【udacity】机器学习-knn最近邻算法

Evernote Export

1.基于实例的学习介绍

不同级别的学习，去除所有的数据点(xi​,yi​)，然后放入一个数据库中，下次直接提取数据
但是这样的实现方法将不能进行泛化，这种方式只能简单的进行数据提取，它也会存储很多的噪音

3.最近邻算法

KNN算法原理就是查找未知点的最近已知点，然后进行归类，但是当遇到最近邻都相差不多的时候，就需要更大的背景去识别
k的意思是邻居的数量

4.KNN算法(伪代码)

Training Data--> D={xi,yi} #训练数据（输入的集合）
Diastance Metric--> d(q,x) #距离度量的方式
Number of Neighbors--> K #邻居的数量(已知)
Query Point--> q (未知点)
-NN = {i:d(qi,xi) k smallest} （由距离查询点最近的所有元素组成的）
-return
    -classification
    -regression

5.KNN算法的时间统计

	Running Time	Space
1-NN learning	1	n
1-NN query	logn	1
k-NN learning	1	n
k-NN query	logn+k	1
linear regression learning	n	1
linear regression query	1	1

我们提出了k近邻算法，算法的核心思想是，即是给定一个训练数据集，对新的输入实例，在训练数据集中找到与该实例最邻近的K个实例，这K个实例的多数属于某个类，就把该输入实例分类到这个类中。更通俗说一遍算法的过程，来了一个新的输入实例，我们算出该实例与每一个训练点的距离（这里的复杂度为0(n)比较大，所以引出了下文的kd树等结构），然后找到前k个，这k个哪个类别数最多，我们就判断新的输入实例就是哪类！knn算法是一种懒惰算法

6.KNN偏差

KNN概述

k-近邻算法(K-Nearest Neighbour alorithm)，又称为KNN算法，是数据挖掘技术中原理最简单的算法。
**KNN的工作原理：**给定一个已知标签类别的训练数据集，输入没有标签的新数据后，在训练数据集中找到与新数据最近邻的K个实例，如果这K个实例的多数属于某个类别，那么新数据就属于这个类别。
**可以简单理解为：**由哪些离X最近的k个点来投票决定x归为哪个类
K近邻算法就是用两点之间的距离来计算

∣AB∣=(x1​−x2​)2+(y1​−y2​)2​

当多个特征扩展到N维空间的时候，一般采用欧式距离来计算

dist(x,y)=(x1​−y1​)2+(x2​−y2​)2+...+(xn​−yn​)2​=i=1∑n​(xi​−yi​)2​

K近邻算法的计算步骤
1.计算已知类别数据集中的点与当前点之间的距离
2.按照距离递增次序排序
3.选取与当前点距离最小的k个点
4.确定前k个点在类别的出现频率
5.返回前k个点出现频率最高的类别作为当前点的预测类别

KNN分类器
分类器并不会百分百的得到正确的结果，我们使用很多种方法来验证分类器的准确率，此外，分类器的性能野兽很多因素的影响，比如k的取值就在很大程度上影响了分类器的预测结果，还有分类器的设置、原始数据集等。为了测试分类器的效果，我们把原始的数据集分为两个部分，一部分用来训练算法(称为训练集)，一部分用来测试算法的准确率(称为测试集)。同时，k-近邻算法没有进行数据的训练，直接使用的是未知的数据和已知数据进行比较。
也就是说knn分类器是懒惰算法

小结

k-近邻
算法功能	分类(核心)，回归
算法类型	有监督学习，惰性学习，距离类模型
数据输入	包含数据标签y，且特征空间中至少包含k个训练样本(k>=1);特征空间中各个特征的量纲需统一，若不统一则需要进行归一化处理;自定义的超参数k(k>=1)
模型输出	在KNN分类中，输出是标签中的某个类别;在KNN回归中，输出是对象的属性值，该值是距离输入的数据最近的k个训练样本标签的平均值

%23%23%23%23%201.%E5%9F%BA%E4%BA%8E%E5%AE%9E%E4%BE%8B%E7%9A%84%E5%AD%A6%E4%B9%A0%E4%BB%8B%E7%BB%8D%0A%E4%B8%8D%E5%90%8C%E7%BA%A7%E5%88%AB%E7%9A%84%E5%AD%A6%E4%B9%A0%EF%BC%8C%E5%8E%BB%E9%99%A4%E6%89%80%E6%9C%89%E7%9A%84%E6%95%B0%E6%8D%AE%E7%82%B9(%24x_i%2Cy_i%24)%EF%BC%8C%E7%84%B6%E5%90%8E%E6%94%BE%E5%85%A5%E4%B8%80%E4%B8%AA%E6%95%B0%E6%8D%AE%E5%BA%93%E4%B8%AD%EF%BC%8C%E4%B8%8B%E6%AC%A1%E7%9B%B4%E6%8E%A5%E6%8F%90%E5%8F%96%E6%95%B0%E6%8D%AE%0A%E4%BD%86%E6%98%AF%E8%BF%99%E6%A0%B7%E7%9A%84%E5%AE%9E%E7%8E%B0%E6%96%B9%E6%B3%95%E5%B0%86%E4%B8%8D%E8%83%BD%E8%BF%9B%E8%A1%8C%E6%B3%9B%E5%8C%96%EF%BC%8C%E8%BF%99%E7%A7%8D%E6%96%B9%E5%BC%8F%E5%8F%AA%E8%83%BD%E7%AE%80%E5%8D%95%E7%9A%84%E8%BF%9B%E8%A1%8C%E6%95%B0%E6%8D%AE%E6%8F%90%E5%8F%96%EF%BC%8C%E5%AE%83%E4%B9%9F%E4%BC%9A%E5%AD%98%E5%82%A8%E5%BE%88%E5%A4%9A%E7%9A%84%E5%99%AA%E9%9F%B3%0A%0A%23%23%23%23%203.%E6%9C%80%E8%BF%91%E9%82%BB%E7%AE%97%E6%B3%95%0AKNN%E7%AE%97%E6%B3%95%E5%8E%9F%E7%90%86%E5%B0%B1%E6%98%AF%E6%9F%A5%E6%89%BE%E6%9C%AA%E7%9F%A5%E7%82%B9%E7%9A%84%E6%9C%80%E8%BF%91%E5%B7%B2%E7%9F%A5%E7%82%B9%EF%BC%8C%E7%84%B6%E5%90%8E%E8%BF%9B%E8%A1%8C%E5%BD%92%E7%B1%BB%EF%BC%8C%E4%BD%86%E6%98%AF%E5%BD%93%E9%81%87%E5%88%B0%E6%9C%80%E8%BF%91%E9%82%BB%E9%83%BD%E7%9B%B8%E5%B7%AE%E4%B8%8D%E5%A4%9A%E7%9A%84%E6%97%B6%E5%80%99%EF%BC%8C%E5%B0%B1%E9%9C%80%E8%A6%81%E6%9B%B4%E5%A4%A7%E7%9A%84%E8%83%8C%E6%99%AF%E5%8E%BB%E8%AF%86%E5%88%AB%0A*k%E7%9A%84%E6%84%8F%E6%80%9D%E6%98%AF%E9%82%BB%E5%B1%85%E7%9A%84%E6%95%B0%E9%87%8F*%0A%0A%23%23%23%23%204.KNN%E7%AE%97%E6%B3%95(%E4%BC%AA%E4%BB%A3%E7%A0%81)%0A%60%60%60%0ATraining%20Data--%3E%20D%3D%7Bxi%2Cyi%7D%20%23%E8%AE%AD%E7%BB%83%E6%95%B0%E6%8D%AE%EF%BC%88%E8%BE%93%E5%85%A5%E7%9A%84%E9%9B%86%E5%90%88%EF%BC%89%0ADiastance%20Metric--%3E%20d(q%2Cx)%20%23%E8%B7%9D%E7%A6%BB%E5%BA%A6%E9%87%8F%E7%9A%84%E6%96%B9%E5%BC%8F%0ANumber%20of%20Neighbors--%3E%20K%20%23%E9%82%BB%E5%B1%85%E7%9A%84%E6%95%B0%E9%87%8F(%E5%B7%B2%E7%9F%A5)%0AQuery%20Point--%3E%20q%20(%E6%9C%AA%E7%9F%A5%E7%82%B9)%0A-NN%20%3D%20%7Bi%3Ad(qi%2Cxi)%20k%20smallest%7D%20%EF%BC%88%E7%94%B1%E8%B7%9D%E7%A6%BB%E6%9F%A5%E8%AF%A2%E7%82%B9%E6%9C%80%E8%BF%91%E7%9A%84%E6%89%80%E6%9C%89%E5%85%83%E7%B4%A0%E7%BB%84%E6%88%90%E7%9A%84%EF%BC%89%0A-return%0A%20%20%20%20-classification%0A%20%20%20%20-regression%0A%60%60%60%0A%23%23%23%23%205.KNN%E7%AE%97%E6%B3%95%E7%9A%84%E6%97%B6%E9%97%B4%E7%BB%9F%E8%AE%A1%0A%0A%7C%20%20%7C%20Running%20Time%20%7C%20Space%20%7C%0A%7C%20---%20%7C%20---%20%7C%20---%20%7C%0A%7C%201-NN%20learning%7C%201%20%7C%20n%20%7C%0A%7C%201-NN%20query%20%7C%20logn%20%7C%201%20%7C%0A%7C%20k-NN%20learning%20%7C%201%20%7C%20n%20%7C%0A%7C%20k-NN%20query%20%7C%20logn%2Bk%20%7C%201%20%7C%0A%7C%20linear%20regression%20learning%20%7C%20n%20%7C%201%20%7C%0A%7C%20linear%20regression%20query%20%7C%201%20%7C%201%20%7C%0A%0A%3E%E6%88%91%E4%BB%AC%E6%8F%90%E5%87%BA%E4%BA%86k%E8%BF%91%E9%82%BB%E7%AE%97%E6%B3%95%EF%BC%8C%E7%AE%97%E6%B3%95%E7%9A%84%E6%A0%B8%E5%BF%83%E6%80%9D%E6%83%B3%E6%98%AF%EF%BC%8C%E5%8D%B3%E6%98%AF%E7%BB%99%E5%AE%9A%E4%B8%80%E4%B8%AA%E8%AE%AD%E7%BB%83%E6%95%B0%E6%8D%AE%E9%9B%86%EF%BC%8C%E5%AF%B9%E6%96%B0%E7%9A%84%E8%BE%93%E5%85%A5%E5%AE%9E%E4%BE%8B%EF%BC%8C%E5%9C%A8%E8%AE%AD%E7%BB%83%E6%95%B0%E6%8D%AE%E9%9B%86%E4%B8%AD%E6%89%BE%E5%88%B0%E4%B8%8E%E8%AF%A5%E5%AE%9E%E4%BE%8B%E6%9C%80%E9%82%BB%E8%BF%91%E7%9A%84K%E4%B8%AA%E5%AE%9E%E4%BE%8B%EF%BC%8C%E8%BF%99K%E4%B8%AA%E5%AE%9E%E4%BE%8B%E7%9A%84%E5%A4%9A%E6%95%B0%E5%B1%9E%E4%BA%8E%E6%9F%90%E4%B8%AA%E7%B1%BB%EF%BC%8C%E5%B0%B1%E6%8A%8A%E8%AF%A5%E8%BE%93%E5%85%A5%E5%AE%9E%E4%BE%8B%E5%88%86%E7%B1%BB%E5%88%B0%E8%BF%99%E4%B8%AA%E7%B1%BB%E4%B8%AD%E3%80%82%E6%9B%B4%E9%80%9A%E4%BF%97%E8%AF%B4%E4%B8%80%E9%81%8D%E7%AE%97%E6%B3%95%E7%9A%84%E8%BF%87%E7%A8%8B%EF%BC%8C%E6%9D%A5%E4%BA%86%E4%B8%80%E4%B8%AA%E6%96%B0%E7%9A%84%E8%BE%93%E5%85%A5%E5%AE%9E%E4%BE%8B%EF%BC%8C%E6%88%91%E4%BB%AC%E7%AE%97%E5%87%BA%E8%AF%A5%E5%AE%9E%E4%BE%8B%E4%B8%8E%E6%AF%8F%E4%B8%80%E4%B8%AA%E8%AE%AD%E7%BB%83%E7%82%B9%E7%9A%84%E8%B7%9D%E7%A6%BB%EF%BC%88%E8%BF%99%E9%87%8C%E7%9A%84%E5%A4%8D%E6%9D%82%E5%BA%A6%E4%B8%BA0(n)%E6%AF%94%E8%BE%83%E5%A4%A7%EF%BC%8C%E6%89%80%E4%BB%A5%E5%BC%95%E5%87%BA%E4%BA%86%E4%B8%8B%E6%96%87%E7%9A%84kd%E6%A0%91%E7%AD%89%E7%BB%93%E6%9E%84%EF%BC%89%EF%BC%8C%E7%84%B6%E5%90%8E%E6%89%BE%E5%88%B0%E5%89%8Dk%E4%B8%AA%EF%BC%8C%E8%BF%99k%E4%B8%AA%E5%93%AA%E4%B8%AA%E7%B1%BB%E5%88%AB%E6%95%B0%E6%9C%80%E5%A4%9A%EF%BC%8C%E6%88%91%E4%BB%AC%E5%B0%B1%E5%88%A4%E6%96%AD%E6%96%B0%E7%9A%84%E8%BE%93%E5%85%A5%E5%AE%9E%E4%BE%8B%E5%B0%B1%E6%98%AF%E5%93%AA%E7%B1%BB%EF%BC%81**knn%E7%AE%97%E6%B3%95%E6%98%AF%E4%B8%80%E7%A7%8D%E6%87%92%E6%83%B0%E7%AE%97%E6%B3%95**%0A%0A%23%23%23%23%206.KNN%E5%81%8F%E5%B7%AE%0A%0A%23%23%23%20KNN%E6%A6%82%E8%BF%B0%0Ak-%E8%BF%91%E9%82%BB%E7%AE%97%E6%B3%95(K-Nearest%20Neighbour%20alorithm)%EF%BC%8C%E5%8F%88%E7%A7%B0%E4%B8%BAKNN%E7%AE%97%E6%B3%95%EF%BC%8C%E6%98%AF%E6%95%B0%E6%8D%AE%E6%8C%96%E6%8E%98%E6%8A%80%E6%9C%AF%E4%B8%AD%E5%8E%9F%E7%90%86%E6%9C%80%E7%AE%80%E5%8D%95%E7%9A%84%E7%AE%97%E6%B3%95%E3%80%82%0A**KNN%E7%9A%84%E5%B7%A5%E4%BD%9C%E5%8E%9F%E7%90%86%EF%BC%9A**%E7%BB%99%E5%AE%9A%E4%B8%80%E4%B8%AA%E5%B7%B2%E7%9F%A5%E6%A0%87%E7%AD%BE%E7%B1%BB%E5%88%AB%E7%9A%84%E8%AE%AD%E7%BB%83%E6%95%B0%E6%8D%AE%E9%9B%86%EF%BC%8C%E8%BE%93%E5%85%A5%E6%B2%A1%E6%9C%89%E6%A0%87%E7%AD%BE%E7%9A%84%E6%96%B0%E6%95%B0%E6%8D%AE%E5%90%8E%EF%BC%8C%E5%9C%A8%E8%AE%AD%E7%BB%83%E6%95%B0%E6%8D%AE%E9%9B%86%E4%B8%AD%E6%89%BE%E5%88%B0%E4%B8%8E%E6%96%B0%E6%95%B0%E6%8D%AE%E6%9C%80%E8%BF%91%E9%82%BB%E7%9A%84K%E4%B8%AA%E5%AE%9E%E4%BE%8B%EF%BC%8C%E5%A6%82%E6%9E%9C%E8%BF%99K%E4%B8%AA%E5%AE%9E%E4%BE%8B%E7%9A%84%E5%A4%9A%E6%95%B0%E5%B1%9E%E4%BA%8E%E6%9F%90%E4%B8%AA%E7%B1%BB%E5%88%AB%EF%BC%8C%E9%82%A3%E4%B9%88%E6%96%B0%E6%95%B0%E6%8D%AE%E5%B0%B1%E5%B1%9E%E4%BA%8E%E8%BF%99%E4%B8%AA%E7%B1%BB%E5%88%AB%E3%80%82%0A**%E5%8F%AF%E4%BB%A5%E7%AE%80%E5%8D%95%E7%90%86%E8%A7%A3%E4%B8%BA%EF%BC%9A**%E7%94%B1%E5%93%AA%E4%BA%9B%E7%A6%BBX%E6%9C%80%E8%BF%91%E7%9A%84k%E4%B8%AA%E7%82%B9%E6%9D%A5%E6%8A%95%E7%A5%A8%E5%86%B3%E5%AE%9Ax%E5%BD%92%E4%B8%BA%E5%93%AA%E4%B8%AA%E7%B1%BB%0AK%E8%BF%91%E9%82%BB%E7%AE%97%E6%B3%95%E5%B0%B1%E6%98%AF%E7%94%A8%E4%B8%A4%E7%82%B9%E4%B9%8B%E9%97%B4%E7%9A%84%E8%B7%9D%E7%A6%BB%E6%9D%A5%E8%AE%A1%E7%AE%97%0A%24%24%7CAB%7C%20%3D%20%5Csqrt%7B(x_1-x_2)%5E2%2B(y_1-y_2)%5E2%7D%24%24%0A**%E5%BD%93%E5%A4%9A%E4%B8%AA%E7%89%B9%E5%BE%81%E6%89%A9%E5%B1%95%E5%88%B0N%E7%BB%B4%E7%A9%BA%E9%97%B4%E7%9A%84%E6%97%B6%E5%80%99%EF%BC%8C%E4%B8%80%E8%88%AC%E9%87%87%E7%94%A8%E6%AC%A7%E5%BC%8F%E8%B7%9D%E7%A6%BB%E6%9D%A5%E8%AE%A1%E7%AE%97**%0A%24%24dist(x%2Cy)%3D%5Csqrt%7B(x_1-y_1)%5E2%2B(x_2-y_2)%5E2%2B...%2B(x_n-y_n)%5E2%7D%3D%5Csqrt%7B%5Csum%5En_%7Bi%3D1%7D(x_i-y_i)%5E2%7D%24%24%0A%0A**K%E8%BF%91%E9%82%BB%E7%AE%97%E6%B3%95%E7%9A%84%E8%AE%A1%E7%AE%97%E6%AD%A5%E9%AA%A4**%0A1.%E8%AE%A1%E7%AE%97%E5%B7%B2%E7%9F%A5%E7%B1%BB%E5%88%AB%E6%95%B0%E6%8D%AE%E9%9B%86%E4%B8%AD%E7%9A%84%E7%82%B9%E4%B8%8E%E5%BD%93%E5%89%8D%E7%82%B9%E4%B9%8B%E9%97%B4%E7%9A%84%E8%B7%9D%E7%A6%BB%0A2.%E6%8C%89%E7%85%A7%E8%B7%9D%E7%A6%BB%E9%80%92%E5%A2%9E%E6%AC%A1%E5%BA%8F%E6%8E%92%E5%BA%8F%0A3.%E9%80%89%E5%8F%96%E4%B8%8E%E5%BD%93%E5%89%8D%E7%82%B9%E8%B7%9D%E7%A6%BB%E6%9C%80%E5%B0%8F%E7%9A%84k%E4%B8%AA%E7%82%B9%0A4.%E7%A1%AE%E5%AE%9A%E5%89%8Dk%E4%B8%AA%E7%82%B9%E5%9C%A8%E7%B1%BB%E5%88%AB%E7%9A%84%E5%87%BA%E7%8E%B0%E9%A2%91%E7%8E%87%0A5.%E8%BF%94%E5%9B%9E%E5%89%8Dk%E4%B8%AA%E7%82%B9%E5%87%BA%E7%8E%B0%E9%A2%91%E7%8E%87%E6%9C%80%E9%AB%98%E7%9A%84%E7%B1%BB%E5%88%AB%E4%BD%9C%E4%B8%BA%E5%BD%93%E5%89%8D%E7%82%B9%E7%9A%84%E9%A2%84%E6%B5%8B%E7%B1%BB%E5%88%AB%0A%0A**KNN%E5%88%86%E7%B1%BB%E5%99%A8**%0A%E5%88%86%E7%B1%BB%E5%99%A8%E5%B9%B6%E4%B8%8D%E4%BC%9A%E7%99%BE%E5%88%86%E7%99%BE%E7%9A%84%E5%BE%97%E5%88%B0%E6%AD%A3%E7%A1%AE%E7%9A%84%E7%BB%93%E6%9E%9C%EF%BC%8C%E6%88%91%E4%BB%AC%E4%BD%BF%E7%94%A8%E5%BE%88%E5%A4%9A%E7%A7%8D%E6%96%B9%E6%B3%95%E6%9D%A5%E9%AA%8C%E8%AF%81%E5%88%86%E7%B1%BB%E5%99%A8%E7%9A%84%E5%87%86%E7%A1%AE%E7%8E%87%EF%BC%8C%E6%AD%A4%E5%A4%96%EF%BC%8C%E5%88%86%E7%B1%BB%E5%99%A8%E7%9A%84%E6%80%A7%E8%83%BD%E9%87%8E%E5%85%BD%E5%BE%88%E5%A4%9A%E5%9B%A0%E7%B4%A0%E7%9A%84%E5%BD%B1%E5%93%8D%EF%BC%8C%E6%AF%94%E5%A6%82k%E7%9A%84%E5%8F%96%E5%80%BC%E5%B0%B1%E5%9C%A8%E5%BE%88%E5%A4%A7%E7%A8%8B%E5%BA%A6%E4%B8%8A%E5%BD%B1%E5%93%8D%E4%BA%86%E5%88%86%E7%B1%BB%E5%99%A8%E7%9A%84%E9%A2%84%E6%B5%8B%E7%BB%93%E6%9E%9C%EF%BC%8C%E8%BF%98%E6%9C%89%E5%88%86%E7%B1%BB%E5%99%A8%E7%9A%84%E8%AE%BE%E7%BD%AE%E3%80%81%E5%8E%9F%E5%A7%8B%E6%95%B0%E6%8D%AE%E9%9B%86%E7%AD%89%E3%80%82%E4%B8%BA%E4%BA%86%E6%B5%8B%E8%AF%95%E5%88%86%E7%B1%BB%E5%99%A8%E7%9A%84%E6%95%88%E6%9E%9C%EF%BC%8C%E6%88%91%E4%BB%AC%E6%8A%8A%E5%8E%9F%E5%A7%8B%E7%9A%84%E6%95%B0%E6%8D%AE%E9%9B%86%E5%88%86%E4%B8%BA%E4%B8%A4%E4%B8%AA%E9%83%A8%E5%88%86%EF%BC%8C%E4%B8%80%E9%83%A8%E5%88%86%E7%94%A8%E6%9D%A5%E8%AE%AD%E7%BB%83%E7%AE%97%E6%B3%95(%E7%A7%B0%E4%B8%BA%E8%AE%AD%E7%BB%83%E9%9B%86)%EF%BC%8C%E4%B8%80%E9%83%A8%E5%88%86%E7%94%A8%E6%9D%A5%E6%B5%8B%E8%AF%95%E7%AE%97%E6%B3%95%E7%9A%84%E5%87%86%E7%A1%AE%E7%8E%87(%E7%A7%B0%E4%B8%BA%E6%B5%8B%E8%AF%95%E9%9B%86)%E3%80%82%E5%90%8C%E6%97%B6%EF%BC%8Ck-%E8%BF%91%E9%82%BB%E7%AE%97%E6%B3%95%E6%B2%A1%E6%9C%89%E8%BF%9B%E8%A1%8C%E6%95%B0%E6%8D%AE%E7%9A%84%E8%AE%AD%E7%BB%83%EF%BC%8C%E7%9B%B4%E6%8E%A5%E4%BD%BF%E7%94%A8%E7%9A%84%E6%98%AF%E6%9C%AA%E7%9F%A5%E7%9A%84%E6%95%B0%E6%8D%AE%E5%92%8C%E5%B7%B2%E7%9F%A5%E6%95%B0%E6%8D%AE%E8%BF%9B%E8%A1%8C%E6%AF%94%E8%BE%83%E3%80%82%0A**%E4%B9%9F%E5%B0%B1%E6%98%AF%E8%AF%B4knn%E5%88%86%E7%B1%BB%E5%99%A8%E6%98%AF%E6%87%92%E6%83%B0%E7%AE%97%E6%B3%95**%0A%0A**%E5%B0%8F%E7%BB%93**%0A%7Ck-%E8%BF%91%E9%82%BB%7C%20%7C%0A----%7C---%7C%0A%E7%AE%97%E6%B3%95%E5%8A%9F%E8%83%BD%7C%E5%88%86%E7%B1%BB(%E6%A0%B8%E5%BF%83)%EF%BC%8C%E5%9B%9E%E5%BD%92%7C%0A%E7%AE%97%E6%B3%95%E7%B1%BB%E5%9E%8B%7C%E6%9C%89%E7%9B%91%E7%9D%A3%E5%AD%A6%E4%B9%A0%EF%BC%8C%E6%83%B0%E6%80%A7%E5%AD%A6%E4%B9%A0%EF%BC%8C%E8%B7%9D%E7%A6%BB%E7%B1%BB%E6%A8%A1%E5%9E%8B%7C%0A%E6%95%B0%E6%8D%AE%E8%BE%93%E5%85%A5%7C%E5%8C%85%E5%90%AB%E6%95%B0%E6%8D%AE%E6%A0%87%E7%AD%BEy%EF%BC%8C%E4%B8%94%E7%89%B9%E5%BE%81%E7%A9%BA%E9%97%B4%E4%B8%AD%E8%87%B3%E5%B0%91%E5%8C%85%E5%90%ABk%E4%B8%AA%E8%AE%AD%E7%BB%83%E6%A0%B7%E6%9C%AC(k%3E%3D1)%3B%E7%89%B9%E5%BE%81%E7%A9%BA%E9%97%B4%E4%B8%AD%E5%90%84%E4%B8%AA%E7%89%B9%E5%BE%81%E7%9A%84%E9%87%8F%E7%BA%B2%E9%9C%80%E7%BB%9F%E4%B8%80%EF%BC%8C%E8%8B%A5%E4%B8%8D%E7%BB%9F%E4%B8%80%E5%88%99%E9%9C%80%E8%A6%81%E8%BF%9B%E8%A1%8C%E5%BD%92%E4%B8%80%E5%8C%96%E5%A4%84%E7%90%86%3B%E8%87%AA%E5%AE%9A%E4%B9%89%E7%9A%84%E8%B6%85%E5%8F%82%E6%95%B0k(k%3E%3D1)%7C%0A%E6%A8%A1%E5%9E%8B%E8%BE%93%E5%87%BA%7C%E5%9C%A8KNN%E5%88%86%E7%B1%BB%E4%B8%AD%EF%BC%8C%E8%BE%93%E5%87%BA%E6%98%AF%E6%A0%87%E7%AD%BE%E4%B8%AD%E7%9A%84%E6%9F%90%E4%B8%AA%E7%B1%BB%E5%88%AB%3B%E5%9C%A8KNN%E5%9B%9E%E5%BD%92%E4%B8%AD%EF%BC%8C%E8%BE%93%E5%87%BA%E6%98%AF%E5%AF%B9%E8%B1%A1%E7%9A%84%E5%B1%9E%E6%80%A7%E5%80%BC%EF%BC%8C%E8%AF%A5%E5%80%BC%E6%98%AF%E8%B7%9D%E7%A6%BB%E8%BE%93%E5%85%A5%E7%9A%84%E6%95%B0%E6%8D%AE%E6%9C%80%E8%BF%91%E7%9A%84k%E4%B8%AA%E8%AE%AD%E7%BB%83%E6%A0%B7%E6%9C%AC%E6%A0%87%E7%AD%BE%E7%9A%84%E5%B9%B3%E5%9D%87%E5%80%BC%7C%0A%23%23%201.%E4%BC%98%E7%82%B9%0A*%20%E7%AE%80%E5%8D%95%E5%A5%BD%E7%94%A8%EF%BC%8C%E5%AE%B9%E6%98%93%E7%90%86%E8%A7%A3%EF%BC%8C%E7%B2%BE%E5%BA%A6%E9%AB%98%EF%BC%8C%E7%90%86%E8%AE%BA%E6%88%90%E7%86%9F%EF%BC%8C%E6%97%A2%E5%8F%AF%E4%BB%A5%E7%94%A8%E6%9D%A5%E5%81%9A%E5%88%86%E7%B1%BB%E4%B9%9F%E5%8F%AF%E4%BB%A5%E7%94%A8%E6%9D%A5%E5%81%9A%E5%9B%9E%E5%BD%92%0A*%20%E5%8F%AF%E7%94%A8%E4%BA%8E%E6%95%B0%E5%80%BC%E5%9E%8B%E6%95%B0%E6%8D%AE%E5%92%8C%E7%A6%BB%E6%95%A3%E5%9E%8B%E6%95%B0%E6%8D%AE%0A*%20%E6%97%A0%E6%95%B0%E6%8D%AE%E8%BE%93%E5%85%A5%E5%81%87%E5%AE%9A%0A*%20%E9%80%82%E5%90%88%E5%AF%B9%E7%A8%80%E6%9C%89%E4%BA%8B%E4%BB%B6%E8%BF%9B%E8%A1%8C%E5%88%86%E7%B1%BB%0A%23%23%202.%E7%BC%BA%E7%82%B9%0A*%20%E8%AE%A1%E7%AE%97%E5%A4%8D%E6%9D%82%E6%80%A7%E9%AB%98%EF%BC%8C%E7%A9%BA%E9%97%B4%E5%A4%8D%E6%9D%82%E6%80%A7%E9%AB%98%0A*%20%E8%AE%A1%E7%AE%97%E9%87%8F%E5%A4%AA%E5%A4%A7%EF%BC%8C%E6%89%80%E4%BB%A5%E4%B8%80%E8%88%AC%E6%95%B0%E5%80%BC%E5%BE%88%E5%A4%A7%E6%97%B6%E5%80%99%E4%B8%8D%E4%BD%BF%E7%94%A8%EF%BC%8C%E5%8D%95%E4%B8%AA%E6%A0%B7%E6%9C%AC%E4%B9%9F%E4%B8%8D%E8%83%BD%E5%A4%AA%E5%B0%91%EF%BC%8C%E5%AE%B9%E6%98%93%E5%8F%91%E7%94%9F%E8%AF%AF%E5%88%86%0A*%20%E6%A0%B7%E6%9C%AC%E4%B8%8D%E5%B9%B3%E8%A1%A1%E9%97%AE%E9%A2%98(%E5%8D%B3%E6%9C%89%E4%BA%9B%E7%B1%BB%E5%9E%8B%E7%9A%84%E6%A0%B7%E6%9C%AC%E6%95%B0%E9%87%8F%E5%BE%88%E5%A4%9A%EF%BC%8C%E8%80%8C%E5%85%B6%E4%BB%96%E6%A0%B7%E6%9C%AC%E7%9A%84%E6%95%B0%E9%87%8F%E5%BE%88%E5%B0%91)%0A*%20%E5%8F%AF%E7%90%86%E8%A7%A3%E6%80%A7%E6%AF%94%E8%BE%83%E5%B7%AE%EF%BC%8C%E6%97%A0%E6%B3%95%E7%BB%99%E5%87%BA%E6%95%B0%E6%8D%AE%E7%9A%84%E5%86%85%E5%9C%A8%E5%90%AB%E4%B9%89%0A%0A%0A%0A%0A%0A%0A%0A%0A

 

转载于:https://www.cnblogs.com/pandaboy1123/p/10254061.html