system_1.py 文件源码

def predict(self, X):
        y_pred = []
        for x in X:
            dists = euclidean_distances([x], self.X)[0]
            simi_m = 1 / (1 + dists)
            nearest_com = self.y_comm[simi_m.argsort()[-self.k:]]
            y_pred.append(self.mapping[Counter(nearest_com).most_common(1)[0][0]])

        return np.array(y_pred)