generated from LufsX/Challenge-kNN
61 lines
1.6 KiB
Python
61 lines
1.6 KiB
Python
import numpy as np
|
|
from sklearn.preprocessing import MinMaxScaler
|
|
|
|
"""
|
|
qin
|
|
"""
|
|
id = "202210423206"
|
|
name = "詹宇豪"
|
|
|
|
def classify0(inX, dataSet, labels, k=3):
|
|
"""
|
|
KNN 分类算法。
|
|
|
|
Args:
|
|
- inX (numpy array): 输入待分类的样本。
|
|
- dataSet (numpy array): 训练数据集。
|
|
- labels (list or numpy array): 训练集对应的标签。
|
|
- k (int): KNN 算法参数，默认值为3。
|
|
|
|
Returns:
|
|
- int: 预测的分类标签。
|
|
"""
|
|
# 计算输入样本与训练数据集中每个样本的欧氏距离
|
|
distances = np.sqrt(((dataSet - inX) ** 2).sum(axis=1))
|
|
|
|
# 按距离从小到大排序并获取对应的索引
|
|
sorted_indices = distances.argsort()
|
|
|
|
# 选择距离最近的k个样本
|
|
top_k_labels = labels[sorted_indices[:k]]
|
|
|
|
# 统计k个样本中出现最多的类别
|
|
predicted_label = np.bincount(top_k_labels).argmax()
|
|
|
|
return predicted_label
|
|
|
|
def load_data(file_path):
|
|
"""
|
|
加载 TSV 格式数据文件。
|
|
"""
|
|
data = np.loadtxt(file_path, delimiter="\t")
|
|
features = data[:, :-1]
|
|
labels = data[:, -1].astype(int)
|
|
|
|
# 数据归一化
|
|
scaler = MinMaxScaler()
|
|
features = scaler.fit_transform(features)
|
|
|
|
return features, labels
|
|
|
|
|
|
if __name__ == "__main__":
|
|
# 测试代码，可以修改以便调试
|
|
train_file = "datingTrainSet2.tsv"
|
|
|
|
# 加载训练数据
|
|
train_data, train_labels = load_data(train_file)
|
|
|
|
# 测试 classify0 函数
|
|
result = classify0(np.array([65915, 3.868359, 0.128078]), train_data, train_labels)
|
|
print("Predicted label:", result) |