2.1 机器学习--KNN算法（分类）

1.KNN算法

1.1 算法介绍

1.2 算法原理

1.3 API介绍

1.4 代码实例

1.4.1.KNN电影分类

1.4.2.约会数据预测

1.KNN算法

1.1 算法介绍

本章节我们来学习一种分类算法，KNN（K- Nearest Neighbor）法，即 K 最邻近法，最初由 Cover 和 Hart 于1968年提出，是一个理论上比较成熟的方法，也是最简单的机器学习算法之一。

定义：如果一个样本在特征空间中的 k 个最相似（即特征空间中最邻近的样本中的大多数属于某一个类别），则该样本也属于这个类别。

我们该怎么理解呢？

KNN 的原理就是当预测一个新的值 x 的时候，根据它距离最近的K个点是什么类别来判断x属于哪个类别。听起来有点绕，我们还是看看图吧。

KNN图1.png

图中绿色的点就是我们要预测的那个点，假设K=3。那么 KNN 算法就会找到与它距离最近的三个点（这里用圆圈把它圈起来了），看看哪种类别多一些，比如这个例子中是蓝色三角形多一些，新来的绿色点就归类到蓝三角了。

KNN图2.png

但是，当K=5的时候，判定就变成不一样了。这次变成红圆多一些，所以新来的绿点被归类成红圆。从这个例子中，我们就能看得出K的取值是很重要的。

该方法在定类决策上只依据最邻近的一个或者几个样本的类别来决定待分样本所属的类别。

1.2 算法原理

KNN 的计算距离公式是基于欧式距离，两个样本的距离可以通过如下公式计算：

这样我们就明白了如何计算距离，KNN 算法最简单粗暴的就是将预测点与所有点距离进行计算，然后保存并排序，选出前面K个值看看哪些类别比较多。不足之处是计算量较大，因为对每一个待分类的文本都要计算它到全体已知样本的距离，才能求得它的K个最邻近点。

从公式可以看出，如果某一特征过大或者过小会影响我们最终的距离结果，所以在使用 KNN 算法模型预测数据之前，我们需要对数据集做标准化处理。

我们从上面的解释可以知道 K 的取值比较重要，那么我们该如何确定 K 取多少值好呢？

我们的经验数据一般 K 的取值为 3~7 之间，如果想要找到最佳的 K 值，可以通过

交叉验证

（将样本数据按照一定比例，拆分出训练用的数据和验证用的数据，比如6：4拆分出部分训练数据和验证数据），
从选取一个较小的K值开始，不断增加 K 的值，然后计算验证集合的方差，最终找到一个比较合适的K值。

1.3 API介绍

sklearn k-近邻算法API：

sklearn.neighbors.KNeighborsClassifier(n_neighbors=5, algorithm='auto')

n_neighbors： int，可选（默认 = 5），k_neighbors查询默认使用的邻居数
algorithm： {”auto“, "ball_tree", "kd_tree", "brute"}，可选用于计算最近邻居的算法：ball_tree 将会使用 BallTree，kd_tree 将使用 KDTree。auto 将尝试根据传递给 fit 方法的值来决定最合适的算法。（不同实现方式影响效率）

了解KNN算法的优势和劣势，可以帮助我们在选择学习算法的时候做出更加明智的决定。那我们就来看看KNN算法都有哪些优势以及其缺陷所在！

算法优点

简单易用，相比其他算法，KNN 算是比较简洁明了的算法。即使没有很高的数学基础也能搞清楚它的原理。
模型训练时间快。
预测效果好。
对异常值不敏感

算法缺点

对内存要求较高，因为该算法存储了所有训练数据
预测阶段可能很慢
对不相关的功能和数据规模敏感

1.4 代码实例

1.4.1.KNN电影分类

#1.1构建已经分类好的原始数据集
import pandas as pd
#定义字典
rowdata={'电影名称':['无问西东','后来的我们','前任3','红海行动','唐人街探案','战狼2'],'打斗镜头':[1,5,12,108,112,115],'接吻镜头':[101,89,97,5,9,8],'电影类型':['爱情片','爱情片','爱情片','动作片','动作片','动作片']}
movie_data= pd.DataFrame(rowdata)
# DataFrame函数，DataFrame是Python中Pandas库中的一种数据结构，
# 它类似excel，是一种二维表。DataFrame的单元格可以存放数值、字符串等，
# 这和excel表很像，同时DataFrame可以设置列名columns与行名index。
print(movie_data)
print( )#2.计算已知类别数据集中的点与当前点之间的距离
new_data=[24,67]#打斗和接吻数
dist=list((((movie_data.iloc[:6,1:3]-new_data)**2).sum(1))**0.5)
print('距离列表：')
print(dist)#KNN距离计算公式，相减平方求和后开方
print( )#3.将距离升序排列，然后选取距离最小的k个点
dist_l = pd.DataFrame({'dist': dist, 'labels': (movie_data.iloc[:6, 3])})
print(dist_l)
dr = dist_l.sort_values(by = 'dist')[: 4]#排序函数sort.values()
print(dr)
print( )#4.确定前k个点所在类别的出现频率
re=dr.loc[:,'labels'].value_counts()
print(re)#5.选择频率最高的类别作为当前点的预测类别
result = []
result.append(re.index[0])
print(result)import pandas as pd
"""
函数功能：KNN分类器
参数说明：new_data:需要预测分类的数据集dataSet:已知分类标签的数据集（训练集）k:k-近邻算法参数，选择距离最小的k个点
返回：result：分类结果
"""def  classify0(inX,dataSet,k):result = []dist = list((((dataSet.iloc[:,1:3]-inX)**2).sum(1))**0.5)dist_l = pd.DataFrame({'dist':dist,'labels':(dataSet.iloc[:, 3])})dr = dist_l.sort_values(by = 'dist')[: k]re = dr.loc[:, 'labels'].value_counts()result.append(re.index[0])return resultinX = new_data
dataSet = movie_data
k = 3
print(classify0(inX,dataSet,k))

1.4.2.约会数据预测

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38172	2.687918	0.660836	didntLike
31676	10.037847	0.962245	largeDoses
74038	12.404762	1.112080	didntLike
44738	10.237305	0.633422	largeDoses
17410	4.745392	0.662520	smallDoses
5688	4.639461	1.569431	smallDoses
36642	3.149310	0.639669	didntLike
29956	13.406875	1.639194	largeDoses
60350	6.068668	0.881241	didntLike
23758	9.477022	0.899002	largeDoses
25780	3.897620	0.560201	smallDoses
11342	5.463615	1.203677	smallDoses
36109	3.369267	1.575043	didntLike
14292	5.234562	0.825954	smallDoses
11160	0.000000	0.722170	smallDoses
23762	12.979069	0.504068	largeDoses
39567	5.376564	0.557476	didntLike
25647	13.527910	1.586732	largeDoses
14814	2.196889	0.784587	smallDoses
73590	10.691748	0.007509	didntLike
35187	1.659242	0.447066	didntLike
49459	8.369667	0.656697	largeDoses
31657	13.157197	0.143248	largeDoses
6259	8.199667	0.908508	smallDoses
33101	4.441669	0.439381	largeDoses
27107	9.846492	0.644523	largeDoses
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43536	8.253774	0.748700	largeDoses
67705	6.038620	1.509646	didntLike
35283	6.091587	1.694641	largeDoses
71308	8.986820	1.225165	didntLike
31054	11.508473	1.624296	largeDoses
52387	8.807734	0.713922	largeDoses
40328	0.000000	0.816676	didntLike
34844	8.889202	1.665414	largeDoses
11607	3.178117	0.542752	smallDoses
64306	7.013795	0.139909	didntLike
32721	9.605014	0.065254	largeDoses
33170	1.230540	1.331674	didntLike
37192	10.412811	0.890803	largeDoses
13089	0.000000	0.567161	smallDoses
66491	9.699991	0.122011	didntLike
15941	0.000000	0.061191	smallDoses
4272	4.455293	0.272135	smallDoses
48812	3.020977	1.502803	didntLike
28818	8.099278	0.216317	largeDoses
35394	1.157764	1.603217	didntLike
71791	10.105396	0.121067	didntLike
40668	11.230148	0.408603	largeDoses
39580	9.070058	0.011379	largeDoses
11786	0.566460	0.478837	smallDoses
19251	0.000000	0.487300	smallDoses
56594	8.956369	1.193484	largeDoses
54495	1.523057	0.620528	didntLike
11844	2.749006	0.169855	smallDoses
45465	9.235393	0.188350	largeDoses
31033	10.555573	0.403927	largeDoses
16633	6.956372	1.519308	smallDoses
13887	0.636281	1.273984	smallDoses
52603	3.574737	0.075163	didntLike
72000	9.032486	1.461809	didntLike
68497	5.958993	0.023012	didntLike
35135	2.435300	1.211744	didntLike
26397	10.539731	1.638248	largeDoses
7313	7.646702	0.056513	smallDoses
91273	20.919349	0.644571	didntLike
24743	1.424726	0.838447	didntLike
31690	6.748663	0.890223	largeDoses
15432	2.289167	0.114881	smallDoses
58394	5.548377	0.402238	didntLike
33962	6.057227	0.432666	didntLike
31442	10.828595	0.559955	largeDoses
31044	11.318160	0.271094	largeDoses
29938	13.265311	0.633903	largeDoses
9875	0.000000	1.496715	smallDoses
51542	6.517133	0.402519	largeDoses
11878	4.934374	1.520028	smallDoses
69241	10.151738	0.896433	didntLike
37776	2.425781	1.559467	didntLike
68997	9.778962	1.195498	didntLike
67416	12.219950	0.657677	didntLike
59225	7.394151	0.954434	didntLike
29138	8.518535	0.742546	largeDoses
5962	2.798700	0.662632	smallDoses
10847	0.637930	0.617373	smallDoses
70527	10.750490	0.097415	didntLike
9610	0.625382	0.140969	smallDoses
64734	10.027968	0.282787	didntLike
25941	9.817347	0.364197	largeDoses
2763	0.646828	1.266069	smallDoses
55601	3.347111	0.914294	didntLike
31128	11.816892	0.193798	largeDoses
5181	0.000000	1.480198	smallDoses
69982	10.945666	0.993219	didntLike
52440	10.244706	0.280539	largeDoses
57350	2.579801	1.149172	didntLike
57869	2.630410	0.098869	didntLike
56557	11.746200	1.695517	largeDoses
42342	8.104232	1.326277	largeDoses
15560	12.409743	0.790295	largeDoses
34826	12.167844	1.328086	largeDoses
8569	3.198408	0.299287	smallDoses
77623	16.055513	0.541052	didntLike
78184	7.138659	0.158481	didntLike
7036	4.831041	0.761419	smallDoses
69616	10.082890	1.373611	didntLike
21546	10.066867	0.788470	largeDoses
36715	8.129538	0.329913	largeDoses
20522	3.012463	1.138108	smallDoses
42349	3.720391	0.845974	didntLike
9037	0.773493	1.148256	smallDoses
26728	10.962941	1.037324	largeDoses
587	0.177621	0.162614	smallDoses
48915	3.085853	0.967899	didntLike
9824	8.426781	0.202558	smallDoses
4135	1.825927	1.128347	smallDoses
9666	2.185155	1.010173	smallDoses
59333	7.184595	1.261338	didntLike
36198	0.000000	0.116525	didntLike
34909	8.901752	1.033527	largeDoses
47516	2.451497	1.358795	didntLike
55807	3.213631	0.432044	didntLike
14036	3.974739	0.723929	smallDoses
42856	9.601306	0.619232	largeDoses
64007	8.363897	0.445341	didntLike
59428	6.381484	1.365019	didntLike
13730	0.000000	1.403914	smallDoses
41740	9.609836	1.438105	largeDoses
63546	9.904741	0.985862	didntLike
30417	7.185807	1.489102	largeDoses
69636	5.466703	1.216571	didntLike
64660	0.000000	0.915898	didntLike
14883	4.575443	0.535671	smallDoses
7965	3.277076	1.010868	smallDoses
68620	10.246623	1.239634	didntLike
8738	2.341735	1.060235	smallDoses
7544	3.201046	0.498843	smallDoses
6377	6.066013	0.120927	smallDoses
36842	8.829379	0.895657	largeDoses
81046	15.833048	1.568245	didntLike
67736	13.516711	1.220153	didntLike
32492	0.664284	1.116755	didntLike
39299	6.325139	0.605109	largeDoses
77289	8.677499	0.344373	didntLike
33835	8.188005	0.964896	largeDoses
71890	9.414263	0.384030	didntLike
32054	9.196547	1.138253	largeDoses
38579	10.202968	0.452363	largeDoses
55984	2.119439	1.481661	didntLike
72694	13.635078	0.858314	didntLike
42299	0.083443	0.701669	didntLike
26635	9.149096	1.051446	largeDoses
8579	1.933803	1.374388	smallDoses
37302	14.115544	0.676198	largeDoses
22878	8.933736	0.943352	largeDoses
4364	2.661254	0.946117	smallDoses
4985	0.988432	1.305027	smallDoses
37068	2.063741	1.125946	didntLike
41137	2.220590	0.690754	didntLike
67759	6.424849	0.806641	didntLike
11831	1.156153	1.613674	smallDoses
34502	3.032720	0.601847	didntLike
4088	3.076828	0.952089	smallDoses
15199	0.000000	0.318105	smallDoses
17309	7.750480	0.554015	largeDoses
42816	10.958135	1.482500	largeDoses
43751	10.222018	0.488678	largeDoses
58335	2.367988	0.435741	didntLike
75039	7.686054	1.381455	didntLike
42878	11.464879	1.481589	largeDoses
42770	11.075735	0.089726	largeDoses
8848	3.543989	0.345853	smallDoses
31340	8.123889	1.282880	largeDoses
41413	4.331769	0.754467	largeDoses
12731	0.120865	1.211961	smallDoses
22447	6.116109	0.701523	largeDoses
33564	7.474534	0.505790	largeDoses
48907	8.819454	0.649292	largeDoses
8762	6.802144	0.615284	smallDoses
46696	12.666325	0.931960	largeDoses
36851	8.636180	0.399333	largeDoses
67639	11.730991	1.289833	didntLike
171	8.132449	0.039062	smallDoses
26674	10.296589	1.496144	largeDoses
8739	7.583906	1.005764	smallDoses
66668	9.777806	0.496377	didntLike
68732	8.833546	0.513876	didntLike
69995	4.907899	1.518036	didntLike
82008	8.362736	1.285939	didntLike
25054	9.084726	1.606312	largeDoses
33085	14.164141	0.560970	largeDoses
41379	9.080683	0.989920	largeDoses
39417	6.522767	0.038548	largeDoses
12556	3.690342	0.462281	smallDoses
39432	3.563706	0.242019	didntLike
38010	1.065870	1.141569	didntLike
69306	6.683796	1.456317	didntLike
38000	1.712874	0.243945	didntLike
46321	13.109929	1.280111	largeDoses
66293	11.327910	0.780977	didntLike
22730	4.545711	1.233254	didntLike
5952	3.367889	0.468104	smallDoses
72308	8.326224	0.567347	didntLike
60338	8.978339	1.442034	didntLike
13301	5.655826	1.582159	smallDoses
27884	8.855312	0.570684	largeDoses
11188	6.649568	0.544233	smallDoses
56796	3.966325	0.850410	didntLike
8571	1.924045	1.664782	smallDoses
4914	6.004812	0.280369	smallDoses
10784	0.000000	0.375849	smallDoses
39296	9.923018	0.092192	largeDoses
13113	2.389084	0.119284	smallDoses
70204	13.663189	0.133251	didntLike
46813	11.434976	0.321216	largeDoses
11697	0.358270	1.292858	smallDoses
44183	9.598873	0.223524	largeDoses
2225	6.375275	0.608040	smallDoses
29066	11.580532	0.458401	largeDoses
4245	5.319324	1.598070	smallDoses
34379	4.324031	1.603481	didntLike
44441	2.358370	1.273204	didntLike
2022	0.000000	1.182708	smallDoses
26866	12.824376	0.890411	largeDoses
57070	1.587247	1.456982	didntLike
32932	8.510324	1.520683	largeDoses
51967	10.428884	1.187734	largeDoses
44432	8.346618	0.042318	largeDoses
67066	7.541444	0.809226	didntLike
17262	2.540946	1.583286	smallDoses
79728	9.473047	0.692513	didntLike
14259	0.352284	0.474080	smallDoses
6122	0.000000	0.589826	smallDoses
76879	12.405171	0.567201	didntLike
11426	4.126775	0.871452	smallDoses
2493	0.034087	0.335848	smallDoses
19910	1.177634	0.075106	smallDoses
10939	0.000000	0.479996	smallDoses
17716	0.994909	0.611135	smallDoses
31390	11.053664	1.180117	largeDoses
20375	0.000000	1.679729	smallDoses
26309	2.495011	1.459589	didntLike
33484	11.516831	0.001156	largeDoses
45944	9.213215	0.797743	largeDoses
4249	5.332865	0.109288	smallDoses
6089	0.000000	1.689771	smallDoses
7513	0.000000	1.126053	smallDoses
27862	12.640062	1.690903	largeDoses
39038	2.693142	1.317518	didntLike
19218	3.328969	0.268271	smallDoses
62911	7.193166	1.117456	didntLike
77758	6.615512	1.521012	didntLike
27940	8.000567	0.835341	largeDoses
2194	4.017541	0.512104	smallDoses
37072	13.245859	0.927465	largeDoses
15585	5.970616	0.813624	smallDoses
25577	11.668719	0.886902	largeDoses
8777	4.283237	1.272728	smallDoses
29016	10.742963	0.971401	largeDoses
21910	12.326672	1.592608	largeDoses
12916	0.000000	0.344622	smallDoses
10976	0.000000	0.922846	smallDoses
79065	10.602095	0.573686	didntLike
36759	10.861859	1.155054	largeDoses
50011	1.229094	1.638690	didntLike
1155	0.410392	1.313401	smallDoses
71600	14.552711	0.616162	didntLike
30817	14.178043	0.616313	largeDoses
54559	14.136260	0.362388	didntLike
29764	0.093534	1.207194	didntLike
69100	10.929021	0.403110	didntLike
47324	11.432919	0.825959	largeDoses
73199	9.134527	0.586846	didntLike
44461	5.071432	1.421420	didntLike
45617	11.460254	1.541749	largeDoses
28221	11.620039	1.103553	largeDoses
7091	4.022079	0.207307	smallDoses
6110	3.057842	1.631262	smallDoses
79016	7.782169	0.404385	didntLike
18289	7.981741	0.929789	largeDoses
43679	4.601363	0.268326	didntLike
22075	2.595564	1.115375	didntLike
23535	10.049077	0.391045	largeDoses
25301	3.265444	1.572970	smallDoses
32256	11.780282	1.511014	largeDoses
36951	3.075975	0.286284	didntLike
31290	1.795307	0.194343	didntLike
38953	11.106979	0.202415	largeDoses
35257	5.994413	0.800021	didntLike
25847	9.706062	1.012182	largeDoses
32680	10.582992	0.836025	largeDoses
62018	7.038266	1.458979	didntLike
9074	0.023771	0.015314	smallDoses
33004	12.823982	0.676371	largeDoses
44588	3.617770	0.493483	didntLike
32565	8.346684	0.253317	largeDoses
38563	6.104317	0.099207	didntLike
75668	16.207776	0.584973	didntLike
9069	6.401969	1.691873	smallDoses
53395	2.298696	0.559757	didntLike
28631	7.661515	0.055981	largeDoses
71036	6.353608	1.645301	didntLike
71142	10.442780	0.335870	didntLike
37653	3.834509	1.346121	didntLike
76839	10.998587	0.584555	didntLike
9916	2.695935	1.512111	smallDoses
38889	3.356646	0.324230	didntLike
39075	14.677836	0.793183	largeDoses
48071	1.551934	0.130902	didntLike
7275	2.464739	0.223502	smallDoses
41804	1.533216	1.007481	didntLike
35665	12.473921	0.162910	largeDoses
67956	6.491596	0.032576	didntLike
41892	10.506276	1.510747	largeDoses
38844	4.380388	0.748506	didntLike
74197	13.670988	1.687944	didntLike
14201	8.317599	0.390409	smallDoses
3908	0.000000	0.556245	smallDoses
2459	0.000000	0.290218	smallDoses
32027	10.095799	1.188148	largeDoses
12870	0.860695	1.482632	smallDoses
9880	1.557564	0.711278	smallDoses
72784	10.072779	0.756030	didntLike
17521	0.000000	0.431468	smallDoses
50283	7.140817	0.883813	largeDoses
33536	11.384548	1.438307	largeDoses
9452	3.214568	1.083536	smallDoses
37457	11.720655	0.301636	largeDoses
17724	6.374475	1.475925	largeDoses
43869	5.749684	0.198875	largeDoses
264	3.871808	0.552602	smallDoses
25736	8.336309	0.636238	largeDoses
39584	9.710442	1.503735	largeDoses
31246	1.532611	1.433898	didntLike
49567	9.785785	0.984614	largeDoses
7052	2.633627	1.097866	smallDoses
35493	9.238935	0.494701	largeDoses
10986	1.205656	1.398803	smallDoses
49508	3.124909	1.670121	didntLike
5734	7.935489	1.585044	smallDoses
65479	12.746636	1.560352	didntLike
77268	10.732563	0.545321	didntLike
28490	3.977403	0.766103	didntLike
13546	4.194426	0.450663	smallDoses
37166	9.610286	0.142912	largeDoses
16381	4.797555	1.260455	smallDoses
10848	1.615279	0.093002	smallDoses
35405	4.614771	1.027105	didntLike
15917	0.000000	1.369726	smallDoses
6131	0.608457	0.512220	smallDoses
67432	6.558239	0.667579	didntLike
30354	12.315116	0.197068	largeDoses
69696	7.014973	1.494616	didntLike
33481	8.822304	1.194177	largeDoses
43075	10.086796	0.570455	largeDoses
38343	7.241614	1.661627	largeDoses
14318	4.602395	1.511768	smallDoses
5367	7.434921	0.079792	smallDoses
37894	10.467570	1.595418	largeDoses
36172	9.948127	0.003663	largeDoses
40123	2.478529	1.568987	didntLike
10976	5.938545	0.878540	smallDoses
12705	0.000000	0.948004	smallDoses
12495	5.559181	1.357926	smallDoses
35681	9.776654	0.535966	largeDoses
46202	3.092056	0.490906	didntLike
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22834	4.459495	0.538867	didntLike
49901	8.334306	1.646600	largeDoses
71932	11.226654	0.384686	didntLike
13279	3.904737	1.597294	smallDoses
49112	7.038205	1.211329	largeDoses
77129	9.836120	1.054340	didntLike
37447	1.990976	0.378081	didntLike
62397	9.005302	0.485385	didntLike
0	1.772510	1.039873	smallDoses
15476	0.458674	0.819560	smallDoses
40625	10.003919	0.231658	largeDoses
36706	0.520807	1.476008	didntLike
28580	10.678214	1.431837	largeDoses
25862	4.425992	1.363842	didntLike
63488	12.035355	0.831222	didntLike
33944	10.606732	1.253858	largeDoses
30099	1.568653	0.684264	didntLike
13725	2.545434	0.024271	smallDoses
36768	10.264062	0.982593	largeDoses
64656	9.866276	0.685218	didntLike
14927	0.142704	0.057455	smallDoses
43231	9.853270	1.521432	largeDoses
66087	6.596604	1.653574	didntLike
19806	2.602287	1.321481	smallDoses
41081	10.411776	0.664168	largeDoses
10277	7.083449	0.622589	smallDoses
7014	2.080068	1.254441	smallDoses
17275	0.522844	1.622458	smallDoses
31600	10.362000	1.544827	largeDoses
59956	3.412967	1.035410	didntLike
42181	6.796548	1.112153	largeDoses
51743	4.092035	0.075804	didntLike
5194	2.763811	1.564325	smallDoses
30832	12.547439	1.402443	largeDoses
7976	5.708052	1.596152	smallDoses
14602	4.558025	0.375806	smallDoses
41571	11.642307	0.438553	largeDoses
55028	3.222443	0.121399	didntLike
5837	4.736156	0.029871	smallDoses
39808	10.839526	0.836323	largeDoses
20944	4.194791	0.235483	smallDoses
22146	14.936259	0.888582	largeDoses
42169	3.310699	1.521855	didntLike
7010	2.971931	0.034321	smallDoses
3807	9.261667	0.537807	smallDoses
29241	7.791833	1.111416	largeDoses
52696	1.480470	1.028750	didntLike
42545	3.677287	0.244167	didntLike
24437	2.202967	1.370399	didntLike
16037	5.796735	0.935893	smallDoses
8493	3.063333	0.144089	smallDoses
68080	11.233094	0.492487	didntLike
59016	1.965570	0.005697	didntLike
11810	8.616719	0.137419	smallDoses
68630	6.609989	1.083505	didntLike
7629	1.712639	1.086297	smallDoses
71992	10.117445	1.299319	didntLike
13398	0.000000	1.104178	smallDoses
26241	9.824777	1.346821	largeDoses
11160	1.653089	0.980949	smallDoses
76701	18.178822	1.473671	didntLike
32174	6.781126	0.885340	largeDoses
45043	8.206750	1.549223	largeDoses
42173	10.081853	1.376745	largeDoses
69801	6.288742	0.112799	didntLike
41737	3.695937	1.543589	didntLike
46979	6.726151	1.069380	largeDoses
79267	12.969999	1.568223	didntLike
4615	2.661390	1.531933	smallDoses
32907	7.072764	1.117386	largeDoses
37444	9.123366	1.318988	largeDoses
569	3.743946	1.039546	smallDoses
8723	2.341300	0.219361	smallDoses
6024	0.541913	0.592348	smallDoses
52252	2.310828	1.436753	didntLike
8358	6.226597	1.427316	smallDoses
26166	7.277876	0.489252	largeDoses
18471	0.000000	0.389459	smallDoses
3386	7.218221	1.098828	smallDoses
41544	8.777129	1.111464	largeDoses
10480	2.813428	0.819419	smallDoses
5894	2.268766	1.412130	smallDoses
7273	6.283627	0.571292	smallDoses
22272	7.520081	1.626868	largeDoses
31369	11.739225	0.027138	largeDoses
10708	3.746883	0.877350	smallDoses
69364	12.089835	0.521631	didntLike
37760	12.310404	0.259339	largeDoses
13004	0.000000	0.671355	smallDoses
37885	2.728800	0.331502	didntLike
52555	10.814342	0.607652	largeDoses
38997	12.170268	0.844205	largeDoses
69698	6.698371	0.240084	didntLike
11783	3.632672	1.643479	smallDoses
47636	10.059991	0.892361	largeDoses
15744	1.887674	0.756162	smallDoses
69058	8.229125	0.195886	didntLike
33057	7.817082	0.476102	largeDoses
28681	12.277230	0.076805	largeDoses
34042	10.055337	1.115778	largeDoses
29928	3.596002	1.485952	didntLike
9734	2.755530	1.420655	smallDoses
7344	7.780991	0.513048	smallDoses
7387	0.093705	0.391834	smallDoses
33957	8.481567	0.520078	largeDoses
9936	3.865584	0.110062	smallDoses
36094	9.683709	0.779984	largeDoses
39835	10.617255	1.359970	largeDoses
64486	7.203216	1.624762	didntLike
0	7.601414	1.215605	smallDoses
39539	1.386107	1.417070	didntLike
66972	9.129253	0.594089	didntLike
15029	1.363447	0.620841	smallDoses
44909	3.181399	0.359329	didntLike
38183	13.365414	0.217011	largeDoses
37372	4.207717	1.289767	didntLike
0	4.088395	0.870075	smallDoses
17786	3.327371	1.142505	smallDoses
39055	1.303323	1.235650	didntLike
37045	7.999279	1.581763	largeDoses
6435	2.217488	0.864536	smallDoses
72265	7.751808	0.192451	didntLike
28152	14.149305	1.591532	largeDoses
25931	8.765721	0.152808	largeDoses
7538	3.408996	0.184896	smallDoses
1315	1.251021	0.112340	smallDoses
12292	6.160619	1.537165	smallDoses
49248	1.034538	1.585162	didntLike
9025	0.000000	1.034635	smallDoses
13438	2.355051	0.542603	smallDoses
69683	6.614543	0.153771	didntLike
25374	10.245062	1.450903	largeDoses
55264	3.467074	1.231019	didntLike
38324	7.487678	1.572293	largeDoses
69643	4.624115	1.185192	didntLike
44058	8.995957	1.436479	largeDoses
41316	11.564476	0.007195	largeDoses
29119	3.440948	0.078331	didntLike
51656	1.673603	0.732746	didntLike
3030	4.719341	0.699755	smallDoses
35695	10.304798	1.576488	largeDoses
1537	2.086915	1.199312	smallDoses
9083	6.338220	1.131305	smallDoses
47744	8.254926	0.710694	largeDoses
71372	16.067108	0.974142	didntLike
37980	1.723201	0.310488	didntLike
42385	3.785045	0.876904	didntLike
22687	2.557561	0.123738	didntLike
39512	9.852220	1.095171	largeDoses
11885	3.679147	1.557205	smallDoses
4944	9.789681	0.852971	smallDoses
73230	14.958998	0.526707	didntLike
17585	11.182148	1.288459	largeDoses
68737	7.528533	1.657487	didntLike
13818	5.253802	1.378603	smallDoses
31662	13.946752	1.426657	largeDoses
86686	15.557263	1.430029	didntLike
43214	12.483550	0.688513	largeDoses
24091	2.317302	1.411137	didntLike
52544	10.069724	0.766119	largeDoses
61861	5.792231	1.615483	didntLike
47903	4.138435	0.475994	didntLike
37190	12.929517	0.304378	largeDoses
6013	9.378238	0.307392	smallDoses
27223	8.361362	1.643204	largeDoses
69027	7.939406	1.325042	didntLike
78642	10.735384	0.705788	didntLike
30254	11.592723	0.286188	largeDoses
21704	10.098356	0.704748	largeDoses
34985	9.299025	0.545337	largeDoses
31316	11.158297	0.218067	largeDoses
76368	16.143900	0.558388	didntLike
27953	10.971700	1.221787	largeDoses
152	0.000000	0.681478	smallDoses
9146	3.178961	1.292692	smallDoses
75346	17.625350	0.339926	didntLike
26376	1.995833	0.267826	didntLike
35255	10.640467	0.416181	largeDoses
19198	9.628339	0.985462	largeDoses
12518	4.662664	0.495403	smallDoses
25453	5.754047	1.382742	smallDoses
12530	0.000000	0.037146	smallDoses
62230	9.334332	0.198118	didntLike
9517	3.846162	0.619968	smallDoses
71161	10.685084	0.678179	didntLike
1593	4.752134	0.359205	smallDoses
33794	0.697630	0.966786	didntLike
39710	10.365836	0.505898	largeDoses
16941	0.461478	0.352865	smallDoses
69209	11.339537	1.068740	didntLike
4446	5.420280	0.127310	smallDoses
9347	3.469955	1.619947	smallDoses
55635	8.517067	0.994858	largeDoses
65889	8.306512	0.413690	didntLike
10753	2.628690	0.444320	smallDoses
7055	0.000000	0.802985	smallDoses
7905	0.000000	1.170397	smallDoses
53447	7.298767	1.582346	largeDoses
9194	7.331319	1.277988	smallDoses
61914	9.392269	0.151617	didntLike
15630	5.541201	1.180596	smallDoses
79194	15.149460	0.537540	didntLike
12268	5.515189	0.250562	smallDoses
33682	7.728898	0.920494	largeDoses
26080	11.318785	1.510979	largeDoses
19119	3.574709	1.531514	smallDoses
30902	7.350965	0.026332	largeDoses
63039	7.122363	1.630177	didntLike
51136	1.828412	1.013702	didntLike
35262	10.117989	1.156862	largeDoses
42776	11.309897	0.086291	largeDoses
64191	8.342034	1.388569	didntLike
15436	0.241714	0.715577	smallDoses
14402	10.482619	1.694972	smallDoses
6341	9.289510	1.428879	smallDoses
14113	4.269419	0.134181	smallDoses
6390	0.000000	0.189456	smallDoses
8794	0.817119	0.143668	smallDoses
43432	1.508394	0.652651	didntLike
38334	9.359918	0.052262	largeDoses
34068	10.052333	0.550423	largeDoses
30819	11.111660	0.989159	largeDoses
22239	11.265971	0.724054	largeDoses
28725	10.383830	0.254836	largeDoses
57071	3.878569	1.377983	didntLike
72420	13.679237	0.025346	didntLike
28294	10.526846	0.781569	largeDoses
9896	0.000000	0.924198	smallDoses
65821	4.106727	1.085669	didntLike
7645	8.118856	1.470686	smallDoses
71289	7.796874	0.052336	didntLike
5128	2.789669	1.093070	smallDoses
13711	6.226962	0.287251	smallDoses
22240	10.169548	1.660104	largeDoses
15092	0.000000	1.370549	smallDoses
5017	7.513353	0.137348	smallDoses
10141	8.240793	0.099735	smallDoses
35570	14.612797	1.247390	largeDoses
46893	3.562976	0.445386	didntLike
8178	3.230482	1.331698	smallDoses
55783	3.612548	1.551911	didntLike
1148	0.000000	0.332365	smallDoses
10062	3.931299	0.487577	smallDoses
74124	14.752342	1.155160	didntLike
66603	10.261887	1.628085	didntLike
11893	2.787266	1.570402	smallDoses
50908	15.112319	1.324132	largeDoses
39891	5.184553	0.223382	largeDoses
65915	3.868359	0.128078	didntLike
65678	3.507965	0.028904	didntLike
62996	11.019254	0.427554	didntLike
36851	3.812387	0.655245	didntLike
36669	11.056784	0.378725	largeDoses
38876	8.826880	1.002328	largeDoses
26878	11.173861	1.478244	largeDoses
46246	11.506465	0.421993	largeDoses
12761	7.798138	0.147917	largeDoses
35282	10.155081	1.370039	largeDoses
68306	10.645275	0.693453	didntLike
31262	9.663200	1.521541	largeDoses
34754	10.790404	1.312679	largeDoses
13408	2.810534	0.219962	smallDoses
30365	9.825999	1.388500	largeDoses
10709	1.421316	0.677603	smallDoses
24332	11.123219	0.809107	largeDoses
45517	13.402206	0.661524	largeDoses
6178	1.212255	0.836807	smallDoses
10639	1.568446	1.297469	smallDoses
29613	3.343473	1.312266	didntLike
22392	5.400155	0.193494	didntLike
51126	3.818754	0.590905	didntLike
53644	7.973845	0.307364	largeDoses
51417	9.078824	0.734876	largeDoses
24859	0.153467	0.766619	didntLike
61732	8.325167	0.028479	didntLike
71128	7.092089	1.216733	didntLike
27276	5.192485	1.094409	largeDoses
30453	10.340791	1.087721	largeDoses
18670	2.077169	1.019775	smallDoses
70600	10.151966	0.993105	didntLike
12683	0.046826	0.809614	smallDoses
81597	11.221874	1.395015	didntLike
69959	14.497963	1.019254	didntLike
8124	3.554508	0.533462	smallDoses
18867	3.522673	0.086725	smallDoses
80886	14.531655	0.380172	didntLike
55895	3.027528	0.885457	didntLike
31587	1.845967	0.488985	didntLike
10591	10.226164	0.804403	largeDoses
70096	10.965926	1.212328	didntLike
53151	2.129921	1.477378	didntLike
11992	0.000000	1.606849	smallDoses
33114	9.489005	0.827814	largeDoses
7413	0.000000	1.020797	smallDoses
10583	0.000000	1.270167	smallDoses
58668	6.556676	0.055183	didntLike
35018	9.959588	0.060020	largeDoses
70843	7.436056	1.479856	didntLike
14011	0.404888	0.459517	smallDoses
35015	9.952942	1.650279	largeDoses
70839	15.600252	0.021935	didntLike
3024	2.723846	0.387455	smallDoses
5526	0.513866	1.323448	smallDoses
5113	0.000000	0.861859	smallDoses
20851	7.280602	1.438470	smallDoses
40999	9.161978	1.110180	largeDoses
15823	0.991725	0.730979	smallDoses
35432	7.398380	0.684218	largeDoses
53711	12.149747	1.389088	largeDoses
64371	9.149678	0.874905	didntLike
9289	9.666576	1.370330	smallDoses
60613	3.620110	0.287767	didntLike
18338	5.238800	1.253646	smallDoses
22845	14.715782	1.503758	largeDoses
74676	14.445740	1.211160	didntLike
34143	13.609528	0.364240	largeDoses
14153	3.141585	0.424280	smallDoses
9327	0.000000	0.120947	smallDoses
18991	0.454750	1.033280	smallDoses
9193	0.510310	0.016395	smallDoses
2285	3.864171	0.616349	smallDoses
9493	6.724021	0.563044	smallDoses
2371	4.289375	0.012563	smallDoses
13963	0.000000	1.437030	smallDoses
2299	3.733617	0.698269	smallDoses
5262	2.002589	1.380184	smallDoses
4659	2.502627	0.184223	smallDoses
17582	6.382129	0.876581	smallDoses
27750	8.546741	0.128706	largeDoses
9868	2.694977	0.432818	smallDoses
18333	3.951256	0.333300	smallDoses
3780	9.856183	0.329181	smallDoses
18190	2.068962	0.429927	smallDoses
11145	3.410627	0.631838	smallDoses
68846	9.974715	0.669787	didntLike
26575	10.650102	0.866627	largeDoses
48111	9.134528	0.728045	largeDoses
43757	7.882601	1.332446	largeDoses

datingTestSet2.txt ：

40920	8.326976	0.953952	3
14488	7.153469	1.673904	2
26052	1.441871	0.805124	1
75136	13.147394	0.428964	1
38344	1.669788	0.134296	1
72993	10.141740	1.032955	1
35948	6.830792	1.213192	3
42666	13.276369	0.543880	3
67497	8.631577	0.749278	1
35483	12.273169	1.508053	3
50242	3.723498	0.831917	1
63275	8.385879	1.669485	1
5569	4.875435	0.728658	2
51052	4.680098	0.625224	1
77372	15.299570	0.331351	1
43673	1.889461	0.191283	1
61364	7.516754	1.269164	1
69673	14.239195	0.261333	1
15669	0.000000	1.250185	2
28488	10.528555	1.304844	3
6487	3.540265	0.822483	2
37708	2.991551	0.833920	1
22620	5.297865	0.638306	2
28782	6.593803	0.187108	3
19739	2.816760	1.686209	2
36788	12.458258	0.649617	3
5741	0.000000	1.656418	2
28567	9.968648	0.731232	3
6808	1.364838	0.640103	2
41611	0.230453	1.151996	1
36661	11.865402	0.882810	3
43605	0.120460	1.352013	1
15360	8.545204	1.340429	3
63796	5.856649	0.160006	1
10743	9.665618	0.778626	2
70808	9.778763	1.084103	1
72011	4.932976	0.632026	1
5914	2.216246	0.587095	2
14851	14.305636	0.632317	3
33553	12.591889	0.686581	3
44952	3.424649	1.004504	1
17934	0.000000	0.147573	2
27738	8.533823	0.205324	3
29290	9.829528	0.238620	3
42330	11.492186	0.263499	3
36429	3.570968	0.832254	1
39623	1.771228	0.207612	1
32404	3.513921	0.991854	1
27268	4.398172	0.975024	1
5477	4.276823	1.174874	2
14254	5.946014	1.614244	2
68613	13.798970	0.724375	1
41539	10.393591	1.663724	3
7917	3.007577	0.297302	2
21331	1.031938	0.486174	2
8338	4.751212	0.064693	2
5176	3.692269	1.655113	2
18983	10.448091	0.267652	3
68837	10.585786	0.329557	1
13438	1.604501	0.069064	2
48849	3.679497	0.961466	1
12285	3.795146	0.696694	2
7826	2.531885	1.659173	2
5565	9.733340	0.977746	2
10346	6.093067	1.413798	2
1823	7.712960	1.054927	2
9744	11.470364	0.760461	3
16857	2.886529	0.934416	2
39336	10.054373	1.138351	3
65230	9.972470	0.881876	1
2463	2.335785	1.366145	2
27353	11.375155	1.528626	3
16191	0.000000	0.605619	2
12258	4.126787	0.357501	2
42377	6.319522	1.058602	1
25607	8.680527	0.086955	3
77450	14.856391	1.129823	1
58732	2.454285	0.222380	1
46426	7.292202	0.548607	3
32688	8.745137	0.857348	3
64890	8.579001	0.683048	1
8554	2.507302	0.869177	2
28861	11.415476	1.505466	3
42050	4.838540	1.680892	1
32193	10.339507	0.583646	3
64895	6.573742	1.151433	1
2355	6.539397	0.462065	2
0	2.209159	0.723567	2
70406	11.196378	0.836326	1
57399	4.229595	0.128253	1
41732	9.505944	0.005273	3
11429	8.652725	1.348934	3
75270	17.101108	0.490712	1
5459	7.871839	0.717662	2
73520	8.262131	1.361646	1
40279	9.015635	1.658555	3
21540	9.215351	0.806762	3
17694	6.375007	0.033678	2
22329	2.262014	1.022169	1
46570	5.677110	0.709469	1
42403	11.293017	0.207976	3
33654	6.590043	1.353117	1
9171	4.711960	0.194167	2
28122	8.768099	1.108041	3
34095	11.502519	0.545097	3
1774	4.682812	0.578112	2
40131	12.446578	0.300754	3
13994	12.908384	1.657722	3
77064	12.601108	0.974527	1
11210	3.929456	0.025466	2
6122	9.751503	1.182050	3
15341	3.043767	0.888168	2
44373	4.391522	0.807100	1
28454	11.695276	0.679015	3
63771	7.879742	0.154263	1
9217	5.613163	0.933632	2
69076	9.140172	0.851300	1
24489	4.258644	0.206892	1
16871	6.799831	1.221171	2
39776	8.752758	0.484418	3
5901	1.123033	1.180352	2
40987	10.833248	1.585426	3
7479	3.051618	0.026781	2
38768	5.308409	0.030683	3
4933	1.841792	0.028099	2
32311	2.261978	1.605603	1
26501	11.573696	1.061347	3
37433	8.038764	1.083910	3
23503	10.734007	0.103715	3
68607	9.661909	0.350772	1
27742	9.005850	0.548737	3
11303	0.000000	0.539131	2
0	5.757140	1.062373	2
32729	9.164656	1.624565	3
24619	1.318340	1.436243	1
42414	14.075597	0.695934	3
20210	10.107550	1.308398	3
33225	7.960293	1.219760	3
54483	6.317292	0.018209	1
18475	12.664194	0.595653	3
33926	2.906644	0.581657	1
43865	2.388241	0.913938	1
26547	6.024471	0.486215	3
44404	7.226764	1.255329	3
16674	4.183997	1.275290	2
8123	11.850211	1.096981	3
42747	11.661797	1.167935	3
56054	3.574967	0.494666	1
10933	0.000000	0.107475	2
18121	7.937657	0.904799	3
11272	3.365027	1.014085	2
16297	0.000000	0.367491	2
28168	13.860672	1.293270	3
40963	10.306714	1.211594	3
31685	7.228002	0.670670	3
55164	4.508740	1.036192	1
17595	0.366328	0.163652	2
1862	3.299444	0.575152	2
57087	0.573287	0.607915	1
63082	9.183738	0.012280	1
51213	7.842646	1.060636	3
6487	4.750964	0.558240	2
4805	11.438702	1.556334	3
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69696	7.014973	1.494616	1
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43075	10.086796	0.570455	3
38343	7.241614	1.661627	3
14318	4.602395	1.511768	2
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152	0.000000	0.681478	2
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43757	7.882601	1.332446	3

import pandas as pdimport matplotlib as mplimport
import matplotlib.pyplot as plt#1、准备数据
datingTest = pd.read_table('C:\pythonProject\机器学习\有监督学习\KNN约会数据预测\datingTestSet2.txt',header=None)
datingTest.columns=['airmiles','gametime','icecream','like']  #设置每一列的名字“飞行里程”、“游戏时间”、“冰淇淋”、“喜欢”
datingTest.head() #列名和前5个样本
datingTest.shape#显示（行，列）
datingTest.info()#显示 DataFrame 的简要摘要，包括每列的非空值数量、数据类型以及内存使用情况。#2.数据分析
# #把不同标签用颜色区分
"""
创建一个空列表 Colors，用于存储每个样本的颜色。
遍历 datingTest DataFrame 的每一行。
使用 iloc 获取每行的最后一个元素（即标签）。
根据 like 标签的值，为每个样本分配不同的颜色。
"""
Colors = []
for i in range(datingTest.shape[0]):m = datingTest.iloc[i,-1]if m=='didntLike':Colors.append('black')if m=='smallDoses':Colors.append('orange')if m=='largeDoses':Colors.append('red')# #绘制两两特征之间的散点图
plt.rcParams['font.sans-serif']=['Simhei']   #图中字体设置为黑体
pl=plt.figure(figsize=(12,8))
fig1=pl.add_subplot(221)
plt.scatter(datingTest.iloc[:,1],datingTest.iloc[:,2],marker='.',c=Colors)
plt.xlabel('玩游戏视频所占时间比')
plt.ylabel('每周消费冰淇淋公升数')
#在图形中添加一个子图，位置为 2x2 网格中的第一个。
#使用 scatter 方法绘制第一个散点图，x 轴为“玩游戏视频所占时间比”，y 轴为“每周消费冰淇淋公升数”，并使用之前分配的颜色。fig2=pl.add_subplot(222)
plt.scatter(datingTest.iloc[:,0],datingTest.iloc[:,1],marker='.',c=Colors)
plt.xlabel('每年飞行常客里程')
plt.ylabel('玩游戏视频所占时间比')fig3=pl.add_subplot(223)
plt.scatter(datingTest.iloc[:,0],datingTest.iloc[:,2],marker='.',c=Colors)
plt.xlabel('每年飞行常客里程')
plt.ylabel('每周消费冰淇淋公升数')
plt.show()
import seaborn as sns
sns.set_style("whitegrid")
# 设置颜色主题
antV = ['#1890FF', '#2FC25B', '#FACC14', '#223273', '#8543E0', '#13C2C2', '#3436c7', '#F04864']
g = sns.pairplot(data=datingTest, palette=antV, hue= 'like') #交会图#3数据归一化
def minmax(dataSet):minDf = dataSet.min()maxDf = dataSet.max()normSet = (dataSet - minDf )/(maxDf - minDf)return normSet
datingT = pd.concat([minmax(datingTest.iloc[:, :3]), datingTest.iloc[:,3]], axis=1) #axis=1为按行对齐
#前三列归一化后，和第四列喜欢程度合并成新的表格
datingT.head()#4，划分训练集和测试集"""
函数功能：切分训练集和测试集
参数说明：dataSet:原始数据集rate:训练集所占比例
返回：切分好的训练集和测试集
"""def randSplit(dataSet,rate=0.9):n = dataSet.shape[0]#在 Python 的 pandas 库中，DataFrame 的 shape 属性是一个元组，包含两组数据：第一组是行数，第二组是列数。#因此，dataSet.shape[0] 就是获取这个元组的第一个元素，即 dataSet 的行数。m = int(n*rate)train = dataSet.iloc[:m,:]test = dataSet.iloc[m:,:]test.index = range(test.shape[0])#重置索引为0-行数return train,test
train,test = randSplit(datingT)
train.info()#5.分类器针对于约会网站的测试代码"""
函数功能：k-近邻算法分类器
参数说明：train:训练集test：测试集k:k-近邻参数，即选择距离最小的k个点
返回：预测好分类的测试集
"""
import numpy as npdef datingClass(train, test, k):n = train.shape[1] - 1  # shape属性返回一个二元组，这里为列数-1m = test.shape[0]  # 行数result = []for i in range(m):  # 对每个测试样本dist = list((((train.iloc[:, :n] - test.iloc[i, :n]) ** 2).sum(1)) ** 0.5)  # 对训练集中每个样本和当前测试样本求距离# 对于每个测试样本，计算其与训练集中每个样本的欧几里得距离。# 这里使用了 ** 运算符来计算平方，然后使用 .sum(1) 来计算每列的平方和，最后使用 **0.5 来计算距离。dist_l = pd.DataFrame({'dist': dist, 'labels': (train.iloc[:, n])})  # 每个样本生成一个距离+label的dataframe，行数为训练样本数dr = dist_l.sort_values(by='dist')[: k]# 对距离 DataFrame 按照距离进行排序，并选择前 k 个距离最小的样本。re = dr.loc[:, 'labels'].value_counts()  # 返回Series类型（一维数组）# 计算排序后的 DataFrame 中标签的计数，并返回一个 Series 类型的对象，其中索引是标签，值是对应的计数。result.append(re.index[0])# 将每个测试样本的预测类别（即计数最多的标签）添加到 result 列表中。result = pd.Series(result)test_T = pd.DataFrame(np.hstack((test.values, result.values.reshape((90, 1)))))# 首先将测试集 test 的值转换为数组，然后使用 np.hstack 将预测结果（作为一个一维数组）与测试集的值合并，创建一个新的 DataFrame test_T。# 由于 result 是一个 Series，其值被转换为一个一维数组，并使用 reshape 函数将其调整为与测试集相同的形状（即 90x1 的矩阵）。acc = (test_T.iloc[:, -1] == test_T.iloc[:, -2]).mean()# 这里，test_T.iloc[:,-1] 表示最后一列，即预测类别，而 test_T.iloc[:,-2] 表示倒数第二列，即实际的类别。# 通过比较这两个列，我们可以得到一个布尔型的 Series，其中 True 表示预测正确，False 表示预测错误。使用 .mean() 函数计算这个 Series 中 True 值的平均数，即分类的准确性。# print(f'模型预测准确率为{acc}')return test_T, accimport numpy as np# train_X = train.iloc[:,:-1].values
# train_Y = train.iloc[:,-1].values
def cross_validation(cv, k):num_validation_samples = len(train) // cv# 计算每个折叠中的验证样本数，这等于训练集 train 的总长度除以折叠数 cv。validation_scores = []for fold in range(1, cv - 1):validation_data = train.iloc[num_validation_samples * fold:num_validation_samples * (fold + 1)]# 提取当前折叠的验证数据。training_data = pd.concat((train.iloc[:num_validation_samples * fold], train.iloc[num_validation_samples * (fold + 1):]), axis=0)# 提取剩余的训练数据，并将它们合并为一个 DataFrame。test, acc = datingClass(training_data, validation_data, k)validation_scores.append(acc)# 将当前折叠的准确性得分添加到 validation_scores 列表中。# print(validation_scores)validation_score = np.average(validation_scores)# 使用 numpy 的 average 函数计算所有折叠的准确性得分的平均值，并将其存储在变量 validation_score 中。return validation_scorek_choices = 25import matplotlib.pyplot as pltklist = []
vlist = []
for k in range(3, k_choices):validation_score = cross_validation(10, k)print('k = %d,accuracy = %f', (k, validation_score))klist.append(k)vlist.append(validation_score)
plt.plot(klist, vlist)