generated from LufsX/Experiment-kNN
zhangchenghong
c02a62fa22
Gitea Actions Experiment Test / Experiment-Actions (push) Successful in 11s
179 lines
5.9 KiB
Python
179 lines
5.9 KiB
Python
# function.py
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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 = "202210423215"
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name = "章程鸿·"
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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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# 这里写自己的代码实现
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def classify(input_vector, dataSet, labels, k=2):
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"""
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使用 KNN 算法对输入样本进行分类。
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参数:
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- input_vector (numpy array): 输入待分类的样本（1xN）。
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- dataSet (numpy array): 训练数据集（MxN）。
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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 2。
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返回:
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- int: 预测的分类标签。
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"""
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# 将欧氏距离改为马氏距离
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mean_vector = np.mean(dataSet, axis=0)
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cov_matrix = np.cov(dataSet.T)
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inv_cov_matrix = np.linalg.inv(cov_matrix)
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diff_vector = input_vector - dataSet
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mahalanobis_distances = np.sqrt(np.einsum('ij,jk,ik->i', diff_vector, inv_cov_matrix, diff_vector.T))
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sorted_indices = mahalanobis_distances.argsort()
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k_nearest_distances = mahalanobis_distances[sorted_indices[:k]]
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k_nearest_labels = labels[sorted_indices[:k]]
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# 计算权重（距离的倒数）
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weights = 1 / k_nearest_distances
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# 归一化权重
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weights /= weights.sum()
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# 计算加权投票
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weighted_votes = np.zeros((10,))
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for label, weight in zip(k_nearest_labels, weights):
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weighted_votes[label] += weight
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return np.argmax(weighted_votes)
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# 这里也写自己的代码实现
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def load_dataset(directory):
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"""
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加载数据集中的图像，并将其转换为特征向量和标签。
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参数:
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- directory (str): 数据文件夹路径。
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返回:
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- np.array: 特征数据（Mx1024）。
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- np.array: 标签数据（Mx1）。
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"""
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feature_list = []
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label_list = []
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for file in os.listdir(directory):
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file_path = os.path.join(directory, file)
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feature_vector = img_to_vector(file_path)
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feature_list.append(feature_vector)
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label_list.append(int(file.split('_')[0]))
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feature_matrix = np.array(feature_list)
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label_array = np.array(label_list)
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# 特征缩放（归一化）
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min_value = np.min(feature_matrix)
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max_value = np.max(feature_matrix)
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feature_matrix = (feature_matrix - min_value) / (max_value - min_value)
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return feature_matrix, label_array
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# 这里还写自己的代码实现
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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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# 初始化向量
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vector = np.zeros((1024,)) # 创建一个一维数组
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# 打开文件并逐行读取
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with open(filename) as file:
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for i, line in enumerate(file):
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# 将每行的数字字符转换为整数并存储到向量中
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vector[i*32:i*32+32] = np.array([int(char) for char in line.strip()])
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return vector
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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}")
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