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简介

这两天我用了KNN方法对Cifar10数据进行分类，结果却是差强人意，只有30%左右的正确率。

KNN算法的训练只是将训练数据集存储起来，所以训练不需要花费很多时间，但是测试就需要花费大量时间。



对于MNIST数据集，该分类器效果很好，原因我觉得主要是MNIST数据集都是黑白照片，KNN本质上是通过图象的像素差来进行计算的，所以MNIST数据集图像像素差包含的信息比较多。

代码

my_utils.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @File  : my_utils.py
# @Author: Fly_dragon
# @Date  : 2019/11/29
# @Desc  : 

import numpy as np


def getXmean(x_train):
    x_train = np.reshape(x_train, (x_train.shape[0], -1))  # Turn the image to 1-D
    mean_image = np.mean(x_train, axis=0)  # 求每一列均值。即求所有图片每一个像素上的平均值
    return mean_image


def centralized(x_test, mean_image):
    x_test = np.reshape(x_test, (x_test.shape[0], -1))
    x_test = x_test.astype(np.float)
    x_test -= mean_image  # Subtract the mean from the graph, and you get zero mean graph
    return x_test

#%% KNN class
class Knn:

    def __init__(self):
        pass

    def fit(self, X_train, y_train):
        self.Xtr = X_train
        self.ytr = y_train

    def predict(self, k, dis, X_test):
        """

        """
        assert dis == 'E' or dis == 'M'
        num_test = X_test.shape[0]
        label_list = []
        # 使用欧拉公式作为距离测量
        if dis == 'E':
            for i in range(num_test):
                distances = np.sqrt(np.sum(((self.Xtr - np.tile(X_test[i],
                                                                (self.Xtr.shape