Method _predict

01 KNN/KNN.py:20–30

(self, x)

        return predictions

    def _predict(self, x):
        # compute the distance
        distances = [euclidean_distance(x, x_train) for x_train in self.X_train]
    
        # get the closest k
        k_indices = np.argsort(distances)[:self.k]
        k_nearest_labels = [self.y_train[i] for i in k_indices]

        # majority voye
        most_common = Counter(k_nearest_labels).most_common()
        return most_common[0][0]