ZenithClown · April 12, 2023 07:27
diff --git a/Scaler Functions.md b/Scaler Functions.md
diff --git a/scaler.py b/scaler.py
 # -*- encoding: utf-8 -*-

 """
 Custom Built Scaler Functioonality

 Data scaling is important for faster model convergence and model
 training. While `sklearn.preprocessing` provides adequate scaling
 functionalities, however some are limited like giving a custom
 feature range which is fulfilled by `RangedScaler` implementation.
 """

 import numpy as np

 class UnivariateRangedScaler(object):
    """
    A Specialized `MinMaxScaler` that Considers a Feature Range

    The function is developed such that the scaling operation can be
    acheived by passing a feature range `(X_min, X_max)` for a given
    univariate series. The model considers the range and scales the
    value even if `X_min` and/or `X_max` is not present in the actual
    series. Mathemaically, the scalling formula is given as:

    ```math
        x' = t0 + ((x - x_min) / (x_max - x_min))
    ```

    In case `feature_range` parameter is not required, please use the
    `sklearn.preprocessing.MinMaxScaler` which is efficient and
    robust to handle univariate and multivariate data series.

    Performance Warning: Currently, the function can only work for a
    feature range of `(t0, t1), where t1 = t0 + 1` because `t1` is
    not yet considered in calculation.
    """

    def __init__(
        self,
        x_min : float,
        x_max : float,
        feature_range : tuple = (0, 1)
    ) -> None:
        self.x_min = x_min
        self.x_max = x_max

        # scale the data into a desired range
        self.t0, _ = feature_range


    def fit_transform(self, X : np.ndarray) -> np.ndarray:
        return self.t0 + (
            (X - self.x_min) / (self.x_max - self.x_min)
        )


    def inverse_transform(self, X : np.ndarray) -> np.ndarray:
        return (X - self.t0) * (self.x_max - self.x_min) \
               + self.x_min
	# -- encoding: utf-8 --

	"""
	Custom Built Scaler Functioonality

	Data scaling is important for faster model convergence and model
	training. While `sklearn.preprocessing` provides adequate scaling
	functionalities, however some are limited like giving a custom
	feature range which is fulfilled by `RangedScaler` implementation.
	"""

	import numpy as np

	class UnivariateRangedScaler(object):
	"""
	A Specialized `MinMaxScaler` that Considers a Feature Range

	The function is developed such that the scaling operation can be
	acheived by passing a feature range `(X_min, X_max)` for a given
	univariate series. The model considers the range and scales the
	value even if `X_min` and/or `X_max` is not present in the actual
	series. Mathemaically, the scalling formula is given as:

	```math
	x' = t0 + ((x - x_min) / (x_max - x_min))
	```

	In case `feature_range` parameter is not required, please use the
	`sklearn.preprocessing.MinMaxScaler` which is efficient and
	robust to handle univariate and multivariate data series.

	Performance Warning: Currently, the function can only work for a
	feature range of `(t0, t1), where t1 = t0 + 1` because `t1` is
	not yet considered in calculation.
	"""

	def __init__(
	self,
	x_min : float,
	x_max : float,
	feature_range : tuple = (0, 1)
	) -> None:
	self.x_min = x_min
	self.x_max = x_max

	# scale the data into a desired range
	self.t0, _ = feature_range


	def fit_transform(self, X : np.ndarray) -> np.ndarray:
	return self.t0 + (
	(X - self.x_min) / (self.x_max - self.x_min)
	)


	def inverse_transform(self, X : np.ndarray) -> np.ndarray:
	return (X - self.t0) * (self.x_max - self.x_min) \
	+ self.x_min