import numpy as np
import os

"""
实验说明:
- 自行编写实现数据加载与 kNN 的手写数字分类算法。
- 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。
- 函数返回: 预测的分类标签。

注意事项:
- 在 function.py 中实现 kNN 分类算法。
- 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。
- 自动评测脚本仅会加载 classify 和 load_data 函数，请勿修改函数名。
- 训练集和测试集需手动下载解压，并放入当前目录下。

测评环境:
- CPU: Intel Xeon(R) CPU E3-1231 v3 @ 3.40GHz
- OS: Ubuntu 22.04.4 LTS
- Python: 3.10.12
- numpy: 2.1.1
"""

"""
请在这里填写学号和姓名，不填写即成绩无效。
"""
id = "202210423203"
name = "赵昊龙"

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。

    Returns:
    - int: 预测的分类标签。
    """
    # 欧式距离
    diffMat = np.tile(input_vector, (dataSet.shape[0], 1)) - dataSet
    sqDiffMat = diffMat**2
    sqDistances = sqDiffMat.sum(axis=1)
    distances = sqDistances**0.5
    sortedDistIndices = distances.argsort()
    
    # 取出距离最近的k个点的标签
    classCount = {}
    for i in range(k):
        voteIlabel = labels[sortedDistIndices[i]]
        # 统计每个类别的出现次数
        classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1
    
    # 选择出现次数最多的类别作为预测标签
    sortedClassCount = sorted(classCount.items(), key=lambda x: x[1], reverse=True)
    return sortedClassCount[0][0]

def load_dataset(directory):
    """
    从文件夹中加载数据。

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

    Returns:
    - np.array: 特征数据。
    - np.array: 标签数据。
    """
    fileList = os.listdir(directory)
    m = len(fileList)
    dataSet = np.zeros((m, 1024))  # 28*28的二值图像数据
    labels = []
    
    for i in range(m):
        filename = fileList[i]
        fileWithPath = os.path.join(directory, filename)
        vector = img_to_vector(fileWithPath)
        dataSet[i, :] = vector
        label = int(filename.split('.')[0])  # 假设文件名的格式为"数字.txt"
        labels.append(label)
    
    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。
    """
    returnVect = np.zeros((1024,))  # 28*28的二值图像数据
    with open(filename, 'r') as file:
        lines = file.readlines()
        n = 0
        for line in lines:
            for i in range(len(line)-1):
                returnVect[n] = int(line[i])
                n = n + 1
    return returnVect

if __name__ == "__main__":
    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}")