Scaler.h

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#pragma once
#include "../../Matrix.h"
#include "../../statistics/Probability.h"
 
 
#include "../../Matrix.h"
#include "../../numerics/utils.h"
#include "../Predictor.h"
 
class StandardScaler : public Transformer
{
private:
  bool with_std   = true;
  bool with_means = true;
 
public:
  Matrix<double> means;
  Matrix<double> std_deviations;
 
public:
  StandardScaler(bool withMeans = true, bool withStd = true)
    : Transformer()
    , with_std(withStd)
    , with_means(withMeans){};
 
  void fit(const Matrix<double>& X, [[maybe_unused]] const Matrix<double>& y) override {
    means          = with_means ? mean(X, 0) : zeros(1, X.columns());
    std_deviations = with_std ? sd(X, 0) : ones(1, X.columns());
  }
 
  Matrix<double> transform(const Matrix<double>& in) override {
    auto diff = in - means;
    return diff / std_deviations;
  }
};
 
class MinMaxScaler : public Transformer
{
private:
  double min_val;
  double max_val;
 
public:
public:
  MinMaxScaler(double range_min_val = 0., double range_max_val = 1.0)
    : Transformer()
    , min_val(range_min_val)
    , max_val(range_max_val) { }
 
  void fit(const Matrix<double>& X, [[maybe_unused]] const Matrix<double>& y) override { }
 
  Matrix<double> transform(const Matrix<double>& in) override {
    auto localDiff  = max(in, 0) - min(in, 0);
    auto shiftedIn  = in - min(in, 0);
    auto scaledDiff = shiftedIn / localDiff;
    scaledDiff      = scaledDiff * (max_val - min_val);
    scaledDiff      = scaledDiff + Matrix<double>(min_val, scaledDiff.rows(), scaledDiff.columns());
    return scaledDiff;
  }
};