import numpy as np
import operator
'''
trainData - 训练集 N
testData - 测试 1
labels - 训练集标签
'''
def knn(trainData, testData, labels, k):
# 计算训练样本的行数
rowSize = trainData.shape[0]
# 计算训练样本和测试样本的差值
diff = np.tile(testData, (rowSize, 1)) - trainData
# 计算差值的平方和
sqrDiff = diff ** 2
sqrDiffSum = sqrDiff.sum(axis=1)
# 计算距离
distances = sqrDiffSum ** 0.5
# 对所得的距离从低到高进行排序
sortDistance = distances.argsort()
count = {}
for i in range(k):
vote = labels[sortDistance[i]]
# print(vote)
count[vote] = count.get(vote, 0) + 1
# 对类别出现的频数从高到低进行排序
sortCount = sorted(count.items(), key=operator.itemgetter(1), reverse=True)
# 返回出现频数最高的类别
return sortCount[0][0]
file_data = 'iris.data'
# 数据读取
data = np.loadtxt(file_data,dtype = float, delimiter = ',',usecols=(0,1,2,3))
lab = np.loadtxt(file_data,dtype = str, delimiter = ',',usecols=(4))
# 分为训练集和测试集和
N = 150
N_train = 100
N_test = 50
perm = np.random.permutation(N)
index_train = perm[:N_train]
index_test = perm[N_train:]
data_train = data[index_train,:]
lab_train = lab[index_train]
data_test = data[index_test,:]
lab_test = lab[index_test]
# 参数设定
k= 5
n_right = 0
for i in range(N_test):
test = data_test[i,:]
det = knn(data_train, test, lab_train, k)
if det == lab_test[i]:
n_right = n_right+1
print('Sample %d lab_ture = %s lab_det = %s'%(i,lab_test[i],det))
# 结果分析
print('Accuracy = %.2f %%'%(n_right*100/N_test))
