generated from LufsX/Challenge-kNN
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+15
-6
@@ -50,15 +50,24 @@ def classify0(inX, dataSet, labels,k=3):
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Returns:
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- int: 预测的分类标签。
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"""
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distances = [np.sqrt(np.sum((inX - x) ** 2)) for x in dataSet]
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dataSetSize = dataSet.shape[0]
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diffMat = np.tile(inX, (dataSetSize, 1)) - dataSet
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sqDiffMat = diffMat ** 2
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sqDistances = sqDiffMat.sum(axis=1)
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distances = sqDistances ** 0.5
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k_indices = np.argsort(distances)[:k]
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k_nearest_labels = [labels[i] for i in k_indices]
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# 对距离进行排序,并获取索引
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sortedDistIndicies = distances.argsort()
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from collections import Counter
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vote_result = Counter(k_nearest_labels).most_common(1)
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# 选择距离最小的k个点
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classCount = {}
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for i in range(k):
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voteIlabel = labels[sortedDistIndicies[i]]
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classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1
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return vote_result[0][0]
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# 多数投票
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sortedClassCount = sorted(classCount.items(), key=lambda item: item[1], reverse=True)
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return sortedClassCount[0][0]
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def load_data(file_path):
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"""
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