generated from LufsX/Challenge-kNN
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+18
-2
@@ -25,8 +25,8 @@ import numpy as np
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"""
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请在这里填写学号和姓名,不填写即成绩无效。
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"""
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id = "202210423xxx"
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name = "test_name"
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id = "202210423111"
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name = "李劲"
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"""
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@@ -54,7 +54,23 @@ def classify0(inX, dataSet, labels, k=0):
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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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inX = np.array(inX)
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dataSet = np.array(dataSet)
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# 计算输入样本与所有训练样本之间的欧几里得距离
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distances = [np.sqrt(np.sum((inX - x_train) ** 2)) for x_train in dataSet]
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# 根据距离对训练样本进行排序,并获取前 k 个最近邻的索引
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k_indices = np.argsort(distances)[:k]
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# 根据索引获取前 k 个最近邻的标签
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k_nearest_labels = [labels[i] for i in k_indices]
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# 使用 Counter 来找出出现次数最多的标签
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most_common = Counter(k_nearest_labels).most_common(1)
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# 返回出现次数最多的标签(即预测的分类标签)
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return most_common[0][0]
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def load_data(file_path):
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"""
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