import numpy as np
import os

id = "202210218225"
name = "周水源"

def classify(input_vector, dataSet, labels, k=3):
    """
    KNN 分类算法。

    Args:
    - input_vector (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。
    """
    # 计算欧氏距离
    distances = np.linalg.norm(dataSet - input_vector, axis=1)
    # 获取距离最近的k个样本的索引
    nearest_indices = distances.argsort()[:k]
    # 获取这k个样本对应的标签
    nearest_labels = labels[nearest_indices]
    # 统计出现最多的标签
    predicted_label = np.bincount(nearest_labels).argmax()
    return predicted_label

def load_dataset(directory):
    """
    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**。

    Args:
    - directory (str): 数据文件夹路径。

    Returns:
    - np.array: 特征数据。
    - np.array: 标签数据。
    """
    labels = []
    file_list = os.listdir(directory)
    dataSet = np.zeros((len(file_list), 1024))
    
    for i, filename in enumerate(file_list):
        # 获取文件的标签，假设标签是文件名的第一个字符
        label = int(filename.split('_')[0])
        labels.append(label)
        # 将图像转换为向量
        dataSet[i, :] = img_to_vector(os.path.join(directory, filename))
    
    return dataSet, np.array(labels)

def img_to_vector(filename):
    """
    将二值图像转换为向量。

    Args:
    - filename (str): 图像文件路径。

    Returns:
    - np.array: 二值图像向量。
    """
    img_vector = np.zeros((1, 1024))
    with open(filename, 'r') as f:
        for i in range(32):  # 32x32图像
            line = f.readline()
            for j in range(32):
                img_vector[0, 32 * i + j] = int(line[j])
    return img_vector

if __name__ == "__main__":
    # 测试
    train_data, train_labels = load_dataset("trainingDigits")
    test_data = (
        "00000000000000110000000000000000"
        "00000000000011111000000000000000"
        "00000000000011111100000000000000"
        "00000000001111111110000000000000"
        "00000000001111111110000000000000"
        "00000000001111111100000000000000"
        "00000000011111111000000000000000"
        "00000000111111111000000000000000"
        "00000000111111110000000000000000"
        "00000000111111110000000000000000"
        "00000000111111110000000000000000"
        "00000000111111110000000000000000"
        "00000000111111100000000000000000"
        "00000001111111100000000000000000"
        "00000001111111100000000000000000"
        "00000001111111100000000000000000"
        "00000001111111111111000000000000"
        "00000001111111111111111000000000"
        "00000001111111111111111100000000"
        "00000011111111111111111110000000"
        "00000011111111111111111110000000"
        "00000001111111111111111110000000"
        "00000001111111111111111110000000"
        "00000000111111111100111111100000"
        "00000000111111111000111111100000"
        "00000000011111111001111111100000"
        "00000000011111111111111111000000"
        "00000000001111111111111111000000"
        "00000000000111111111111111000000"
        "00000000000011111111111100000000"
        "00000000000001111111110000000000"
        "00000000000000000111100000000000"
    )
    predicted_label = classify(
        np.array([int(char) for char in test_data]).reshape(1, 1024),
        train_data,
        train_labels,
    )

    print(f"预测的标签: {predicted_label}")