false, bool reconstruir = false) override;
 };

//+------------------------------------------------------------------+
//|                                                                  |
//+------------------------------------------------------------------+
bool MaxMinScaler::Save()
 {
  FileDelete(this.file_name_out);
  ResetLastError();

//---
  int handle = FileOpen(this.file_name_out, FILE_WRITE | FILE_CSV | FILE_COMMON);
  if(handle == INVALID_HANDLE)
   {
    LogFatalError(StringFormat("Invalid handle Err= %d >> Filename= %s", GetLastError(), this.file_name_out), FUNCION_ACTUAL);
    return false;
   }

//---
  FileWrite(handle, vector_to_string(this.min_vals));
  FileWrite(handle, vector_to_string(this.max_vals));
  FileWrite(handle, count_cols);
  FileWrite(handle, start_col);
  FileWrite(handle, excluyed_cols);
  FileWrite(handle, (int)use_custom);

//---
  FileClose(handle);
  return true;
 }

//+------------------------------------------------------------------+
bool MaxMinScaler::Load(string prefix_name)
 {
  this.file_name_out = prefix_name + this.prefix_file;

//---
  ResetLastError();
  int handle = FileOpen(file_name_out, FILE_READ | FILE_CSV | FILE_COMMON, "\n");
  if(handle == INVALID_HANDLE)
   {
    LogFatalError(StringFormat("Invalid handle Err= %d >> Filename= %s", GetLastError(), this.file_name_out), FUNCION_ACTUAL);
    return false;
   }

//---
  this.min_vals = string_to_vector(FileReadString(handle));
  this.max_vals = string_to_vector(FileReadString(handle));
  this.count_cols = (ulong)StringToInteger(FileReadString(handle));
  this.start_col = (ulong)StringToInteger(FileReadString(handle));
  this.excluyed_cols = (ulong)StringToInteger(FileReadString(handle));
  this.use_custom = (bool)StringToInteger(FileReadString(handle));

//---
  FileClose(handle);
  return true;
 }

//+------------------------------------------------------------------+
//|                                                                  |
//+------------------------------------------------------------------+
vector MaxMinScaler::fit_transform(const vector &X)
 {
  if(loaded_scaler)
   {
    LogWarning("Este es un escalador cargado >> no es necesario ajustarlo a los nuevos datos, llame a otra instancia de una clase", FUNCION_ACTUAL);
    return transform(X);
   }

//---
  if(use_custom)
   {
    if(!CheckSizeCustom(X))
      return X;
   }
  else
   {
    if(!CheckSizeExcluded(X))
      return X;
   }

//---
  vector X_to_scale = ExtractVectorToScale(X);

//---
  double max = X_to_scale.Max();
  double min = X_to_scale.Min();
  if(max - min < SCALER_BASE_MIN_VALUE)
    max = min + 1.0;

//---
  for(ulong i = 0; i < X_to_scale.Size(); i++)
    X_to_scale[i] = (X_to_scale[i] - min) / (max - min);

//---
  return ReconstructVector(X, X_to_scale);
 }

//+------------------------------------------------------------------+
matrix MaxMinScaler::fit_transform(const matrix &X, bool save_data)
 {
  if(loaded_scaler)
   {
    LogWarning("Este es un escalador cargado >> no es necesario ajustarlo a los nuevos datos, llame a otra instancia de una clase", FUNCION_ACTUAL);
    return transform(X);
   }

  LogInfo(StringFormat("Numero de columnas de entrada: %I64u", X.Cols()), FUNCION_ACTUAL);

//---
  if(use_custom)
   {
    if(!CheckSizeCustom(X))
      return X;
   }
  else
   {
    if(!CheckSizeExcluded(X))
      return X;
   }

//---
  matrix X_to_scale = ExtractMatrixToScale(X);
  const ulong X_to_scale_cols = X_to_scale.Cols();
  const ulong X_to_scale_rows = X_to_scale.Rows();

  LogInfo(StringFormat("Columnas a escalar: %I64u", X_to_scale_cols), FUNCION_ACTUAL);

//---
  vector min_vals_cts(X_to_scale_cols);
  vector max_vals_cts(X_to_scale_cols);

//---
  for(ulong i = 0; i < X_to_scale_cols; i++)
   {
    min_vals_cts[i] = X_to_scale.Col(i).Min();
    max_vals_cts[i] = X_to_scale.Col(i).Max();

    if(fabs(max_vals_cts[i] - min_vals_cts[i]) < SCALER_BASE_MIN_VALUE)
     {
      LogWarning(StringFormat("Columna %I64u tiene rango muy pequeño (%.2e), usando rango 1.0", i, max_vals_cts[i] - min_vals_cts[i]), FUNCION_ACTUAL);
      max_vals_cts[i] = min_vals_cts[i] + 1.0;
     }
   }

//---
//Print(min_vals_cts);

//---
  for(ulong row = 0; row < X_to_scale_rows; row++)
    for(ulong col = 0; col < X_to_scale_cols; col++)
      X_to_scale[row][col] = (X_to_scale[row][col] - min_vals_cts[col]) / (max_vals_cts[col] - min_vals_cts[col]);

//---
  if(save_data)
   {
    this.min_vals = min_vals_cts;
    this.max_vals = max_vals_cts;
   }

//--- Aqui siempre se reconstruye
  return ReconstructMatrix(X, X_to_scale);
 }

//+------------------------------------------------------------------+
//|                                                                  |
//+------------------------------------------------------------------+
matrix MaxMinScaler::transform(const matrix &X, bool solo_escalar_lo_previsto = false, bool reconstruir = false)
 {
  if(!loaded_scaler)
   {
    LogError("Primero llame a fit_transform o load() antes de transform", FUNCION_ACTUAL);
    return X;
   }

//---
  if(use_custom)
   {
    if(!CheckSizeCustom(X))
      return X;
   }
  else
   {
    if(!CheckSizeExcluded(X))
      return X;
   }

//---
  matrix X_to_scale = solo_escalar_lo_previsto ? ExtractMatrixToScale(X) : X;
  const ulong X_to_scale_cols = X_to_scale.Cols();
  const ulong X_to_scale_rows = X_to_scale.Rows();

//---
  if(X_to_scale_cols != this.min_vals.Size())
   {
    LogError(StringFormat("Columnas a escalar %I64u != columnas entrenadas %u", X_to_scale_cols, this.min_vals.Size()), FUNCION_ACTUAL);
    return X;
   }

//---
  for(ulong row = 0; row < X_to_scale_rows; row++)
    for(ulong col = 0; col < X_to_scale_cols; col++)
      X_to_scale[row][col] = (X_to_scale[row][col] - this.min_vals[col]) / (this.max_vals[col] - this.min_vals[col]);

//---
  return (reconstruir) ?  ReconstructMatrix(X, X_to_scale) : X_to_scale;
 }

//+------------------------------------------------------------------+
vector MaxMinScaler::transform(const vector &X, bool solo_escalar_lo_previsto = false, bool reconstruir = false)
 {
  if(!loaded_scaler)
   {
    LogError("Primero llame a fit_transform o load() antes de transform", FUNCION_ACTUAL);
    return X;
   }

//---
  vector X_to_scale = solo_escalar_lo_previsto ? ExtractVectorToScale(X) : X;
  const ulong X_to_scale_size = X_to_scale.Size();

  if(X_to_scale_size != this.min_vals.Size())
   {
    LogError(StringFormat("Elementos a escalar %I64u != elementos entrenados %u", X_to_scale_size, this.min_vals.Size()), FUNCION_ACTUAL);
    return X;
   }

//---
  for(ulong i = 0; i < X_to_scale_size; i++)
    X_to_scale[i] = (X_to_scale[i] - this.min_vals[i]) / (this.max_vals[i] - this.min_vals[i]);

//---
  return (reconstruir) ? ReconstructVector(X, X_to_scale) : X_to_scale;
 }

//+------------------------------------------------------------------+
//|                                                                  |
//+------------------------------------------------------------------+
matrix MaxMinScaler::inverse_transform(const matrix &X_scaled, bool solo_escalar_lo_previsto = false, bool reconstruir = false)
 {
  if(!loaded_scaler)
   {
    LogError("Escalador no entrenado. Llame primero a fit_transform", FUNCION_ACTUAL);
    return X_scaled;
   }

//---
  matrix X_to_unscale = (solo_escalar_lo_previsto) ? ExtractMatrixToScale(X_scaled) : X_scaled;
  const ulong X_to_unscale_cols = X_to_unscale.Cols();
  const ulong X_to_unscale_rows = X_to_unscale.Rows();

//---
  if(X_to_unscale_cols != this.min_vals.Size())
   {
    LogError(StringFormat("Columnas escaladas %I64u != columnas entrenadas %u", X_to_unscale_cols, this.min_vals.Size()), FUNCION_ACTUAL);
    return X_scaled;
   }

//---
  for(ulong row = 0; row < X_to_unscale_rows; row++)
    for(ulong col = 0; col < X_to_unscale_cols; col++)
      X_to_unscale[row][col] = X_to_unscale[row][col] * (this.max_vals[col] - this.min_vals[col]) + this.min_vals[col];

//---
  return (reconstruir) ?  ReconstructMatrix(X_scaled, X_to_unscale) : X_to_unscale;
 }

//+------------------------------------------------------------------+
vector MaxMinScaler::inverse_transform(const vector &X_scaled, bool solo_escalar_lo_previsto = false, bool reconstruir = false)
 {
  if(!loaded_scaler)
   {
    LogError("Escalador no entrenado. Llame primero a fit_transform", FUNCION_ACTUAL);
    return X_scaled;
   }

//---
  vector X_to_unscale = (solo_escalar_lo_previsto) ?  ExtractVectorToScale(X_scaled) : X_scaled;
  const ulong X_to_unscale_size = X_to_unscale.Size();

//---
  if(X_to_unscale_size != this.min_vals.Size())
   {
    LogError(StringFormat("Elementos escalados %I64u != elementos entrenados %u", X_to_unscale_size, this.min_vals.Size()), FUNCION_ACTUAL);
    return X_scaled;
   }

//---
  for(ulong i = 0; i < X_to_unscale_size; i++)
    X_to_unscale[i] = X_to_unscale[i] * (this.max_vals[i] - this.min_vals[i]) + this.min_vals[i];

//---
  return (reconstruir) ?  ReconstructVector(X_scaled, X_to_unscale) : X_to_unscale;
 }

//+------------------------------------------------------------------+
//|                    Robust Scaler                                 |
//+------------------------------------------------------------------+
class RobustScaler : public ScalerBase
 {
protected:
  vector             medians, iqrs;

  bool               Save() override;
  bool               Load(string prefix_name) override;

public:
                     RobustScaler() : ScalerBase() { this.prefix_file = "_median_iqr.csv"; }

  vector             fit_transform(const vector &X) override;
  matrix             fit_transform(const matrix &X, bool save_data) override;
  matrix             transform(const matrix &X, bool solo_escalar_lo_previsto = false, bool reconstruir = false) override;
  vector             transform(const vector &X, bool solo_escalar_lo_previsto = false, bool reconstruir = false) override;
  matrix             inverse_transform(const matrix &X_scaled, bool solo_escalar_lo_previsto = false, bool reconstruir = false) override;
  vector             inverse_transform(const vector &X_scaled, bool solo_escalar_lo_previsto = false, bool reconstruir = false) override;
 };

//+------------------------------------------------------------------+
//|                                                                  |
//+------------------------------------------------------------------+
bool RobustScaler::Save()
 {
  FileDelete(this.file_name_out);
  ResetLastError();

//---
  int handle = FileOpen(this.file_name_out, FILE_WRITE | FILE_CSV | FILE_COMMON);
  if(handle == INVALID_HANDLE)
   {
    LogFatalError(StringFormat("Invalid handle Err= %d >> Filename= %s", GetLastError(), this.file_name_out), FUNCION_ACTUAL);
    return false;
   }

//---
  FileWrite(handle, vector_to_string(this.medians));
  FileWrite(handle, vector_to_string(this.iqrs));
  FileWrite(handle, count_cols);
  FileWrite(handle, start_col);
  FileWrite(handle, excluyed_cols);
  FileWrite(handle, (int)use_custom);

//---
  FileClose(handle);
  return true;
 }

//+------------------------------------------------------------------+
bool RobustScaler::Load(string prefix_name)
 {
  this.file_name_out = prefix_name + this.prefix_file;

//---
  ResetLastError();
  int handle = FileOpen(file_name_out, FILE_READ | FILE_CSV | FILE_COMMON, "\n");
  if(handle == INVALID_HANDLE)
   {
    LogFatalError(StringFormat("Invalid handle Err= %d >> Filename= %s", GetLastError(), this.file_name_out), FUNCION_ACTUAL);
    return false;
   }

//---
  this.medians = string_to_vector(FileReadString(handle));
  this.iqrs = string_to_vector(FileReadString(handle));
  this.count_cols = (ulong)StringToInteger(FileReadString(handle));
  this.start_col = (ulong)StringToInteger(FileReadString(handle));
  this.excluyed_cols = (ulong)StringToInteger(FileReadString(handle));
  this.use_custom = (bool)StringToInteger(FileReadString(handle));

//---
  FileClose(handle);
  return true;
 }

//+------------------------------------------------------------------+
//|                                                                  |
//+------------------------------------------------------------------+
vector RobustScaler::fit_transform(const vector &X)
 {
  if(loaded_scaler)
   {
    LogWarning("Este es un escalador cargado >> no es necesario ajustarlo a los nuevos datos, llame a otra instancia de una clase", FUNCION_ACTUAL);
    return transform(X);
   }

//---
  if(use_custom)
   {
    if(!CheckSizeCustom(X))
      return X;
   }
  else
   {
    if(!CheckSizeExcluded(X))
      return X;
   }

//---
  vector X_to_scale = ExtractVectorToScale(X);

//---
  double medians_cts = X_to_scale.Median();
  double q75 = X_to_scale.Percentile(75);
  double q25 = X_to_scale.Percentile(25);
  double iqrs_cts = q75 - q25;

  if(fabs(iqrs_cts) < SCALER_BASE_MIN_VALUE)
    iqrs_cts = 1.00;

//---
  for(ulong i = 0; i < X_to_scale.Size(); i++)
    X_to_scale[i] = (X_to_scale[i] - medians_cts) / iqrs_cts;

//---
  return ReconstructVector(X, X_to_scale);
 }

//+------------------------------------------------------------------+
matrix RobustScaler::fit_transform(const matrix &X, bool save_data)
 {
  if(loaded_scaler)
   {
    LogWarning("Este es un escalador cargado >> no es necesario ajustarlo a los nuevos datos, llame a otra instancia de una clase", FUNCION_ACTUAL);
    return transform(X);
   }

  LogInfo(StringFormat("Numero de columnas de entrada: %I64u", X.Cols()), FUNCION_ACTUAL);

//---
  if(use_custom)
   {
    if(!CheckSizeCustom(X))
      return X;
   }
  else
   {
    if(!CheckSizeExcluded(X))
      return X;
   }

//---
  matrix X_to_scale = ExtractMatrixToScale(X);
  const ulong X_to_scale_cols = X_to_scale.Cols();
  const ulong X_to_scale_rows = X_to_scale.Rows();

  LogInfo(StringFormat("Columnas a escalar: %I64u", X_to_scale_cols), FUNCION_ACTUAL);

//---
  vector medians_cts(X_to_scale_cols);
  vector iqrs_cts(X_to_scale_cols);

//---
  for(ulong i = 0; i < X_to_scale_cols; i++)
   {
    vector col = X_to_scale.Col(i);
    medians_cts[i] = col.Median();
    double q75 = col.Percentile(75);
    double q25 = col.Percentile(25);
    iqrs_cts[i] = q75 - q25;

    if(fabs(iqrs_cts[i]) < SCALER_BASE_MIN_VALUE)
     {
      LogWarning(StringFormat("Columna %I64u tiene IQR muy pequeño (%.2e), usando 1.0", i, iqrs_cts[i]), FUNCION_ACTUAL);
      iqrs_cts[i] = 1.0;
     }
   }

//---
  for(ulong row = 0; row < X_to_scale_rows; row++)
    for(ulong col = 0; col < X_to_scale_cols; col++)
      X_to_scale[row][col] = (X_to_scale[row][col] - medians_cts[col]) / iqrs_cts[col];

//---
  if(save_data)
   {
    this.medians = medians_cts;
    this.iqrs = iqrs_cts;
   }

//--- Aqui siempre se reconstruye
  return ReconstructMatrix(X, X_to_scale);
 }

//+------------------------------------------------------------------+
//|                                                                  |
//+------------------------------------------------------------------+
matrix RobustScaler::transform(const matrix &X, bool solo_escalar_lo_previsto = false, bool reconstruir = false)
 {
  if(!loaded_scaler)
   {
    LogError("Primero llame a fit_transform o load() antes de transform", FUNCION_ACTUAL);
    return X;
   }

//---
  if(use_custom)
   {
    if(!CheckSizeCustom(X))
      return X;
   }
  else
   {
    if(!CheckSizeExcluded(X))
      return X;
   }

//---
  matrix X_to_scale = solo_escalar_lo_previsto ? ExtractMatrixToScale(X) : X;
  const ulong X_to_scale_cols = X_to_scale.Cols();
  const ulong X_to_scale_rows = X_to_scale.Rows();

//---
  if(X_to_scale_cols != this.medians.Size())
   {
    LogError(StringFormat("Columnas a escalar %I64u != columnas entrenadas %u", X_to_scale_cols, this.medians.Size()), FUNCION_ACTUAL);
    return X;
   }

//---
  for(ulong row = 0; row < X_to_scale_rows; row++)
    for(ulong col = 0; col < X_to_scale_cols; col++)
      X_to_scale[row][col] = (X_to_scale[row][col] - this.medians[col]) / this.iqrs[col];

//---
  return (reconstruir) ?  ReconstructMatrix(X, X_to_scale) : X_to_scale;
 }

//+------------------------------------------------------------------+
vector RobustScaler::transform(const vector &X, bool solo_escalar_lo_previsto = false, bool reconstruir = false)
 {
  if(!loaded_scaler)
   {
    LogError("Primero llame a fit_transform o load() antes de transform", FUNCION_ACTUAL);
    return X;
   }

//---
  vector X_to_scale = solo_escalar_lo_previsto ? ExtractVectorToScale(X) : X;
  const ulong X_to_scale_size = X_to_scale.Size();

  if(X_to_scale_size != this.medians.Size())
   {
    LogError(StringFormat("Elementos a escalar %I64u != elementos entrenados %u", X_to_scale_size, this.medians.Size()), FUNCION_ACTUAL);
    return X;
   }

//---
  for(ulong i = 0; i < X_to_scale_size; i++)
    X_to_scale[i] = (X_to_scale[i] - this.medians[i]) / this.iqrs[i];

//---
  return (reconstruir) ? ReconstructVector(X, X_to_scale) : X_to_scale;
 }

//+------------------------------------------------------------------+
//|                                                                  |
//+------------------------------------------------------------------+
matrix RobustScaler::inverse_transform(const matrix &X_scaled, bool solo_escalar_lo_previsto = false, bool reconstruir = false)
 {
  if(!loaded_scaler)
   {
    LogError("Escalador no entrenado. Llame primero a fit_transform", FUNCION_ACTUAL);
    return X_scaled;
   }

//---
  matrix X_to_unscale = (solo_escalar_lo_previsto) ? ExtractMatrixToScale(X_scaled) : X_scaled;
  const ulong X_to_unscale_cols = X_to_unscale.Cols();
  const ulong X_to_unscale_rows = X_to_unscale.Rows();

//---
  if(X_to_unscale_cols != this.medians.Size())
   {
    LogError(StringFormat("Columnas escaladas %I64u != columnas entrenadas %u", X_to_unscale_cols, this.medians.Size()), FUNCION_ACTUAL);
    return X_scaled;
   }

//---
  for(ulong row = 0; row < X_to_unscale_rows; row++)
    for(ulong col = 0; col < X_to_unscale_cols; col++)
      X_to_unscale[row][col] = X_to_unscale[row][col] * this.iqrs[col] + this.medians[col];

//---
  return (reconstruir) ?  ReconstructMatrix(X_scaled, X_to_unscale) : X_to_unscale;
 }

//+------------------------------------------------------------------+
vector RobustScaler::inverse_transform(const vector &X_scaled, bool solo_escalar_lo_previsto = false, bool reconstruir = false)
 {
  if(!loaded_scaler)
   {
    LogError("Escalador no entrenado. Llame primero a fit_transform", FUNCION_ACTUAL);
    return X_scaled;
   }

//---
  vector X_to_unscale = (solo_escalar_lo_previsto) ?  ExtractVectorToScale(X_scaled) : X_scaled;
  const ulong X_to_unscale_size = X_to_unscale.Size();

//---
  if(X_to_unscale_size != this.medians.Size())
   {
    LogError(StringFormat("Elementos escalados %I64u != elementos entrenados %u", X_to_unscale_size, this.medians.Size()), FUNCION_ACTUAL);
    return X_scaled;
   }

//---
  for(ulong i = 0; i < X_to_unscale_size; i++)
    X_to_unscale[i] = X_to_unscale[i] * this.iqrs[i] + this.medians[i];

//---
  return (reconstruir) ?  ReconstructVector(X_scaled, X_to_unscale) : X_to_unscale;
 }

//+------------------------------------------------------------------+
#endif // SCALERBYLEO_SCALER_BASE_MQH
//+------------------------------------------------------------------+