63 lines
1.6 KiB
Python
63 lines
1.6 KiB
Python
import matplotlib.pyplot as plt
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import numpy as np
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import operator
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# 已知分类的数据
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x1 = np.array([3,2,1])
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y1 = np.array([104,100,81])
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x2 = np.array([101,99,98])
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y2 = np.array([10,5,2])
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scatter1 = plt.scatter(x1,y1,c='r')
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scatter2 = plt.scatter(x2,y2,c='b')
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# 未知数据
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x = np.array([18])
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y = np.array([90])
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scatter3 = plt.scatter(x,y,c='k')
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#画图例
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plt.legend(handles=[scatter1,scatter2,scatter3],labels=['labelA','labelB','X'],loc='best')
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plt.show()
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# 已知分类的数据
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x_data = np.array([[3,104],
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[2,100],
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[1,81],
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[101,10],
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[99,5],
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[81,2]])
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y_data = np.array(['A','A','A','B','B','B'])
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x_test = np.array([18,90])
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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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x_data_size
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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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diffMat
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# 计算差值的平方
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sqDiffMat = diffMat**2
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sqDiffMat
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# 求和
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sqDistances = sqDiffMat.sum(axis=1)
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sqDistances
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# 开方
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distances = sqDistances**0.5
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distances
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# 从小到大排序
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sortedDistances = distances.argsort()
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sortedDistances
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classCount = {}
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# 设置k
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k = 5
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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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classCount
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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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sortedClassCount
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# 获取数量最多的标签
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knnclass = sortedClassCount[0][0]
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knnclass
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