Library "MathTransform" Auxiliary functions for transforming data using mathematical and statistical methods
scaler_zscore(x, lookback_window) Calculates Z-Score normalization of a series. Parameters: x (float): : floating point series to normalize lookback_window (int): : lookback period for calculating mean and standard deviation Returns: Z-Score normalized series
scaler_min_max(x, lookback_window, min_val, max_val, empiric_min, empiric_max, empiric_mid) Performs Min-Max scaling of a series within a given window, user-defined bounds, and optional midpoint Parameters: x (float): : floating point series to transform lookback_window (int): : int : optional lookback window size to consider for scaling. min_val (float): : float : minimum value of the scaled range. Default is 0.0. max_val (float): : float : maximum value of the scaled range. Default is 1.0. empiric_min (float): : float : user-defined minimum value of the input data. This means that the output could exceed the `min_val` bound if there is data in `x` lesser than `empiric_min`. If na, it's calculated from `x` and `lookback_window`. empiric_max (float): : float : user-defined maximum value of the input data. This means that the output could exceed the `max_val` bound if there is data in `x` greater than `empiric_max`. If na, it's calculated from `x` and `lookback_window`. empiric_mid (float): : float : user-defined midpoint value of the input data. If na, it's calculated from `empiric_min` and `empiric_max`. Returns: rescaled series
log(x, base) Applies logarithmic transformation to a value, base can be user-defined. Parameters: x (float): : floating point value to transform base (float): : logarithmic base, must be greater than 0 Returns: logarithm of the value to the given base, if x <= 0, returns logarithm of 1 to the given base
exp(x, base) Applies exponential transformation to a value, base can be user-defined. Parameters: x (float): : floating point value to transform base (float): : base of the exponentiation, must be greater than 0 Returns: the result of raising the base to the power of the value
power(x, exponent) Applies power transformation to a value, exponent can be user-defined. Parameters: x (float): : floating point value to transform exponent (float): : exponent for the transformation Returns: the value raised to the given exponent, preserving the sign of the original value
tanh(x, scale) The hyperbolic tangent is the ratio of the hyperbolic sine and hyperbolic cosine. It limits an output to a range of −1 to 1. Parameters: x (float): : floating point series scale (float)
sigmoid(x, scale, offset) Applies the sigmoid function to a series. Parameters: x (float): : floating point series to transform scale (float): : scaling factor for the sigmoid function offset (float): : offset for the sigmoid function Returns: transformed series using the sigmoid function
sigmoid_double(x, scale, offset) Applies a double sigmoid function to a series, handling positive and negative values differently. Parameters: x (float): : floating point series to transform scale (float): : scaling factor for the sigmoid function offset (float): : offset for the sigmoid function Returns: transformed series using the double sigmoid function
logistic_decay(a, b, c, t) Calculates logistic decay based on given parameters. Parameters: a (float): : parameter affecting the steepness of the curve b (float): : parameter affecting the direction of the decay c (float): : the upper bound of the function's output t (float): : time variable Returns: value of the logistic decay function at time t
Nota Keluaran
v2
Added: scaler_robust(x, lookback_window) Performs robust scaling of a series within a given window
sinh(x, scale) The hyperbolic sine (sinh) is defined as the half-difference of the exponential function of its argument.
cosh(x, scale) The hyperbolic cosine (cosh) is defined as the half-sum of the exponential function of its argument.
Updated: tanh(x, scale) The Inversie Fisher Transform, or hyperbolic tangent (tanh), is the ratio of the hyperbolic sine and hyperbolic cosine.
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