KNN最邻近分类算法

参考https://blog.csdn.net/pengjunlee/article/details/82713047，讲解的较为详细

 

1、数据准备

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

30302	8.243063	1.122768	3

68680	7.949017	0.271865	1

17591	7.875477	0.227085	2

74391	9.569087	0.364856	1

37217	7.750103	0.869094	3

42814	0.000000	1.515293	1

14738	3.396030	0.633977	2

19896	11.916091	0.025294	3

14673	0.460758	0.689586	2

32011	13.087566	0.476002	3

58736	4.589016	1.672600	1

54744	8.397217	1.534103	1

29482	5.562772	1.689388	1

27698	10.905159	0.619091	3

11443	1.311441	1.169887	2

56117	10.647170	0.980141	3

39514	0.000000	0.481918	1

26627	8.503025	0.830861	3

16525	0.436880	1.395314	2

24368	6.127867	1.102179	1

22160	12.112492	0.359680	3

6030	1.264968	1.141582	2

6468	6.067568	1.327047	2

22945	8.010964	1.681648	3

18520	3.791084	0.304072	2

34914	11.773195	1.262621	3

6121	8.339588	1.443357	2

38063	2.563092	1.464013	1

23410	5.954216	0.953782	1

35073	9.288374	0.767318	3

52914	3.976796	1.043109	1

16801	8.585227	1.455708	3

9533	1.271946	0.796506	2

16721	0.000000	0.242778	2

5832	0.000000	0.089749	2

44591	11.521298	0.300860	3

10143	1.139447	0.415373	2

21609	5.699090	1.391892	2

23817	2.449378	1.322560	1

15640	0.000000	1.228380	2

8847	3.168365	0.053993	2

50939	10.428610	1.126257	3

28521	2.943070	1.446816	1

32901	10.441348	0.975283	3

42850	12.478764	1.628726	3

13499	5.856902	0.363883	2

40345	2.476420	0.096075	1

43547	1.826637	0.811457	1

70758	4.324451	0.328235	1

19780	1.376085	1.178359	2

44484	5.342462	0.394527	1

54462	11.835521	0.693301	3

20085	12.423687	1.424264	3

42291	12.161273	0.071131	3

47550	8.148360	1.649194	3

11938	1.531067	1.549756	2

40699	3.200912	0.309679	1

70908	8.862691	0.530506	1

73989	6.370551	0.369350	1

11872	2.468841	0.145060	2

48463	11.054212	0.141508	3

15987	2.037080	0.715243	2

70036	13.364030	0.549972	1

32967	10.249135	0.192735	3

63249	10.464252	1.669767	1

42795	9.424574	0.013725	3

14459	4.458902	0.268444	2

19973	0.000000	0.575976	2

5494	9.686082	1.029808	3

67902	13.649402	1.052618	1

25621	13.181148	0.273014	3

27545	3.877472	0.401600	1

58656	1.413952	0.451380	1

7327	4.248986	1.430249	2

64555	8.779183	0.845947	1

8998	4.156252	0.097109	2

11752	5.580018	0.158401	2

76319	15.040440	1.366898	1

27665	12.793870	1.307323	3

67417	3.254877	0.669546	1

21808	10.725607	0.588588	3

15326	8.256473	0.765891	2

20057	8.033892	1.618562	3

79341	10.702532	0.204792	1

15636	5.062996	1.132555	2

35602	10.772286	0.668721	3

28544	1.892354	0.837028	1

57663	1.019966	0.372320	1

78727	15.546043	0.729742	1

68255	11.638205	0.409125	1

14964	3.427886	0.975616	2

21835	11.246174	1.475586	3

7487	0.000000	0.645045	2

8700	0.000000	1.424017	2

26226	8.242553	0.279069	3

65899	8.700060	0.101807	1

6543	0.812344	0.260334	2

46556	2.448235	1.176829	1

71038	13.230078	0.616147	1

47657	0.236133	0.340840	1

19600	11.155826	0.335131	3

37422	11.029636	0.505769	3

1363	2.901181	1.646633	2

26535	3.924594	1.143120	1

47707	2.524806	1.292848	1

38055	3.527474	1.449158	1

6286	3.384281	0.889268	2

10747	0.000000	1.107592	2

44883	11.898890	0.406441	3

56823	3.529892	1.375844	1

68086	11.442677	0.696919	1

70242	10.308145	0.422722	1

11409	8.540529	0.727373	2

67671	7.156949	1.691682	1

61238	0.720675	0.847574	1

17774	0.229405	1.038603	2

53376	3.399331	0.077501	1

30930	6.157239	0.580133	1

28987	1.239698	0.719989	1

13655	6.036854	0.016548	2

7227	5.258665	0.933722	2

40409	12.393001	1.571281	3

13605	9.627613	0.935842	2

26400	11.130453	0.597610	3

13491	8.842595	0.349768	3

30232	10.690010	1.456595	3

43253	5.714718	1.674780	3

55536	3.052505	1.335804	1

8807	0.000000	0.059025	2

25783	9.945307	1.287952	3

22812	2.719723	1.142148	1

77826	11.154055	1.608486	1

38172	2.687918	0.660836	1

31676	10.037847	0.962245	3

74038	12.404762	1.112080	1

44738	10.237305	0.633422	3

17410	4.745392	0.662520	2

5688	4.639461	1.569431	2

36642	3.149310	0.639669	1

29956	13.406875	1.639194	3

60350	6.068668	0.881241	1

23758	9.477022	0.899002	3

25780	3.897620	0.560201	2

11342	5.463615	1.203677	2

36109	3.369267	1.575043	1

14292	5.234562	0.825954	2

11160	0.000000	0.722170	2

23762	12.979069	0.504068	3

39567	5.376564	0.557476	1

25647	13.527910	1.586732	3

14814	2.196889	0.784587	2

73590	10.691748	0.007509	1

35187	1.659242	0.447066	1

49459	8.369667	0.656697	3

31657	13.157197	0.143248	3

6259	8.199667	0.908508	2

33101	4.441669	0.439381	3

27107	9.846492	0.644523	3

17824	0.019540	0.977949	2

43536	8.253774	0.748700	3

67705	6.038620	1.509646	1

35283	6.091587	1.694641	3

71308	8.986820	1.225165	1

31054	11.508473	1.624296	3

52387	8.807734	0.713922	3

40328	0.000000	0.816676	1

34844	8.889202	1.665414	3

11607	3.178117	0.542752	2

64306	7.013795	0.139909	1

32721	9.605014	0.065254	3

33170	1.230540	1.331674	1

37192	10.412811	0.890803	3

13089	0.000000	0.567161	2

66491	9.699991	0.122011	1

15941	0.000000	0.061191	2

4272	4.455293	0.272135	2

48812	3.020977	1.502803	1

28818	8.099278	0.216317	3

35394	1.157764	1.603217	1

71791	10.105396	0.121067	1

40668	11.230148	0.408603	3

39580	9.070058	0.011379	3

11786	0.566460	0.478837	2

19251	0.000000	0.487300	2

56594	8.956369	1.193484	3

54495	1.523057	0.620528	1

11844	2.749006	0.169855	2

45465	9.235393	0.188350	3

31033	10.555573	0.403927	3

16633	6.956372	1.519308	2

13887	0.636281	1.273984	2

52603	3.574737	0.075163	1

72000	9.032486	1.461809	1

68497	5.958993	0.023012	1

35135	2.435300	1.211744	1

26397	10.539731	1.638248	3

7313	7.646702	0.056513	2

91273	20.919349	0.644571	1

24743	1.424726	0.838447	1

31690	6.748663	0.890223	3

15432	2.289167	0.114881	2

58394	5.548377	0.402238	1

33962	6.057227	0.432666	1

31442	10.828595	0.559955	3

31044	11.318160	0.271094	3

29938	13.265311	0.633903	3

9875	0.000000	1.496715	2

51542	6.517133	0.402519	3

11878	4.934374	1.520028	2

69241	10.151738	0.896433	1

37776	2.425781	1.559467	1

68997	9.778962	1.195498	1

67416	12.219950	0.657677	1

59225	7.394151	0.954434	1

29138	8.518535	0.742546	3

5962	2.798700	0.662632	2

10847	0.637930	0.617373	2

70527	10.750490	0.097415	1

9610	0.625382	0.140969	2

64734	10.027968	0.282787	1

25941	9.817347	0.364197	3

2763	0.646828	1.266069	2

55601	3.347111	0.914294	1

31128	11.816892	0.193798	3

5181	0.000000	1.480198	2

69982	10.945666	0.993219	1

52440	10.244706	0.280539	3

57350	2.579801	1.149172	1

57869	2.630410	0.098869	1

56557	11.746200	1.695517	3

42342	8.104232	1.326277	3

15560	12.409743	0.790295	3

34826	12.167844	1.328086	3

8569	3.198408	0.299287	2

77623	16.055513	0.541052	1

78184	7.138659	0.158481	1

7036	4.831041	0.761419	2

69616	10.082890	1.373611	1

21546	10.066867	0.788470	3

36715	8.129538	0.329913	3

20522	3.012463	1.138108	2

42349	3.720391	0.845974	1

9037	0.773493	1.148256	2

26728	10.962941	1.037324	3

587	0.177621	0.162614	2

48915	3.085853	0.967899	1

9824	8.426781	0.202558	2

4135	1.825927	1.128347	2

9666	2.185155	1.010173	2

59333	7.184595	1.261338	1

36198	0.000000	0.116525	1

34909	8.901752	1.033527	3

47516	2.451497	1.358795	1

55807	3.213631	0.432044	1

14036	3.974739	0.723929	2

42856	9.601306	0.619232	3

64007	8.363897	0.445341	1

59428	6.381484	1.365019	1

13730	0.000000	1.403914	2

41740	9.609836	1.438105	3

63546	9.904741	0.985862	1

30417	7.185807	1.489102	3

69636	5.466703	1.216571	1

64660	0.000000	0.915898	1

14883	4.575443	0.535671	2

7965	3.277076	1.010868	2

68620	10.246623	1.239634	1

8738	2.341735	1.060235	2

7544	3.201046	0.498843	2

6377	6.066013	0.120927	2

36842	8.829379	0.895657	3

81046	15.833048	1.568245	1

67736	13.516711	1.220153	1

32492	0.664284	1.116755	1

39299	6.325139	0.605109	3

77289	8.677499	0.344373	1

33835	8.188005	0.964896	3

71890	9.414263	0.384030	1

32054	9.196547	1.138253	3

38579	10.202968	0.452363	3

55984	2.119439	1.481661	1

72694	13.635078	0.858314	1

42299	0.083443	0.701669	1

26635	9.149096	1.051446	3

8579	1.933803	1.374388	2

37302	14.115544	0.676198	3

22878	8.933736	0.943352	3

4364	2.661254	0.946117	2

4985	0.988432	1.305027	2

37068	2.063741	1.125946	1

41137	2.220590	0.690754	1

67759	6.424849	0.806641	1

11831	1.156153	1.613674	2

34502	3.032720	0.601847	1

4088	3.076828	0.952089	2

15199	0.000000	0.318105	2

17309	7.750480	0.554015	3

42816	10.958135	1.482500	3

43751	10.222018	0.488678	3

58335	2.367988	0.435741	1

75039	7.686054	1.381455	1

42878	11.464879	1.481589	3

42770	11.075735	0.089726	3

8848	3.543989	0.345853	2

31340	8.123889	1.282880	3

41413	4.331769	0.754467	3

12731	0.120865	1.211961	2

22447	6.116109	0.701523	3

33564	7.474534	0.505790	3

48907	8.819454	0.649292	3

8762	6.802144	0.615284	2

46696	12.666325	0.931960	3

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55895	3.027528	0.885457	1

31587	1.845967	0.488985	1

10591	10.226164	0.804403	3

70096	10.965926	1.212328	1

53151	2.129921	1.477378	1

11992	0.000000	1.606849	2

33114	9.489005	0.827814	3

7413	0.000000	1.020797	2

10583	0.000000	1.270167	2

58668	6.556676	0.055183	1

35018	9.959588	0.060020	3

70843	7.436056	1.479856	1

14011	0.404888	0.459517	2

35015	9.952942	1.650279	3

70839	15.600252	0.021935	1

3024	2.723846	0.387455	2

5526	0.513866	1.323448	2

5113	0.000000	0.861859	2

20851	7.280602	1.438470	2

40999	9.161978	1.110180	3

15823	0.991725	0.730979	2

35432	7.398380	0.684218	3

53711	12.149747	1.389088	3

64371	9.149678	0.874905	1

9289	9.666576	1.370330	2

60613	3.620110	0.287767	1

18338	5.238800	1.253646	2

22845	14.715782	1.503758	3

74676	14.445740	1.211160	1

34143	13.609528	0.364240	3

14153	3.141585	0.424280	2

9327	0.000000	0.120947	2

18991	0.454750	1.033280	2

9193	0.510310	0.016395	2

2285	3.864171	0.616349	2

9493	6.724021	0.563044	2

2371	4.289375	0.012563	2

13963	0.000000	1.437030	2

2299	3.733617	0.698269	2

5262	2.002589	1.380184	2

4659	2.502627	0.184223	2

17582	6.382129	0.876581	2

27750	8.546741	0.128706	3

9868	2.694977	0.432818	2

18333	3.951256	0.333300	2

3780	9.856183	0.329181	2

18190	2.068962	0.429927	2

11145	3.410627	0.631838	2

68846	9.974715	0.669787	1

26575	10.650102	0.866627	3

48111	9.134528	0.728045	3

43757	7.882601	1.332446	3

 

 

 

2、python实现

import numpy as np
import pandas as pd

# 读取数据
dataarr=pd.read_csv('../../datingTestSet.txt')
dataset=[]
for row in dataarr.itertuples():
    if row:
        line=row._1
        line=line.strip()
        dataset.append(np.array(line.split('\t')))
dataset=np.array(dataset)
dataset = dataset.astype(float)    
X = dataset[:, 0: 3]
y = dataset[:, -1]
# testt=[[43605  ,  0.120460  ,  1.352013]]

# 切分数据集为训练集和测试集
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)

# 归一化数据
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

# 训练模型
from sklearn.neighbors import KNeighborsClassifier
classifier = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)
classifier.fit(X_train, y_train)
 
# 测试模型
y_pred = classifier.predict(X_test)

# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
cm = confusion_matrix(y_test, y_pred)
print(cm)
print( y_test, "--",y_pred) 
print(classification_report(y_test, y_pred))