generated from LufsX/Experiment-kNN
152 lines
5.7 KiB
Python
152 lines
5.7 KiB
Python
# function.py
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import numpy as np
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from collections import Counter
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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 = "202210423221"
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name = "廖子豪"
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"""
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参数说明:
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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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返回值:
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- int: 预测的分类标签。
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"""
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# 这里写自己的代码实现
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import numpy as np
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import os
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from collections import Counter
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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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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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with open(filename, 'r') as file:
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lines = file.readlines()
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vector = [int(pixel) for line in lines for pixel in line.strip()]
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return np.array(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。
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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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images = []
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labels = []
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for label_dir in os.listdir(directory):
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label_dir_path = os.path.join(directory, label_dir)
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if os.path.isdir(label_dir_path):
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for filename in os.listdir(label_dir_path):
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file_path = os.path.join(label_dir_path, filename)
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image_vector = img_to_vector(file_path)
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images.append(image_vector)
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labels.append(int(label_dir))
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return np.array(images), np.array(labels)
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def classify(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，默认值为 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((input_vector - dataSet) ** 2, axis=1))
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k_indices = np.argsort(distances)[:k]
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k_nearest_labels = labels[k_indices]
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label_count = Counter(k_nearest_labels)
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return label_count.most_common(1)[0][0]
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if __name__ == "__main__":
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train_dir = "trainingDigits" # 训练集目录
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test_dir = "testDigits" # 测试集目录
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train_images, train_labels = load_dataset(train_dir)
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test_images, test_labels = load_dataset(test_dir)
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print("训练集特征形状：", train_images.shape)
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print("训练集标签形状：", train_labels.shape)
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print("测试集特征形状：", test_images.shape)
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print("测试集标签形状：", test_labels.shape)
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# 测试 classify 函数
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test_data = np.array([int(char) for char in "00000000000000110000000000000000000" # 示例数据
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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"] * 4).reshape(1, 1024)
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predicted_label = classify(test_data, train_images, train_labels)
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print(f"预测的标签: {predicted_label}") |