#importare tutti i pacchetti necessari per costruire un classificatore o un regressore
#che erediti i metodi dei classificatori o regressori di sklearn
import numpy as np
from sklearn.base import BaseEstimator, ClassifierMixin

class Custom_Classifier(ClassifierMixin, BaseEstimator):
    #Initialization of the hyper-parameters 
    def __init__(self, param1, param2):
        self.param1 = param1
        self.param2 = param2

    def fit(self, X, y):
        #Training phase of the model. Implement all the steps that yield the attributes needed for the prediction phase

        return self

    def decision_function(self, X):
        #Whenever necessary, implement a function that associate to every element x a score that determines the label assigned to x
        score=[]
        return score

    def predict(self, X):
        #Prediction phase of the model. Assign to every x in X a label saved in y_pred
        y_pred=np.zeros(X.shape[0])
        return y_pred