generated from LufsX/Experiment-kNN
131 lines
4.6 KiB
Python
131 lines
4.6 KiB
Python
# function.py
|
||
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。
|
||
|
||
"""
|
||
参数说明:
|
||
- 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，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**。
|
||
返回值:
|
||
- int: 预测的分类标签。
|
||
"""
|
||
|
||
|
||
# 这里写自己的代码实现
|
||
def classify(input_vector, dataSet, labels, k=1):
|
||
"""
|
||
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，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**。
|
||
|
||
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_neighbors_indices = distances.argsort()[:k]
|
||
# 获取这些样本的标签
|
||
nearest_labels = np.array(labels)[nearest_neighbors_indices]
|
||
# 统计出现次数最多的标签并返回
|
||
predicted_label = np.bincount(nearest_labels).argmax()
|
||
return predicted_label
|
||
|
||
# 这里也写自己的代码实现
|
||
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 = []
|
||
data_list = []
|
||
for filename in os.listdir(directory):
|
||
if filename.endswith(".txt"): # 确保读取的是 txt 文件
|
||
label = int(filename.split('_')[0])
|
||
labels.append(label)
|
||
vector = img_to_vector(os.path.join(directory, filename))
|
||
data_list.append(vector)
|
||
return np.array(data_list), np.array(labels)
|
||
|
||
# 这里还写自己的代码实现
|
||
def img_to_vector(filename):
|
||
"""
|
||
将二值图像转换为向量。
|
||
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。
|
||
|
||
Args:
|
||
- filename (str): 图像文件路径。
|
||
|
||
Returns:
|
||
- np.array: 二值图像向量。
|
||
"""
|
||
with open(filename, 'r') as f: # 确保使用文本模式读取
|
||
lines = f.readlines()
|
||
vector = np.zeros((1024,), dtype=int) # 将其改为 (1024,) 的一维数组
|
||
for i in range(32):
|
||
for j in range(32):
|
||
vector[32 * i + j] = int(lines[i][j])
|
||
return vector
|
||
|
||
|
||
if __name__ == "__main__":
|
||
# 测试代码
|
||
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"
|
||
)
|
||
|
||
test_vector = np.array([int(char) for char in test_data]).reshape(1, 1024)
|
||
|
||
predicted_label = classify(test_vector, train_data, train_labels)
|
||
print(f"预测的标签: {predicted_label}") |