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