1246 lines
No EOL
77 KiB
MQL5
1246 lines
No EOL
77 KiB
MQL5
//+------------------------------------------------------------------+
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//| ScalerBase.mqh |
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//| Copyright 2025, Leo. |
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//| https://www.mql5.com |
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//+------------------------------------------------------------------+
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#property copyright "Copyright 2025, Leo."
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#property link "https://www.mql5.com/en/users/nique_372"
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#property strict
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#ifndef SCALER_BY_LEO_GEN_MQH
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#define SCALER_BY_LEO_GEN_MQH
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#include "..\\MQLArticles\\Utils\\Funciones Array.mqh"
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#include "..\\MQLArticles\\Utils\\File.mqh"
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//+------------------------------------------------------------------+
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//| Scaler Base |
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//+------------------------------------------------------------------+
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class ScalerBase : public CLoggerBase
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{
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protected:
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string file_name_out;
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string prefix_file;
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bool loaded_scaler;
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bool use_custom; //Bandera para saber si se usa custom (true) o excluyed (false)
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ulong start_col;
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ulong count_cols;
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ulong excluyed_cols;
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virtual bool Save() = 0;
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virtual bool Load(string prefix_name) = 0;
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//--- Métodos auxiliares
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bool CheckSizeCustom(const matrix &mtx) const;
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bool CheckSizeExcluded(const matrix &mtx) const;
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bool CheckSizeCustom(const vector &v) const;
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bool CheckSizeExcluded(const vector &v) const;
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//---
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matrix ExtractMatrixToScale(const matrix &X) const;
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matrix ReconstructMatrix(const matrix &X_original, const matrix &X_scaled) const;
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vector ExtractVectorToScale(const vector &X) const;
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vector ReconstructVector(const vector &X_original, const vector &X_scaled) const;
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public:
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ScalerBase(void);
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~ScalerBase(void) {}
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//---
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inline void SetRangeEscaler(ulong start_col_, ulong count_col_); //Custom
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inline void SetRangeEscaler(ulong excluyed_cols_ = 1); //Mas simple, el usuario decide cuantas columnas empezando por atras se excluyen
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//---
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inline bool save(string prefix_name);
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inline bool load(string prefix_name);
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//---
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virtual matrix fit_transform(const matrix &X, bool save_data) = 0;
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virtual vector fit_transform(const vector &X) = 0; //Para vectores no se guarda data
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virtual matrix transform(const matrix &X, bool solo_escalar_lo_previsto = false, bool reconstruir = false) = 0;
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virtual vector transform(const vector &X, bool solo_escalar_lo_previsto = false, bool reconstruir = false) = 0;
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virtual matrix inverse_transform(const matrix &X_scaled, bool solo_escalar_lo_previsto = false, bool reconstruir = false) = 0;
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virtual vector inverse_transform(const vector &X_scaled, bool solo_escalar_lo_previsto = false, bool reconstruir = false) = 0;
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// --- Métodos comunes ---
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virtual inline string GetOutputFile() const final { return this.file_name_out; }
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};
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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ScalerBase::ScalerBase(void)
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: start_col(0), count_cols(0), use_custom(false), excluyed_cols(1), file_name_out(NULL), loaded_scaler(false)
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{
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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inline void ScalerBase::SetRangeEscaler(ulong start_col_, ulong count_col_)
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{
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this.use_custom = true;
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this.count_cols = count_col_;
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this.start_col = start_col_;
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LogInfo(StringFormat("Configurado escalado CUSTOM: columnas %I64u a %I64u (%I64u columnas)", start_col_, start_col_ + count_col_ - 1, count_col_), FUNCION_ACTUAL);
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}
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//+------------------------------------------------------------------+
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inline void ScalerBase::SetRangeEscaler(ulong excluyed_cols_ = 1)
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{
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this.excluyed_cols = excluyed_cols_;
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this.use_custom = false;
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LogInfo(StringFormat("Configurado escalado EXCLUDED: excluir últimas %I64u columnas", excluyed_cols_), FUNCION_ACTUAL);
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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bool ScalerBase::CheckSizeCustom(const matrix &mtx) const
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{
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if(start_col >= mtx.Cols())
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{
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LogError(StringFormat("Columna de inicio %I64u >= total columnas %I64u", start_col, mtx.Cols()), FUNCION_ACTUAL);
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return false;
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}
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if(start_col + count_cols > mtx.Cols())
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{
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LogError(StringFormat("Rango [%I64u:%I64u] excede columnas disponibles %I64u", start_col, start_col + count_cols - 1, mtx.Cols()), FUNCION_ACTUAL);
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return false;
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}
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return true;
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}
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//+------------------------------------------------------------------+
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bool ScalerBase::CheckSizeExcluded(const matrix &mtx) const
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{
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if(mtx.Cols() < excluyed_cols)
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{
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LogError(StringFormat("Columnas a excluir %I64u >= total columnas %I64u", excluyed_cols, mtx.Cols()), FUNCION_ACTUAL);
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return false;
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}
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return true;
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}
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//+------------------------------------------------------------------+
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bool ScalerBase::CheckSizeCustom(const vector &v) const
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{
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if(start_col >= v.Size())
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{
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LogError(StringFormat("Columna de inicio %I64u >= tamaño total del vector%I64u", start_col, v.Size()), FUNCION_ACTUAL);
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return false;
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}
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if(start_col + count_cols > v.Size())
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{
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LogError(StringFormat("Rango [%I64u:%I64u] excede el tamaño del vector %I64u", start_col, start_col + count_cols - 1, v.Size()), FUNCION_ACTUAL);
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return false;
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}
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return true;
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}
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//+------------------------------------------------------------------+
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bool ScalerBase::CheckSizeExcluded(const vector &v) const
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{
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if(v.Size() < excluyed_cols)
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{
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LogError(StringFormat("Columnas a excluir %I64u >= tamaño del vector: %I64u", excluyed_cols, v.Size()), FUNCION_ACTUAL);
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return false;
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}
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return true;
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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matrix ScalerBase::ExtractMatrixToScale(const matrix &X) const
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{
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matrix result;
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if(use_custom)
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{
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if(X.Cols() == count_cols)
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return X;
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result.Init(X.Rows(), count_cols);
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for(ulong row = 0; row < X.Rows(); row++)
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for(ulong col = 0; col < count_cols; col++)
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result[row][col] = X[row][start_col + col];
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}
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else
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{
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if(excluyed_cols == 0)
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return X;
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ulong cols_to_scale = X.Cols() - excluyed_cols;
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result.Init(X.Rows(), cols_to_scale);
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for(ulong row = 0; row < X.Rows(); row++)
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for(ulong col = 0; col < cols_to_scale; col++)
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result[row][col] = X[row][col];
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}
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return result;
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}
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//+------------------------------------------------------------------+
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matrix ScalerBase::ReconstructMatrix(const matrix &X_original, const matrix &X_scaled) const
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{
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if(X_original.Rows() == X_scaled.Rows() && X_original.Cols() == X_scaled.Cols())
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return X_scaled;
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matrix result = X_original; // Copia completa
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if(use_custom)
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{
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for(ulong row = 0; row < X_original.Rows(); row++)
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for(ulong col = 0; col < count_cols; col++)
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result[row][start_col + col] = X_scaled[row][col];
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}
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else
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{
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for(ulong row = 0; row < X_original.Rows(); row++)
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for(ulong col = 0; col < X_scaled.Cols(); col++)
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result[row][col] = X_scaled[row][col];
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}
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return result;
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}
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//+------------------------------------------------------------------+
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vector ScalerBase::ExtractVectorToScale(const vector &X) const
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{
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vector result;
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if(use_custom)
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{
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if(X.Size() == count_cols)
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return X;
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// Extraer rango específico
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result.Resize(count_cols);
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for(ulong i = 0; i < count_cols; i++)
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result[i] = X[start_col + i];
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}
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else
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{
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if(excluyed_cols == 0)
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return X;
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// Extraer todas excepto las últimas N
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ulong size_to_scale = X.Size() - excluyed_cols;
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result.Resize(size_to_scale);
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for(ulong i = 0; i < size_to_scale; i++)
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result[i] = X[i];
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}
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return result;
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}
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//+------------------------------------------------------------------+
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vector ScalerBase::ReconstructVector(const vector &X_original, const vector &X_scaled) const
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{
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if(X_original.Size() == X_scaled.Size())
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return X_scaled;
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vector result = X_original;
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if(use_custom)
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{
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// Reemplazar rango específico
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for(ulong i = 0; i < count_cols; i++)
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result[start_col + i] = X_scaled[i];
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}
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else
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{
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// Reemplazar todas excepto las últimas N
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for(ulong i = 0; i < X_scaled.Size(); i++)
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result[i] = X_scaled[i];
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}
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return result;
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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inline bool ScalerBase::load(string prefix_name)
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{
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loaded_scaler = true;
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return this.Load(prefix_name);
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}
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//+------------------------------------------------------------------+
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inline bool ScalerBase::save(string prefix_name)
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{
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this.file_name_out = prefix_name + this.prefix_file;
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return this.Save();
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}
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//+------------------------------------------------------------------+
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//| Standardization Scaler |
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//+------------------------------------------------------------------+
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class StandardizationScaler : public ScalerBase
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{
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protected:
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vector mean, std;
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bool Save() override;
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bool Load(string prefix_name) override;
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public:
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StandardizationScaler() : ScalerBase() { this.prefix_file = "_mean_std.csv"; }
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matrix fit_transform(const matrix &X, bool save_data) override;
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vector fit_transform(const vector &X) override; //Para vectores no se guarda data
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matrix transform(const matrix &X, bool solo_escalar_lo_previsto = false, bool reconstruir = false) override;
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vector transform(const vector &X, bool solo_escalar_lo_previsto = false, bool reconstruir = false) override;
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matrix inverse_transform(const matrix &X_scaled, bool solo_escalar_lo_previsto = false, bool reconstruir = false) override;
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vector inverse_transform(const vector &X_scaled, bool solo_escalar_lo_previsto = false, bool reconstruir = false) override;
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};
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//+------------------------------------------------------------------+
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bool StandardizationScaler::Save()
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{
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FileDelete(this.file_name_out);
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ResetLastError();
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int handle = FileOpen(this.file_name_out, FILE_WRITE | FILE_CSV | FILE_COMMON);
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if(handle == INVALID_HANDLE)
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{
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LogFatalError(StringFormat("Invalid handle Err= %d >> Filename= %s", GetLastError(), this.file_name_out), FUNCION_ACTUAL);
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return false;
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}
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FileWrite(handle, vector_to_string(this.mean));
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FileWrite(handle, vector_to_string(this.std));
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FileWrite(handle, count_cols);
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FileWrite(handle, start_col);
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FileWrite(handle, excluyed_cols);
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FileWrite(handle, (int)use_custom);
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FileClose(handle);
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return true;
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}
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//+------------------------------------------------------------------+
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bool StandardizationScaler::Load(string prefix_name)
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{
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this.file_name_out = prefix_name + this.prefix_file;
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ResetLastError();
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int handle = FileOpen(file_name_out, FILE_READ | FILE_CSV | FILE_COMMON, "\n");
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if(handle == INVALID_HANDLE)
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{
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LogFatalError(StringFormat("Invalid handle Err= %d >> Filename= %s", GetLastError(), this.file_name_out), FUNCION_ACTUAL);
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return false;
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}
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this.mean = string_to_vector(FileReadString(handle));
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this.std = string_to_vector(FileReadString(handle));
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this.count_cols = (ulong)StringToInteger(FileReadString(handle));
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this.start_col = (ulong)StringToInteger(FileReadString(handle));
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this.excluyed_cols = (ulong)StringToInteger(FileReadString(handle));
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this.use_custom = (bool)StringToInteger(FileReadString(handle));
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FileClose(handle);
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return true;
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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vector StandardizationScaler::fit_transform(const vector &X)
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{
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if(loaded_scaler)
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{
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LogWarning("Este es un escalador cargado >> no es necesario ajustarlo a los nuevos datos, llame a otra instancia de una clase", FUNCION_ACTUAL);
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return transform(X);
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}
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//---
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if(use_custom)
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{
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if(!CheckSizeCustom(X))
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return X;
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}
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else
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{
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if(!CheckSizeExcluded(X))
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return X;
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}
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//---
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vector X_to_scaled = ExtractVectorToScale(X);
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//---
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double mean_val = X_to_scaled.Mean();
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double std_val = X_to_scaled.Std();
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if(std_val < 1e-9)
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std_val = 1.0;
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//---
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for(ulong i = 0; i < X_to_scaled.Size(); i++)
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X_to_scaled[i] = (X_to_scaled[i] - mean_val) / std_val;
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return ReconstructVector(X, X_to_scaled);
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}
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//+------------------------------------------------------------------+
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matrix StandardizationScaler::fit_transform(const matrix &X, bool save_data)
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{
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if(loaded_scaler)
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{
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LogWarning("Este es un escalador cargado >> no es necesario ajustarlo a los nuevos datos, llame a otra instancia de una clase", FUNCION_ACTUAL);
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return transform(X);
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}
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LogInfo(StringFormat("Numero de columnas de entrada: %I64u", X.Cols()), FUNCION_ACTUAL);
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//---
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if(use_custom)
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{
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if(!CheckSizeCustom(X))
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return X;
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}
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else
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{
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if(!CheckSizeExcluded(X))
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return X;
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}
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//---
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matrix X_to_scale = ExtractMatrixToScale(X);
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LogInfo(StringFormat("Columnas a escalar: %I64u", X_to_scale.Cols()), FUNCION_ACTUAL);
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vector mean_cts(X_to_scale.Cols());
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vector std_cts(X_to_scale.Cols());
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for(ulong i = 0; i < X_to_scale.Cols(); i++)
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{
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mean_cts[i] = X_to_scale.Col(i).Mean();
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std_cts[i] = X_to_scale.Col(i).Std();
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// Evitar división por cero
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if(std_cts[i] < 1e-9)
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{
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LogWarning(StringFormat("Columna %I64u tiene std muy pequeño (%.2e), usando 1.0", i, std_cts[i]), FUNCION_ACTUAL);
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std_cts[i] = 1.0;
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}
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}
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//---
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matrix X_scaled(X_to_scale.Rows(), X_to_scale.Cols());
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for(ulong row = 0; row < X_to_scale.Rows(); row++)
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for(ulong col = 0; col < X_to_scale.Cols(); col++)
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X_scaled[row][col] = (X_to_scale[row][col] - mean_cts[col]) / std_cts[col];
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if(save_data)
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{
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this.mean = mean_cts;
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this.std = std_cts;
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}
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//--- Aqui siempre se reconstruye
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return ReconstructMatrix(X, X_scaled);
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}
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//+------------------------------------------------------------------+
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matrix StandardizationScaler::transform(const matrix &X, bool solo_escalar_lo_previsto = false, bool reconstruir = false)
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{
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if(!loaded_scaler)
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{
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LogError("Primero llame a fit_transform o load() antes de transform", FUNCION_ACTUAL);
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return X;
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}
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//---
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if(use_custom)
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{
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if(!CheckSizeCustom(X))
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return X;
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}
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else
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{
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if(!CheckSizeExcluded(X))
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return X;
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}
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//---
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matrix X_to_scale = solo_escalar_lo_previsto ? ExtractMatrixToScale(X) : X;
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if(X_to_scale.Cols() != this.mean.Size())
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{
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LogError(StringFormat("Columnas a escalar %I64u != columnas entrenadas %u", X_to_scale.Cols(), this.mean.Size()), FUNCION_ACTUAL);
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return X;
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}
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//---
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matrix X_scaled(X_to_scale.Rows(), X_to_scale.Cols());
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for(ulong row = 0; row < X_to_scale.Rows(); row++)
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for(ulong col = 0; col < X_to_scale.Cols(); col++)
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X_scaled[row][col] = (X_to_scale[row][col] - this.mean[col]) / this.std[col];
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//---
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if(reconstruir)
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return ReconstructMatrix(X, X_scaled);
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else
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return X_scaled;
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}
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//+------------------------------------------------------------------+
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vector StandardizationScaler::transform(const vector &X, bool solo_escalar_lo_previsto = false, bool reconstruir = false)
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{
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if(!loaded_scaler)
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{
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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;
|
|
|
|
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;
|
|
}
|
|
|
|
//---
|
|
vector X_scaled(X_to_scale.Size());
|
|
for(ulong i = 0; i < X_to_scale.Size(); i++)
|
|
X_scaled[i] = (X_to_scale[i] - this.mean[i]) / this.std[i];
|
|
|
|
//---
|
|
if(reconstruir)
|
|
return ReconstructVector(X, X_scaled);
|
|
else
|
|
return X_scaled;
|
|
}
|
|
|
|
//+------------------------------------------------------------------+
|
|
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;
|
|
|
|
//---
|
|
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;
|
|
}
|
|
|
|
//---
|
|
matrix X_unscaled(X_to_unscale.Rows(), X_to_unscale.Cols());
|
|
for(ulong row = 0; row < X_to_unscale.Rows(); row++)
|
|
for(ulong col = 0; col < X_to_unscale.Cols(); col++)
|
|
X_unscaled[row][col] = X_to_unscale[row][col] * this.std[col] + this.mean[col];
|
|
|
|
|
|
//---
|
|
if(reconstruir)
|
|
return ReconstructMatrix(X_scaled, X_unscaled);
|
|
else
|
|
return X_unscaled;
|
|
}
|
|
|
|
//+------------------------------------------------------------------+
|
|
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;
|
|
|
|
if(solo_escalar_lo_previsto)
|
|
X_to_unscale = ExtractVectorToScale(X_scaled);
|
|
else
|
|
X_to_unscale = X_scaled;
|
|
|
|
//---
|
|
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;
|
|
}
|
|
|
|
//---
|
|
vector X_unscaled(X_to_unscale.Size());
|
|
for(ulong i = 0; i < X_to_unscale.Size(); i++)
|
|
{
|
|
X_unscaled[i] = X_to_unscale[i] * this.std[i] + this.mean[i];
|
|
}
|
|
|
|
//---
|
|
if(reconstruir)
|
|
return ReconstructVector(X_scaled, X_unscaled);
|
|
else
|
|
return X_unscaled;
|
|
}
|
|
//+------------------------------------------------------------------+
|
|
//| 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; //Para vectores no se guarda data
|
|
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 < 1e-10)
|
|
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);
|
|
|
|
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]) < 1e-10)
|
|
{
|
|
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;
|
|
}
|
|
}
|
|
|
|
//---
|
|
matrix X_scaled(X_to_scale.Rows(), X_to_scale.Cols());
|
|
for(ulong row = 0; row < X_to_scale.Rows(); row++)
|
|
for(ulong col = 0; col < X_to_scale.Cols(); col++)
|
|
X_scaled[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;
|
|
}
|
|
|
|
//---
|
|
return ReconstructMatrix(X, X_scaled);
|
|
}
|
|
|
|
//+------------------------------------------------------------------+
|
|
matrix MaxMinScaler::transform(const matrix &X, bool solo_escalar_lo_previsto = false, bool reconstruir = false)
|
|
{
|
|
if(!loaded_scaler)
|
|
{
|
|
LogError("Escalador no entrenado. Llame primero a fit_transform o load()", 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;
|
|
|
|
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;
|
|
}
|
|
|
|
//---
|
|
matrix X_scaled(X_to_scale.Rows(), X_to_scale.Cols());
|
|
for(ulong row = 0; row < X_to_scale.Rows(); row++)
|
|
for(ulong col = 0; col < X_to_scale.Cols(); col++)
|
|
X_scaled[row][col] = (X_to_scale[row][col] - this.min_vals[col]) / (this.max_vals[col] - this.min_vals[col]);
|
|
|
|
//---
|
|
if(reconstruir)
|
|
return ReconstructMatrix(X, X_scaled);
|
|
else
|
|
return X_scaled;
|
|
}
|
|
|
|
//+------------------------------------------------------------------+
|
|
vector MaxMinScaler::transform(const vector &X, bool solo_escalar_lo_previsto = false, bool reconstruir = false)
|
|
{
|
|
if(!loaded_scaler)
|
|
{
|
|
LogError("Escalador no entrenado. Llame primero a fit_transform o load()", FUNCION_ACTUAL);
|
|
return X;
|
|
}
|
|
|
|
//---
|
|
vector X_to_scale = solo_escalar_lo_previsto ? ExtractVectorToScale(X) : X;
|
|
|
|
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;
|
|
}
|
|
|
|
//---
|
|
vector X_scaled(X_to_scale.Size());
|
|
for(ulong i = 0; i < X_to_scale.Size(); i++)
|
|
X_scaled[i] = (X_to_scale[i] - this.min_vals[i]) / (this.max_vals[i] - this.min_vals[i]);
|
|
|
|
|
|
//---
|
|
if(reconstruir)
|
|
return ReconstructVector(X, X_scaled);
|
|
else
|
|
return X_scaled;
|
|
|
|
}
|
|
|
|
//+------------------------------------------------------------------+
|
|
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;
|
|
if(solo_escalar_lo_previsto)
|
|
X_to_unscale = ExtractMatrixToScale(X_scaled);
|
|
else
|
|
X_to_unscale = X_scaled;
|
|
|
|
//---
|
|
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;
|
|
}
|
|
|
|
//---
|
|
matrix X_unscaled(X_to_unscale.Rows(), X_to_unscale.Cols());
|
|
for(ulong row = 0; row < X_to_unscale.Rows(); row++)
|
|
{
|
|
for(ulong col = 0; col < X_to_unscale.Cols(); col++)
|
|
{
|
|
X_unscaled[row][col] = X_to_unscale[row][col] * (this.max_vals[col] - this.min_vals[col]) + this.min_vals[col];
|
|
|
|
}
|
|
}
|
|
|
|
//---
|
|
if(reconstruir)
|
|
return ReconstructMatrix(X_scaled, X_unscaled);
|
|
else
|
|
return X_unscaled;
|
|
}
|
|
|
|
//+------------------------------------------------------------------+
|
|
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;
|
|
|
|
if(solo_escalar_lo_previsto)
|
|
X_to_unscale = ExtractVectorToScale(X_scaled);
|
|
else
|
|
X_to_unscale = X_scaled;
|
|
|
|
|
|
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;
|
|
}
|
|
|
|
//---
|
|
vector X_unscaled(X_to_unscale.Size());
|
|
for(ulong i = 0; i < X_to_unscale.Size(); i++)
|
|
X_unscaled[i] = X_to_unscale[i] * (this.max_vals[i] - this.min_vals[i]) + this.min_vals[i];
|
|
|
|
//---
|
|
if(reconstruir)
|
|
return ReconstructVector(X_scaled, X_unscaled);
|
|
else
|
|
return X_unscaled;
|
|
}
|
|
|
|
//+------------------------------------------------------------------+
|
|
//| 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; //Para vectores no se guarda data
|
|
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;
|
|
}
|
|
|
|
//+------------------------------------------------------------------+
|
|
//| |
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//+------------------------------------------------------------------+
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vector RobustScaler::fit_transform(const vector &X)
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{
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if(loaded_scaler)
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{
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LogWarning("Este es un escalador cargado | no es necesario ajustarlo a los nuevos datos, llame a otra instancia de una clase", FUNCION_ACTUAL);
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return transform(X);
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}
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//---
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if(use_custom)
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{
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if(!CheckSizeCustom(X))
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return X;
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}
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else
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{
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if(!CheckSizeExcluded(X))
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return X;
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}
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//---
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vector X_to_scale = ExtractVectorToScale(X);
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//---
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double medians_cts = X_to_scale.Median();
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double q75 = X_to_scale.Percentile(75);
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double q25 = X_to_scale.Percentile(25);
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double iqrs_cts = q75 - q25;
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if(fabs(iqrs_cts) < 1e-10)
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iqrs_cts = 1.00;
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|
|
|
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//---
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for(ulong i = 0; i < X_to_scale.Size(); i++)
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X_to_scale[i] = (X_to_scale[i] - medians_cts) / iqrs_cts;
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//---
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return ReconstructVector(X, X_to_scale);
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}
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//+------------------------------------------------------------------+
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matrix RobustScaler::fit_transform(const matrix &X, bool save_data)
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{
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|
if(loaded_scaler)
|
|
{
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LogWarning("Este es un escalador cargado | no es necesario ajustarlo a los nuevos datos, llame a otra instancia de una clase", FUNCION_ACTUAL);
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return transform(X);
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}
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|
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LogInfo(StringFormat("Numero de columnas de entrada: %I64u", X.Cols()), FUNCION_ACTUAL);
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|
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//---
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|
if(use_custom)
|
|
{
|
|
if(!CheckSizeCustom(X))
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return X;
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}
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else
|
|
{
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|
if(!CheckSizeExcluded(X))
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|
return X;
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}
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|
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|
//---
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matrix X_to_scale = ExtractMatrixToScale(X);
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LogInfo(StringFormat("Columnas a escalar: %I64u", X_to_scale.Cols()), FUNCION_ACTUAL);
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|
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|
//---
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|
vector medians_cts(X_to_scale.Cols());
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vector iqrs_cts(X_to_scale.Cols());
|
|
|
|
//---
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|
for(ulong i = 0; i < X_to_scale.Cols(); i++)
|
|
{
|
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vector col = X_to_scale.Col(i);
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|
medians_cts[i] = col.Median();
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|
double q75 = col.Percentile(75);
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|
double q25 = col.Percentile(25);
|
|
iqrs_cts[i] = q75 - q25;
|
|
|
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if(fabs(iqrs_cts[i]) < 1e-10)
|
|
{
|
|
LogWarning(StringFormat("Columna %I64u tiene IQR muy pequeño (%.2e), usando 1.0", i, iqrs_cts[i]), FUNCION_ACTUAL);
|
|
iqrs_cts[i] = 1.0;
|
|
}
|
|
}
|
|
|
|
//---
|
|
matrix X_scaled(X_to_scale.Rows(), X_to_scale.Cols());
|
|
for(ulong row = 0; row < X_to_scale.Rows(); row++)
|
|
for(ulong col = 0; col < X_to_scale.Cols(); col++)
|
|
X_scaled[row][col] = (X_to_scale[row][col] - medians_cts[col]) / iqrs_cts[col];
|
|
|
|
if(save_data)
|
|
{
|
|
this.medians = medians_cts;
|
|
this.iqrs = iqrs_cts;
|
|
}
|
|
|
|
//---
|
|
return ReconstructMatrix(X, X_scaled);
|
|
}
|
|
|
|
//+------------------------------------------------------------------+
|
|
matrix RobustScaler::transform(const matrix &X, bool solo_escalar_lo_previsto = false, bool reconstruir = false)
|
|
{
|
|
if(!loaded_scaler)
|
|
{
|
|
LogError("Escalador no entrenado. Llame primero a fit_transform o load()", 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;
|
|
|
|
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;
|
|
}
|
|
|
|
//---
|
|
matrix X_scaled(X_to_scale.Rows(), X_to_scale.Cols());
|
|
for(ulong row = 0; row < X_to_scale.Rows(); row++)
|
|
for(ulong col = 0; col < X_to_scale.Cols(); col++)
|
|
X_scaled[row][col] = (X_to_scale[row][col] - this.medians[col]) / this.iqrs[col];
|
|
|
|
//---
|
|
if(reconstruir)
|
|
return ReconstructMatrix(X, X_scaled);
|
|
else
|
|
return X_scaled;
|
|
}
|
|
|
|
//+------------------------------------------------------------------+
|
|
vector RobustScaler::transform(const vector &X, bool solo_escalar_lo_previsto = false, bool reconstruir = false)
|
|
{
|
|
if(!loaded_scaler)
|
|
{
|
|
LogError("Escalador no entrenado. Llame primero a fit_transform o load()", FUNCION_ACTUAL);
|
|
return X;
|
|
}
|
|
|
|
//---
|
|
vector X_to_scale = solo_escalar_lo_previsto ? ExtractVectorToScale(X) : X;
|
|
|
|
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;
|
|
}
|
|
|
|
//---
|
|
vector X_scaled(X_to_scale.Size());
|
|
for(ulong i = 0; i < X_to_scale.Size(); i++)
|
|
X_scaled[i] = (X_to_scale[i] - this.medians[i]) / this.iqrs[i];
|
|
|
|
|
|
//---
|
|
if(reconstruir)
|
|
return ReconstructVector(X, X_scaled);
|
|
else
|
|
return X_scaled;
|
|
}
|
|
|
|
//+------------------------------------------------------------------+
|
|
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;
|
|
|
|
if(solo_escalar_lo_previsto)
|
|
X_to_unscale = ExtractMatrixToScale(X_scaled);
|
|
else
|
|
X_to_unscale = X_scaled;
|
|
|
|
|
|
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;
|
|
}
|
|
|
|
//---
|
|
matrix X_unscaled(X_to_unscale.Rows(), X_to_unscale.Cols());
|
|
for(ulong row = 0; row < X_to_unscale.Rows(); row++)
|
|
for(ulong col = 0; col < X_to_unscale.Cols(); col++)
|
|
X_unscaled[row][col] = X_to_unscale[row][col] * this.iqrs[col] + this.medians[col];
|
|
|
|
//---
|
|
if(reconstruir)
|
|
return ReconstructMatrix(X_scaled, X_unscaled);
|
|
else
|
|
return X_unscaled;
|
|
|
|
}
|
|
|
|
//+------------------------------------------------------------------+
|
|
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;
|
|
|
|
if(solo_escalar_lo_previsto)
|
|
X_to_unscale = ExtractVectorToScale(X_scaled);
|
|
else
|
|
X_to_unscale = X_scaled;
|
|
|
|
|
|
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;
|
|
}
|
|
|
|
//---
|
|
vector X_unscaled(X_to_unscale.Size());
|
|
for(ulong i = 0; i < X_to_unscale.Size(); i++)
|
|
X_unscaled[i] = X_to_unscale[i] * this.iqrs[i] + this.medians[i];
|
|
|
|
|
|
//---
|
|
if(reconstruir)
|
|
return ReconstructVector(X_scaled, X_unscaled);
|
|
else
|
|
return X_unscaled;
|
|
}
|
|
//+------------------------------------------------------------------+
|
|
#endif |