class SharedScalerModel:

    def __init__(self, scaler, lm):
        self.scaler = scaler
        self.lm = lm
        self.coef_ = lm.coef_
        self.intercept_ = lm.intercept_

    def predict(self, X):
        return self.lm.predict(self.scaler.transform(X))

def fit_linear_model(x, y):
    lr = Ridge()
    lr.fit(shared_scaler.transform(x), y)
    return SharedScalerModel(shared_scaler, lr)

def load_model(well_id, path):
    files = os.listdir(path)
    for name in files:
        if name.startswith(well_id) and name.endswith('.joblib'):
            return joblib.load(path+name)

def load_param(well_id, path):
    files = os.listdir(path)
    for name in files:
        if name.startswith(well_id) and name.endswith('.txt'):
            data = pd.read_csv(path+name, delimiter = '\t')
            return data