generated from LufsX/Challenge-kNN
61 lines
1.8 KiB
Python
61 lines
1.8 KiB
Python
import numpy as np
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import os
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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 = "202210423124"
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name = "刘子豪"
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def classify0(input_vector, dataSet, labels, k=3):
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"""
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KNN 分类算法。
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Args:
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- input_vector (numpy array): 输入待分类的样本。
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- dataSet (numpy array): 训练数据集。
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- labels (list or numpy array): 训练集对应的标签。
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- k (int): kNN 算法参数，请**自行设置默认值**。
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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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# 计算输入向量与训练集中每个样本的欧氏距离
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distances = np.linalg.norm(dataSet - input_vector, axis=1)
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# 按照距离从小到大排序并获取前k个距离最小的索引
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sorted_indices = distances.argsort()[:k]
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# 统计前k个最近邻的标签
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nearest_labels = [labels[i] for i in sorted_indices]
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# 返回出现次数最多的标签
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predicted_label = max(set(nearest_labels), key=nearest_labels.count)
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return predicted_label
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def load_data(file_path):
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"""
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从 TSV 文件中加载数据集。
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Args:
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- file_path (str): TSV 文件路径。
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Returns:
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- np.array: 特征数据。
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- np.array: 标签数据。
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"""
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data = np.loadtxt(file_path, delimiter='\t')
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data_set = data[:, :-1] # 假设最后一列是标签
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labels = data[:, -1].astype(int)
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return data_set, labels
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if __name__ == "__main__":
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# 以下代码可用于测试，可自行随意修改
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train_file = "datingTrainSet2.tsv"
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train_data, train_labels = load_data(train_file)
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# 示例测试样本，确保维度与训练数据一致
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test_sample = np.array([65915, 3.868359, 0.128078])
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prediction = classify0(test_sample, train_data, train_labels)
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print(f"预测的分类标签: {prediction}")、 |