KNN算法应用

import numpy as np# 运算符模块，这里主要用来排序
import operator
import matplotlib.pylab as plt

def create_dataset():
    group = np.array([[1., 1.1], [1., 1.], [0., 0.], [0., 0.1]])
    labels = ['A', 'A', 'B', 'B']
    return group, labels

def classify0(in_x, dataset, labels, k):
    """简单分类器1"""
    # 行，也即点的个数，每个点一行表示，提取几行就几个点
    dataset_size = dataset.shape[0]
    # 纵向拓展一下输入的x，让它可以和原来的所有向量批量求差
    diff_mat = np.tile(in_x, (dataset_size, 1)) - dataset

    # 求差的平方和
    sq_diff_mat = diff_mat ** 2
    # 在计算距离的时候，如果有个维度的数值很大，那么将直接影响计算的结果
    # 简单归一化new_value = (old_value - min)/(max-min)
    distance = (sq_diff_mat.sum(axis=1)) ** 0.5  # 求与每一个点的距离平方，是列向量

    # 返回数组从小到大的索引，ndarray
    sorted_distance_indices = distance.argsort()
    # 弄个空的
    class_count = {}

    for i in range(k):
        # 把刚距离由小到大的label放进去
        vote_label = labels[sorted_distance_indices[i]]
        # 对每个label进行计数
        # dict.get前面是key，不存在赋0，存在返回原有的值
        class_count[vote_label] = class_count.get(vote_label, 0) + 1
    # 定义一个排序，排的是计数的所有内容，第二个域（第二维变量）
    # 改成operator.itemgetter(1，0)就会参造第二维先，再考虑第一维
    sorted_class_count = sorted(class_count.items(),
                                key=operator.itemgetter(1), reverse=True)
    # 这里得到的B是[('B', 2), ('A', 1)]

    # 这个对应的就是标签号了
    return sorted_class_count[0][0]

def file2matrix(filename):
    """
    传入文件，读取内容，得到点和label
    第二列是玩游戏所耗时间比
    第三列是每周所消费的冰淇淋公升数
    """
    with open(filename, 'r') as f:
        # 所有行数读取存在一个列表里
        lines = f.readlines()
    num_of_lines = len(lines)
    # 初始化点的矩阵,第二个参数其实直接可以写3，不过万一你要加多几列呢
    mat = np.zeros((num_of_lines, lines[0].count('\t')))
    # 初始化标签
    class_label_vector = []
    # 用enumerate省得再打一个index
    for index, line in enumerate(lines):
        # 每一行拆成列表，strip()去掉头尾任意空字符
        list_from_line = line.strip().split('\t')
        # 点
        mat[index, :] = list_from_line[0:3]
        # 标签,注这里的标签可能是string，看输入的是datingTestSet几
        class_label_vector.append(int(list_from_line[-1]))
    return mat, class_label_vector

def plot_figure(data_mat, labels):
    """
    没有加入legend
    有需要参考：https://www.zhihu.com/question/37146648
    """
    # 生成一个新的图像
    fig = plt.figure()
    # 这里的111的意思就是，图像画成一行一列（其实就一个框），最后一个1就是放在从左到右，从上到下的第1个
    # 想在一个画面里面放多几个子图和分配位置改下这个参数就好了
    ax = fig.add_subplot(111)
    # 前两个参数试一试data_mat[:,1],data_mat[:,2]
    # 或者data_mat[:,1],data_mat[:,0]  # 这种的区分度更高
    # scatter是画散点图
    # 10是点的大小，前后是颜色
    t = ax.scatter(data_mat[:, 1], data_mat[:, 0],
                   10.0 * np.array(labels), np.array(labels))
    plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签
    plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号
    plt.title(u'散点图')
    plt.xlabel(u'打机时间')
    plt.ylabel(u'飞机里程')
    plt.show()

def auto_norm(dataset):
    # 类似[0.       0.       0.001156]
    min_val = np.min(dataset, axis=0)
    # [9.1273000e+04 2.0919349e+01 1.6955170e+00]
    max_val = np.max(dataset, axis=0)
    # 范围
    ranges = max_val - min_val
    # 行数，即样本数
    m = dataset.shape[0]
    # 归一化
    norm_dataset = (dataset - np.tile(min_val, (m, 1))) / ranges
    return norm_dataset, ranges, min_val

def dating_class_test():
    ratio = 0.1
    mat, labels = file2matrix('./datingTestSet2.txt')
    # 归一化
    norm_mat, ranges, miv_val = auto_norm(mat)
    # 样本数
    m = norm_mat.shape[0]
    # 取一定的样本
    num_test_vecs = int(m * ratio)
    error = 0.
    for i in range(num_test_vecs):
        # 只能一个个取，真弱，knn算了，不然有空改下claasify
        result = classify0(norm_mat[i, :], norm_mat[num_test_vecs:m, :],
                           labels[num_test_vecs:m], 3)
        # print("the classifier came back with: %d, the real answer is: %d"\
        #       % (result,labels[i]))
        if result != labels[i]:
            error += 1.
    print("the accuracy rate is: %.1f%%" % (100 * (1 - error / float(num_test_vecs))))

def main():
    group, labels = create_dataset()
    # 看看点[0,0]的归类，k取值3
    result = classify0([0, 0], group, labels, 3)
    print('the class of [0,0] is: ', result)
    mat, labels = file2matrix(r'./datingTestSet2.txt')
    norm_dataset, ranges, min_val = auto_norm(mat)
    # 画散点图
    plot_figure(norm_dataset, labels)
    # 预测
    dating_class_test()

if __name__ == '__main__':
    main()

　　

datingTestSet2数据
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
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47903	4.138435	0.475994	1
37190	12.929517	0.304378	3
6013	9.378238	0.307392	2
27223	8.361362	1.643204	3
69027	7.939406	1.325042	1
78642	10.735384	0.705788	1
30254	11.592723	0.286188	3
21704	10.098356	0.704748	3
34985	9.299025	0.545337	3
31316	11.158297	0.218067	3
76368	16.143900	0.558388	1
27953	10.971700	1.221787	3
152	0.000000	0.681478	2
9146	3.178961	1.292692	2
75346	17.625350	0.339926	1
26376	1.995833	0.267826	1
35255	10.640467	0.416181	3
19198	9.628339	0.985462	3
12518	4.662664	0.495403	2
25453	5.754047	1.382742	2
12530	0.000000	0.037146	2
62230	9.334332	0.198118	1
9517	3.846162	0.619968	2
71161	10.685084	0.678179	1
1593	4.752134	0.359205	2
33794	0.697630	0.966786	1
39710	10.365836	0.505898	3
16941	0.461478	0.352865	2
69209	11.339537	1.068740	1
4446	5.420280	0.127310	2
9347	3.469955	1.619947	2
55635	8.517067	0.994858	3
65889	8.306512	0.413690	1
10753	2.628690	0.444320	2
7055	0.000000	0.802985	2
7905	0.000000	1.170397	2
53447	7.298767	1.582346	3
9194	7.331319	1.277988	2
61914	9.392269	0.151617	1
15630	5.541201	1.180596	2
79194	15.149460	0.537540	1
12268	5.515189	0.250562	2
33682	7.728898	0.920494	3
26080	11.318785	1.510979	3
19119	3.574709	1.531514	2
30902	7.350965	0.026332	3
63039	7.122363	1.630177	1
51136	1.828412	1.013702	1
35262	10.117989	1.156862	3
42776	11.309897	0.086291	3
64191	8.342034	1.388569	1
15436	0.241714	0.715577	2
14402	10.482619	1.694972	2
6341	9.289510	1.428879	2
14113	4.269419	0.134181	2
6390	0.000000	0.189456	2
8794	0.817119	0.143668	2
43432	1.508394	0.652651	1
38334	9.359918	0.052262	3
34068	10.052333	0.550423	3
30819	11.111660	0.989159	3
22239	11.265971	0.724054	3
28725	10.383830	0.254836	3
57071	3.878569	1.377983	1
72420	13.679237	0.025346	1
28294	10.526846	0.781569	3
9896	0.000000	0.924198	2
65821	4.106727	1.085669	1
7645	8.118856	1.470686	2
71289	7.796874	0.052336	1
5128	2.789669	1.093070	2
13711	6.226962	0.287251	2
22240	10.169548	1.660104	3
15092	0.000000	1.370549	2
5017	7.513353	0.137348	2
10141	8.240793	0.099735	2
35570	14.612797	1.247390	3
46893	3.562976	0.445386	1
8178	3.230482	1.331698	2
55783	3.612548	1.551911	1
1148	0.000000	0.332365	2
10062	3.931299	0.487577	2
74124	14.752342	1.155160	1
66603	10.261887	1.628085	1
11893	2.787266	1.570402	2
50908	15.112319	1.324132	3
39891	5.184553	0.223382	3
65915	3.868359	0.128078	1
65678	3.507965	0.028904	1
62996	11.019254	0.427554	1
36851	3.812387	0.655245	1
36669	11.056784	0.378725	3
38876	8.826880	1.002328	3
26878	11.173861	1.478244	3
46246	11.506465	0.421993	3
12761	7.798138	0.147917	3
35282	10.155081	1.370039	3
68306	10.645275	0.693453	1
31262	9.663200	1.521541	3
34754	10.790404	1.312679	3
13408	2.810534	0.219962	2
30365	9.825999	1.388500	3
10709	1.421316	0.677603	2
24332	11.123219	0.809107	3
45517	13.402206	0.661524	3
6178	1.212255	0.836807	2
10639	1.568446	1.297469	2
29613	3.343473	1.312266	1
22392	5.400155	0.193494	1
51126	3.818754	0.590905	1
53644	7.973845	0.307364	3
51417	9.078824	0.734876	3
24859	0.153467	0.766619	1
61732	8.325167	0.028479	1
71128	7.092089	1.216733	1
27276	5.192485	1.094409	3
30453	10.340791	1.087721	3
18670	2.077169	1.019775	2
70600	10.151966	0.993105	1
12683	0.046826	0.809614	2
81597	11.221874	1.395015	1
69959	14.497963	1.019254	1
8124	3.554508	0.533462	2
18867	3.522673	0.086725	2
80886	14.531655	0.380172	1
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

　　

实验结果：