56 lines
1.8 KiB
Python
56 lines
1.8 KiB
Python
|
# 导入算法包以及数据集
|
|||
|
|
import numpy as np
|
|||
|
|
from sklearn import datasets
|
|||
|
|
from sklearn.model_selection import train_test_split
|
|||
|
|
from sklearn.metrics import classification_report,confusion_matrix
|
|||
|
|
import operator
|
|||
|
|
import random
|
|||
|
|
def knn(x_test, x_data, y_data, k):
|
|||
|
|
# 计算样本数量
|
|||
|
|
x_data_size = x_data.shape[0]
|
|||
|
|
# 复制x_test
|
|||
|
|
np.tile(x_test, (x_data_size,1))
|
|||
|
|
# 计算x_test与每一个样本的差值
|
|||
|
|
diffMat = np.tile(x_test, (x_data_size,1)) - x_data
|
|||
|
|
# 计算差值的平方
|
|||
|
|
sqDiffMat = diffMat**2
|
|||
|
|
# 求和
|
|||
|
|
sqDistances = sqDiffMat.sum(axis=1)
|
|||
|
|
# 开方
|
|||
|
|
distances = sqDistances**0.5
|
|||
|
|
# 从小到大排序
|
|||
|
|
sortedDistances = distances.argsort()
|
|||
|
|
classCount = {}
|
|||
|
|
for i in range(k):
|
|||
|
|
# 获取标签
|
|||
|
|
votelabel = y_data[sortedDistances[i]]
|
|||
|
|
# 统计标签数量
|
|||
|
|
classCount[votelabel] = classCount.get(votelabel,0) + 1
|
|||
|
|
# 根据operator.itemgetter(1)-第1个值对classCount排序，然后再取倒序
|
|||
|
|
sortedClassCount = sorted(classCount.items(),key=operator.itemgetter(1), reverse=True)
|
|||
|
|
# 获取数量最多的标签
|
|||
|
|
return sortedClassCount[0][0]
|
|||
|
|
# 载入数据
|
|||
|
|
iris = datasets.load_iris()
|
|||
|
|
# x_train,x_test,y_train,y_test = train_test_split(iris.data, iris.target, test_size=0.完整例子) #分割数据0.2为测试数据，0.8为训练数据
|
|||
|
|
#打乱数据
|
|||
|
|
data_size = iris.data.shape[0]
|
|||
|
|
index = [i for i in range(data_size)]
|
|||
|
|
random.shuffle(index)
|
|||
|
|
iris.data = iris.data[index]
|
|||
|
|
iris.target = iris.target[index]
|
|||
|
|
#切分数据集
|
|||
|
|
test_size = 40
|
|||
|
|
x_train = iris.data[test_size:]
|
|||
|
|
x_test = iris.data[:test_size]
|
|||
|
|
y_train = iris.target[test_size:]
|
|||
|
|
y_test = iris.target[:test_size]
|
|||
|
|
predictions = []
|
|||
|
|
for i in range(x_test.shape[0]):
|
|||
|
|
predictions.append(knn(x_test[i], x_train, y_train, 5))
|
|||
|
|
print(classification_report(y_test, predictions))
|
|||
|
|
print(confusion_matrix(y_test,predictions))
|
|||
|
|
|
|||
|
|
|
|||
|
|
|