一：简单实现K-近邻算法
（一）导入数据
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
def CreateDataSet():
data = np.array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]])
labels = np.array(['A','A','B','B'])
return data,labels
data,labels = CreateDataSet()
print(data)
print(labels)
plt.figure()
plt.scatter(data[:,0],data[:,1],c="b")
for i in range(data.shape[0]):
plt.text(data[i,0]+0.02,data[i,1],labels[i])
plt.show()
（二）实现KNN算法
def KNNClassfy(preData,dataSet,labels,k):
distance = np.sum(np.power(dataSet - preData,2),1) #注意：这里我们不进行开方，可以少算一次
sortDistIdx = np.argsort(distance,0) #小到大排序，获取索引
labels_idx = {}
for i in range(k): #获取分类
idx = sortDistIdx[i] #获取索引
label = labels[idx] #获取标签
labels_idx[label] = labels_idx.get(label,0)+1
labels_sort = sorted(labels_idx.items(),key=lambda x:x[1],reverse=True)
return labels_sort[0][0] #获取最大可能分类
（三）结果测试
preData = np.array([0,0.3])
preLab = KNNClassfy(preData,data,labels,3)
print(preLab)
二：使用KNN算法分析喜好---多维
(一)读取数据
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
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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
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41732 9.505944 0.005273 3
11429 8.652725 1.348934 3
75270 17.101108 0.490712 1
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73520 8.262131 1.361646 1
40279 9.015635 1.658555 3
21540 9.215351 0.806762 3
17694 6.375007 0.033678 2
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46570 5.677110 0.709469 1
42403 11.293017 0.207976 3
33654 6.590043 1.353117 1
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28122 8.768099 1.108041 3
34095 11.502519 0.545097 3
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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
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27742 9.005850 0.548737 3
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24619 1.318340 1.436243 1
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33225 7.960293 1.219760 3
54483 6.317292 0.018209 1
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33926 2.906644 0.581657 1
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26547 6.024471 0.486215 3
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22160 12.112492 0.359680 3
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34914 11.773195 1.262621 3
6121 8.339588 1.443357 2
38063 2.563092 1.464013 1
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35073 9.288374 0.767318 3
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28521 2.943070 1.446816 1
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43757 7.882601 1.332446 3
datingTestSet2.txt
data = np.loadtxt("datingTestSet2.txt",delimiter='\t') #读取数据
hob_data = data[:,:-1]
hob_labels = data[:,-1]
(二)归一化处理
def dataNorm(data): #归一化操作
mn = np.mean(data,0)
sigma = np.std(data,0,ddof=0)
return (data - mn)/sigma,mn,sigma
(三)简单划分训练集,测试集
#划分测试集和训练集数据
hoRatio = 0.4 #测试集比例
m = hob_labels.size
m_test = int(hob_labels.size*hoRatio)
hob_data_norm,mn,sigma = dataNorm(hob_data)
#获取训练集数据
X = hob_data_norm[:m-m_test,:]
y = hob_labels[:m-m_test]
#获取测试集数据
X_test = hob_data_norm[m-m_test:m,:]
y_test = hob_labels[m-m_test:m]
(四)进行测试,获取错误率
#进行测试
error_count = 0
for i in range(y_test.size):
clf = KNNClassfy(X_test[i],X,y,3)
if clf != y_test[i]:
error_count = error_count + 1
print("{} --- {}".format(clf,y_test[i]))
print("error rate is:",error_count/y_test.size)
(五)进行预测
#进行预测
resultList = ['not at all','in small doses','in large doses']
hb_1 = float(input("爱好一:"))
hb_2 = float(input("爱好二:"))
hb_3 = float(input("爱好三:"))
preData = np.array([[hb_1,hb_2,hb_3]])
preData_norm = ((preData - mn)/sigma).flatten()
clf = KNNClassfy(preData_norm,hob_data_norm,hob_labels,3)
print("喜欢程度:{}".format(resultList[int(clf)-1]))
三:手写数字识别
(一)数据展示
数据存放形式:比如7,是32*32像素
文件存放形式:_前面是数字,_后面表示是该数字的第几种形式
(二)数据读取---将图像转向量
from os import listdir
import codecs
#将每一个数字文件转换为矩阵向量
def image2Vector(filename):
data = []
with codecs.open(filename,'r') as fp:
for i in range(32):
linestr = fp.readline() #读取一行数据
for j in range(32):
data.append(int(linestr[j])) #添加数据
fp.close()
return np.array(data)
(三)获取训练集和测试集
#获取数据集
def getDataSet(path):
#读取数据
hwLabels = []
filelist = listdir(path) #获取所有文件目录
m = len(filelist)
data = np.zeros((m,1024))
#先获取标签值
for i in range(m):
filename = filelist[i]
hwLabels.append(int(filename.split('_')[0])) #添加标签值
data[i,:] = image2Vector("%s/%s"%(path,filename))
return data,hwLabels
#获取训练集
data,labels = getDataSet("trainingDigits")
#获取测试集
data_test,labels_test = getDataSet("testDigits")
print(data_test.shape)
(三)实现KNN,改变部分
def KNNClassfy(preData,dataSet,labels,k):
distance = np.sum(np.power(dataSet - preData,2),1) #注意：这里我们不进行开方，可以少算一次
sortDistIdx = np.argsort(distance,0) #小到大排序，获取索引
labels_idx = {}
for i in range(k): #获取分类
idx = sortDistIdx[i] #获取索引
label = labels[idx] #获取标签
labels_idx[label] = labels_idx.get(label,0)
labels_sort = sorted(labels_idx.items(),key=lambda x:x[1],reverse=True)
return labels_sort[0][0] #获取最大可能分类
(四)结果测试
#进行测试
error_count = 0
for i in range(data_test.shape[0]):
clf = KNNClassfy(data_test[i,:],data,labels,3)
if clf != labels_test[i]:
error_count += 1
print(error_count)
print("{} {}".format(error_count,error_count/data_test.shape[0]))
