TanhTransformer.py

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from ._CustomTransformer import _CustomTransformer
 
import numpy as np 
 
 
class TanhTransformer(_CustomTransformer):
    ''' tanh-estimator (Hampel et al. 1986; Latha & Thangasamy, 2011) '''
    scale = 0.01
 
    def _fit(self, X, *args, **kwargs):
        m = np.median(X, 0)
        d = np.abs(X - m)
 
        a = np.percentile(d, 70, 0)
        b = np.percentile(d, 85, 0)
        c = np.percentile(d, 95, 0)
 
        Xab = np.abs(X)
        Xsi = np.sign(X)
        phi = np.zeros(X.shape)
        idx = np.logical_and(0 <= Xab, Xab < a)
        phi[idx] = X[idx]
        idx = np.logical_and(a <= Xab, Xab < b)
        phi[idx] = (a * Xsi)[idx]
        idx = np.logical_and(b <= Xab, Xab < c)
        phi[idx] = (a * Xsi * ((c - Xab) / (c - b)))[idx]
 
        self.mu_gh  = np.mean(phi, 0)
        self.sig_gh = np.std(phi, 0) 
 
    def _transform(self, X, *args, **kwargs):
        return 0.5 * (np.tanh(self.scale * ((X - self.mu_gh)/self.sig_gh)) + 1)
 
    def _inverse_transform(self, X, *args, **kwargs):
        return ((np.tan(X * 2 - 1) / self.scale) * self.sig_gh) + self.mu_gh