generated from LufsX/Experiment-kNN
149 lines
5.0 KiB
Python
149 lines
5.0 KiB
Python
import numpy as np
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import os
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"""
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实验说明:
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- 自行编写实现数据加载与 kNN 的手写数字分类算法。
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- 函数输入: 待分类的样本 input_vector, 训练数据集 dataSet, 训练集对应的标签 labels。
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- 函数返回: 预测的分类标签。
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注意事项:
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- 在 function.py 中实现 kNN 分类算法。
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- 可以自行导入其他库，需要在 requirements.txt 添加依赖，若未在 requirements.txt 中添加使用到的依赖，则无法自动评测，成绩为零。
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- 自动评测脚本仅会加载 classify 和 load_data 函数，请勿修改函数名。
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- 训练集和测试集需手动下载解压，并放入当前目录下。
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测评环境:
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- CPU: Intel Xeon(R) CPU E3-1231 v3 @ 3.40GHz
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- OS: Ubuntu 22.04.4 LTS
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- Python: 3.10.12
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- numpy: 2.1.1
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"""
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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 = "202210423117"
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name = "程悦"
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def classify(input_vector, dataSet, labels, k=2):
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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**，评测时仅会使用其默认值，不修改默认值将**无法通过**评测，**成绩为零**。
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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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dataSet_size = dataSet.shape[0]
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# 计算欧氏距离
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diff_mat = np.tile(input_vector, (dataSet_size, 1)) - dataSet
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sq_diff_mat = diff_mat ** 2
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sq_distances = sq_diff_mat.sum(axis=1)
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distances = np.sqrt(sq_distances)
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# 对距离进行排序
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sorted_indices = distances.argsort()
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class_count = {}
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for i in range(k):
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vote_label = labels[sorted_indices[i]]
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class_count[vote_label] = class_count.get(vote_label, 0) + 1
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sorted_class_count = sorted(class_count.items(), key=lambda x: x[1], reverse=True)
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return sorted_class_count[0][0]
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def img_to_vector(filename):
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"""
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将二值图像转换为向量。
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该部分应为 load_dataset 函数的辅助子函数，可单独在这里实现，也可直接集成至 load_dataset 函数中。
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Args:
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- filename (str): 图像文件路径。
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Returns:
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- np.array: 二值图像向量。
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"""
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return_vector = np.zeros((1, 1024))
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with open(filename) as f:
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for i in range(32):
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line = f.readline()
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for j in range(32):
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return_vector[0, 32 * i + j] = int(line[j])
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return return_vector
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def load_dataset(directory):
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"""
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从文件夹中加载数据，若不自行正确实现，则无法通过评测，**不记录成绩**。
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Args:
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- directory (str): 数据文件夹路径。
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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_set = []
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labels = []
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for root, dirs, files in os.walk(directory):
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for file in files:
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if file.endswith('.txt'):
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label = int(file.split('_')[0])
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labels.append(label)
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data_set.append(img_to_vector(os.path.join(root, file)))
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data_set = np.array(data_set).reshape(len(data_set), -1)
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scaler = StandardScaler()
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data_set = scaler.fit_transform(data_set)
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return data_set, np.array(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_data, train_labels = load_dataset("trainingDigits")
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test_data = (
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"00000000000000110000000000000000"
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"00000000000011111000000000000000"
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"00000000000011111100000000000000"
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"00000000001111111110000000000000"
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"00000000001111111110000000000000"
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"00000000001111111100000000000000"
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"00000000011111111000000000000000"
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"00000000111111111000000000000000"
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"00000000111111110000000000000000"
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"00000000111111110000000000000000"
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"00000000111111110000000000000000"
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"00000000111111110000000000000000"
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"00000000111111100000000000000000"
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"00000001111111100000000000000000"
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"00000001111111100000000000000000"
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"00000001111111100000000000000000"
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"00000001111111111111000000000000"
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"00000001111111111111111000000000"
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"00000001111111111111111100000000"
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"00000011111111111111111110000000"
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"00000011111111111111111110000000"
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"00000001111111111111111110000000"
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"00000001111111111111111110000000"
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"00000000111111111100111111100000"
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"00000000111111111000111111100000"
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"00000000011111111001111111100000"
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"00000000011111111111111111000000"
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"00000000001111111111111111000000"
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"00000000000111111111111111000000"
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"00000000000011111111111100000000"
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"00000000000001111111110000000000"
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"00000000000000000111100000000000"
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)
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predicted_label = classify(
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np.array([int(char) for char in test_data]).reshape(1, 1024),
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train_data,
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train_labels,
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)
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print(f"预测的标签: {predicted_label}") |