本文实例为大家分享了python实现knn算法的具体代码，供大家参考，具体内容如下
knn算法描述
对需要分类的点依次执行以下操作：
1.计算已知类别数据集中每个点与该点之间的距离
2.按照距离递增顺序排序
3.选取与该点距离最近的k个点
4.确定前k个点所在类别出现的频率
5.返回前k个点出现频率最高的类别作为该点的预测分类
knn算法实现
数据处理
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#从文件中读取数据，返回的数据和分类均为二维数组
def loadDataSet(filename):
dataSet = []
labels = []
fr = open(filename)
for line in fr.readlines():
lineArr = line.strip().split(",")
dataSet.append([float(lineArr[0]),float(lineArr[1])])
labels.append([float(lineArr[2])])
return dataSet , labels
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knn算法
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#计算两个向量之间的欧氏距离
def calDist(X1 , X2):
sum = 0
for x1 , x2 in zip(X1 , X2):
sum += (x1 - x2) ** 2
return sum ** 0.5
def knn(data , dataSet , labels , k):
n = shape(dataSet)[0]
for i in range(n):
dist = calDist(data , dataSet[i])
#只记录两点之间的距离和已知点的类别
labels[i].append(dist)
#按照距离递增排序
labels.sort(key=lambda x:x[1])
count = {}
#统计每个类别出现的频率
for i in range(k):
key = labels[i][0]
if count.has_key(key):
count[key] += 1
else : count[key] = 1
#按频率递减排序
sortCount = sorted(count.items(),key=lambda item:item[1],reverse=True)
return sortCount[0][0]#返回频率最高的key，即label
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结果测试
已知类别数据（来源于西瓜书+虚构）
0.697,0.460,1
0.774,0.376,1
0.720,0.330,1
0.634,0.264,1
0.608,0.318,1
0.556,0.215,1
0.403,0.237,1
0.481,0.149,1
0.437,0.211,1
0.525,0.186,1
0.666,0.091,0
0.639,0.161,0
0.657,0.198,0
0.593,0.042,0
0.719,0.103,0
0.671,0.196,0
0.703,0.121,0
0.614,0.116,0
绘图方法
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def drawPoints(data , dataSet, labels):
xcord1 = [];
ycord1 = [];
xcord2 = [];
ycord2 = [];
for i in range(shape(dataSet)[0]):
if labels[i][0] == 0:
xcord1.append(dataSet[i][0])
ycord1.append(dataSet[i][1])
if labels[i][0] == 1:
xcord2.append(dataSet[i][0])
ycord2.append(dataSet[i][1])
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(xcord1, ycord1, s=30, c='blue', marker='s',label=0)
ax.scatter(xcord2, ycord2, s=30, c='green',label=1)
ax.scatter(data[0], data[1], s=30, c='red',label="testdata")
plt.legend(loc='upper right')
plt.show()
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测试代码
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dataSet , labels = loadDataSet('dataSet.txt')
data = [0.6767,0.2122]
drawPoints(data , dataSet, labels)
newlabels = knn(data, dataSet , labels , 5)
print newlabels
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运行结果
以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持服务器之家。
原文链接：http://blog.csdn.net/chenge_j/article/details/72110652