class artifact_removal_with_MAC:
def __init__(self, th = 0.1):
self.th = th
return
def get_params(self, deep=True):
return {"th": self.th}
def set_params(self, **parameters):
for parameter, value in parameters.items():
setattr(self, parameter, value)
return self
def fit(self,X,y):
from mac import MAC
self.mac = MAC(th=self.th)
self.mac.fit(X,y)
return
def transform(self, X):
return self.__noise_removal(X)
def predict(self, X):
return self.transform(X)
def fit_transform(self, X, y):
self.fit(X,y)
return self.transform(X)
def __noise_removal(self, X_true):
import numpy as np
from tqdm import tqdm
from mac import MAC
noise_est = self.mac.transform(X_true)
return np.array([[self.__ch_noise_removal(rnc, Xnc) for rnc, Xnc in zip(rn,Xn)] for rn, Xn in tqdm(zip(noise_est ,X_true))])
def __ch_noise_removal(self, noise_true, X_true):
import numpy as np
signalEmbedding = 5
noise_em = np.array([noise_true[i-signalEmbedding:i] for i in range(signalEmbedding,len(noise_true))])
X_em = np.array([X_true[i] for i in range(signalEmbedding,len(noise_true))]).reshape(-1,1)
if np.sum(noise_em) != 0.0:
from KAF import QKLMS
f = QKLMS(epsilon = 0)
noise_pred = f.evaluate(noise_em,X_em)
return X_em.ravel() - noise_pred.ravel()
else:
return X_em.ravel()
