海伦约会问题

我们要输入一个人的特征，看看这个人是否适合做海伦的约会对象，这里相比于入门版的Knn用到了文件流

点此可以看之前那个入门版KNN的代码

样本特征取三个值

1.每周获得的飞行常客里程数

2.玩视频游戏所耗时间比例

3.每周消费的冰淇淋公升数

推荐先把上面的入门版先看完再来看这个

代码如下：

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.lines as mlines
import operatorfrom sklearn.preprocessing import MinMaxScalerdef file_matrix(filename):"""准备数据，从文本文件中解析数据:param filename:文件名称:return:特征值矩阵（数据集）、目标值向量"""# 打开文件with open(filename, 'r') as f:# 以行读取文件所有内容array_lines = f.readlines()# 得到文件行数lines_num = len(array_lines)# 返回的numpy矩阵,解析完成的数据:lines_num行,3列return_mat = np.zeros((lines_num, 3))# 返回的分类标签向量class_label_vector = []# 行的索引值index = 0for line in array_lines:# s.strip(rm)，当rm空时,默认删除空白符(包括'\n','\r','\t',' ')line = line.strip()# 使用s.split(str="",num=string,cout(str))将字符串根据','分隔符进行切片。list_from_line = line.split(',')# 将数据前三列提取出来,存放到return_mat的NumPy矩阵中,也就是特征矩阵return_mat[index, :] = list_from_line[0:3]# 根据文本中标记的喜欢的程度进行分类,1代表不喜欢,2代表魅力一般,3代表极具魅力class_label_vector.append(list_from_line[-1])index += 1return return_mat, class_label_vectordef show_datas(data_mat, labels):"""分析数据，数据可视化，使用Matplotlib创建散点图:param data_mat: 特征值矩阵（数据集）:param labels: 目标值向量:return: None"""# 将fig画布分隔成2行2列,不共享x轴和y轴,fig画布的大小为(13,9)# 当nrow=2,nclos=2时,代表fig画布被分为四个区域,axs[0][0]表示第一行第一个区域fig, axs = plt.subplots(nrows=2, ncols=2, figsize=(12, 10))# 设置点的颜色labels_colors = []for i in labels:if i == "1":labels_colors.append('black')if i == "2":labels_colors.append('orange')if i == "3":labels_colors.append('red')# 画出散点图,以data_mat矩阵的第一(飞行常客例程)、第二列(玩游戏)数据画散点数据,散点大小为15,透明度为0.5axs[0][0].scatter(x=data_mat[:, 0], y=data_mat[:, 1], color=labels_colors, s=15, alpha=0.5)# 设置标题,x轴label,y轴labelaxs0_title_text = axs[0][0].set_title('每年获得的飞行常客里程数与玩视频游戏所消耗时间占比')axs0_xlabel_text = axs[0][0].set_xlabel('每年获得的飞行常客里程数')axs0_ylabel_text = axs[0][0].set_ylabel('玩视频游戏所消耗时间占')# 设置标题,x轴label,y轴label的字体大小、是否加粗、颜色plt.setp(axs0_title_text, size=12, weight='bold', color='red')plt.setp(axs0_xlabel_text, size=10, weight='bold', color='black')plt.setp(axs0_ylabel_text, size=10, weight='bold', color='black')# 画出散点图,以data_mat矩阵的第一(飞行常客例程)、第三列(冰激凌)数据画散点数据,散点大小为15,透明度为0.5axs[0][1].scatter(x=data_mat[:, 0], y=data_mat[:, 2], color=labels_colors, s=15, alpha=0.5)# 设置标题,x轴label,y轴labelaxs1_title_text = axs[0][1].set_title('每年获得的飞行常客里程数与每周消费的冰激淋公升数', )axs1_xlabel_text = axs[0][1].set_xlabel('每年获得的飞行常客里程数')axs1_ylabel_text = axs[0][1].set_ylabel('每周消费的冰激淋公升数')# 设置标题,x轴label,y轴label的字体大小、是否加粗、颜色plt.setp(axs1_title_text, size=12, weight='bold', color='red')plt.setp(axs1_xlabel_text, size=10, weight='bold', color='black')plt.setp(axs1_ylabel_text, size=10, weight='bold', color='black')# 画出散点图,以data_mat矩阵的第二(玩游戏)、第三列(冰激凌)数据画散点数据,散点大小为15,透明度为0.5axs[1][0].scatter(x=data_mat[:, 1], y=data_mat[:, 2], color=labels_colors, s=15, alpha=0.5)# 设置标题,x轴label,y轴labelaxs2_title_text = axs[1][0].set_title('玩视频游戏所消耗时间占比与每周消费的冰激淋公升数')axs2_xlabel_text = axs[1][0].set_xlabel('玩视频游戏所消耗时间占比')axs2_ylabel_text = axs[1][0].set_ylabel('每周消费的冰激淋公升数')# 设置标题,x轴label,y轴label的字体大小、是否加粗、颜色plt.setp(axs2_title_text, size=12, weight='bold', color='red')plt.setp(axs2_xlabel_text, size=10, weight='bold', color='black')plt.setp(axs2_ylabel_text, size=10, weight='bold', color='black')# 设置图例didnt_like = mlines.Line2D([], [], color='black', marker='.', markersize=6, label='不喜欢')small_doses = mlines.Line2D([], [], color='orange', marker='.', markersize=6, label='魅力一般')large_doses = mlines.Line2D([], [], color='red', marker='.', markersize=6, label='极具魅力')# 添加图例axs[0][0].legend(handles=[didnt_like, small_doses, large_doses])axs[0][1].legend(handles=[didnt_like, small_doses, large_doses])axs[1][0].legend(handles=[didnt_like, small_doses, large_doses])# 显示图片plt.show()'''
def auto_norm(data_set):"""准备数据，数据归一化处理:param data_set: 需要进行归一化的数据集:return: 归一化是数据集、最小值与最大值的范围、最小值"""# 获得每列数据的最小值和最大值min_vals = data_set.min(0)max_vals = data_set.max(0)# 最大值和最小值的范围ranges = max_vals - min_vals# data_set.shape 返回 data_set的矩阵行列数# 返回data_set的行数lines_num = data_set.shape[0]# np.tile(min_vals, (lines_num, 1))在行向量方向上重复min_vals共lines_num次(横向),列向量方向上重复1次(纵向)# 原始值减去最小值norm_data_set = data_set - np.tile(min_vals, (lines_num, 1))# 除以最大和最小值的差,得到归一化数据norm_data_set = norm_data_set / np.tile(ranges, (lines_num, 1))# 返回归一化数据结果,数据范围,最小值return norm_data_set, ranges, min_vals
'''def auto_norm(data_set):"""准备数据，数据归一化处理:param data_set: 需要进行归一化的数据集:return: 归一化是数据集、最小值与最大值的范围、最小值"""# 获得每列数据的最小值和最大值min_vals = data_set.min(0)max_vals = data_set.max(0)# 最大值和最小值的范围ranges = max_vals - min_vals# feature_range=(0, 1) 数据返回值在（0，1）之间# 对数据进行归一化处理mm = MinMaxScaler(feature_range=(0, 1))norm_data_set = mm.fit_transform(data_set)return norm_data_set, ranges, min_valsdef kNN(inX, data_set, labels, k):"""KNN算法分类器 :param inX: 用于分类的数据(测试集):param data_set: 用于训练的数据(训练集):param labels: 训练数据的分类标签:param k: kNN算法参数,选择距离最小的k个点:return: 分类结果"""# numpy函数shape[0]返回data_set的行数data_set_size = data_set.shape[0]# 在列向量方向上重复inX共1次(横向),行向量方向上重复inX共data_set_size次(纵向)diff_mat = np.tile(inX, (data_set_size, 1)) - data_set# 二维特征相减后平方diff_mat_sq = diff_mat ** 2# sum()所有元素相加,sum(0)列相加,sum(1)行相加distances_sq = diff_mat_sq.sum(axis=1)# 开方,计算出距离distances = distances_sq ** 0.5# 返回distances中元素从小到大排序后的索引值sorted_dist_index = distances.argsort()# 定一个记录类别次数的字典class_count = {}for i in range(k):# 取出前k个元素的类别vote_label = labels[sorted_dist_index[i]]# dict.get(key,default=None),字典的get()方法,返回指定键的值,如果值不在字典中返回默认值。# 计算类别次数class_count[vote_label] = class_count.get(vote_label, 0) + 1# python2中用iteritems()替换python3中的items()# key=operator.itemgetter(1)根据字典的值进行排序# key=operator.itemgetter(0)根据字典的键进行排序# reverse降序排序字典sorted_class_count = sorted(class_count.items(), key=operator.itemgetter(1), reverse=True)# 返回次数最多的类别,即所要分类的类别return sorted_class_count[0][0]def class_test(norm_mat, labels):"""测试算法，计算分类器的准确率，验证分类器:param norm_mat: 归一化处理后的特征值矩阵（数据集）:param labels: 目标值向量:return:None"""# 取所有数据的百分之十作为测试集ratio = 0.10# 获得norm_mat的行数lines_num = norm_mat.shape[0]# 百分之十的测试数据的个数num_test_vecs = int(lines_num * ratio)# 分类错误计数error_count = 0.0for i in range(num_test_vecs):# 前num_test_vecs个数据作为测试集,后m-num_test_vecs个数据作为训练集classifier_result = kNN(norm_mat[i, :], norm_mat[num_test_vecs:lines_num, :], labels[num_test_vecs:lines_num], 4)print("分类结果:%s\t真实类别:%s" % (classifier_result, labels[i]))if classifier_result != labels[i]:error_count += 1.0print("错误率:%.2f%%" % (error_count / float(num_test_vecs) * 100))def class_person(norm_mat, labels):"""使用算法，构建完整可用系统:param norm_mat: 归一化处理后的特征值矩阵（数据集）:param labels: 目标值向量:return: None"""# 输出结果result_list = ['不喜欢', '有些喜欢', '非常喜欢']# 三维特征用户输入ff_miles = float(input("每年获得的飞行常客里程数:"))precent_tats = float(input("玩视频游戏所耗时间百分比:"))ice_cream = float(input("每周消费的冰激淋公升数:"))# 生成NumPy数组,测试集test_array = np.array([ff_miles, precent_tats, ice_cream])# 测试集归一化test_array_nor = (test_array - min_vals) / ranges# 返回分类结果classifier_result = kNN(test_array_nor, norm_mat, labels, 3)# 打印结果print("你可能%s这个人" % (result_list[int(classifier_result) - 1]))# 主函数，测试以上各个步骤，并输出各个步骤的结果
if __name__ == '__main__':# 打开的文件名filename = "dataset.txt"# 打开并处理数据data_mat, labels = file_matrix(filename)# 数据可视化show_datas(data_mat, labels)# 训练集归一化norm_mat, ranges, min_vals = auto_norm(data_mat)# 验证分类器class_test(norm_mat, labels)# 使用分类器class_person(norm_mat, labels)

运行结果：（如果中文不能正常显示，点击查看：windows下matplotlib图例中文无法显示的解决办法）

分类结果:3	真实类别:3
分类结果:2	真实类别:2
分类结果:1	真实类别:1
分类结果:1	真实类别:1
分类结果:1	真实类别:1
分类结果:1	真实类别:1
分类结果:3	真实类别:3
分类结果:3	真实类别:3
分类结果:1	真实类别:1
分类结果:3	真实类别:3
分类结果:1	真实类别:1
分类结果:1	真实类别:1
分类结果:2	真实类别:2
分类结果:1	真实类别:1
分类结果:1	真实类别:1
分类结果:1	真实类别:1
分类结果:1	真实类别:1
分类结果:1	真实类别:1
分类结果:2	真实类别:2
分类结果:3	真实类别:3
分类结果:2	真实类别:2
分类结果:1	真实类别:1
分类结果:2	真实类别:2
分类结果:3	真实类别:3
分类结果:2	真实类别:2
分类结果:3	真实类别:3
分类结果:2	真实类别:2
分类结果:3	真实类别:3
分类结果:2	真实类别:2
分类结果:1	真实类别:1
分类结果:3	真实类别:3
分类结果:1	真实类别:1
分类结果:3	真实类别:3
分类结果:1	真实类别:1
分类结果:2	真实类别:2
分类结果:1	真实类别:1
分类结果:1	真实类别:1
分类结果:2	真实类别:2
分类结果:3	真实类别:3
分类结果:3	真实类别:3
分类结果:1	真实类别:1
分类结果:2	真实类别:2
分类结果:3	真实类别:3
分类结果:3	真实类别:3
分类结果:3	真实类别:3
分类结果:1	真实类别:1
分类结果:1	真实类别:1
分类结果:1	真实类别:1
分类结果:1	真实类别:1
分类结果:2	真实类别:2
分类结果:2	真实类别:2
分类结果:1	真实类别:1
分类结果:3	真实类别:3
分类结果:2	真实类别:2
分类结果:2	真实类别:2
分类结果:2	真实类别:2
分类结果:2	真实类别:2
分类结果:3	真实类别:3
分类结果:1	真实类别:1
分类结果:2	真实类别:2
分类结果:1	真实类别:1
分类结果:2	真实类别:2
分类结果:2	真实类别:2
分类结果:2	真实类别:2
分类结果:2	真实类别:2
分类结果:2	真实类别:2
分类结果:3	真实类别:3
分类结果:2	真实类别:2
分类结果:3	真实类别:3
分类结果:1	真实类别:1
分类结果:2	真实类别:2
分类结果:3	真实类别:3
分类结果:2	真实类别:2
分类结果:2	真实类别:2
分类结果:3	真实类别:1
分类结果:3	真实类别:3
分类结果:1	真实类别:1
分类结果:1	真实类别:1
分类结果:3	真实类别:3
分类结果:3	真实类别:3
分类结果:1	真实类别:1
分类结果:2	真实类别:2
分类结果:3	真实类别:3
分类结果:3	真实类别:1
分类结果:3	真实类别:3
分类结果:1	真实类别:1
分类结果:2	真实类别:2
分类结果:2	真实类别:2
分类结果:1	真实类别:1
分类结果:1	真实类别:1
分类结果:3	真实类别:3
分类结果:2	真实类别:3
分类结果:1	真实类别:1
分类结果:2	真实类别:2
分类结果:1	真实类别:1
分类结果:3	真实类别:3
分类结果:3	真实类别:3
分类结果:2	真实类别:2
分类结果:2	真实类别:1
分类结果:1	真实类别:1
错误率:4.00%
每年获得的飞行常客里程数:1
玩视频游戏所耗时间百分比:1
每周消费的冰激淋公升数:1
你可能有些喜欢这个人

代码数据如下：

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
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