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lzh
2024-10-16 23:44:59 +08:00
parent 60398b3e6b
commit 19665a8133
1 changed files with 61 additions and 125 deletions
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function.py
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@@ -42,35 +42,10 @@ name = "廖子豪"
# 这里写自己的代码实现
def classify(input_vector, dataSet, labels, k=3):
"""
KNN 分类算法。
import numpy as np
import os
from collections import Counter
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。
"""
# 计算输入向量与数据集中每个向量之间的欧几里得距离
distances = np.sqrt(np.sum((input_vector - dataSet) ** 2, axis=1))
# 获取距离最小的k个样本的索引
k_indices = np.argsort(distances)[:k]
# 获取这些样本对应的标签
k_nearest_labels = labels[k_indices]
# 使用Counter统计这些标签中出现次数最多的标签
label_count = Counter(k_nearest_labels)
# 返回出现次数最多的标签
return label_count.most_common(1)[0][0]
# 这里也写自己的代码实现
def img_to_vector(filename):
"""
将二值图像转换为向量。
@@ -81,7 +56,10 @@ def img_to_vector(filename):
Returns:
- np.array: 二值图像向量。
"""
return np.array([int(char) for line in open(filename) for char in line.strip()])
with open(filename, 'r') as file:
lines = file.readlines()
vector = [int(pixel) for line in lines for pixel in line.strip()]
return np.array(vector)
def load_dataset(directory):
"""
@@ -96,30 +74,35 @@ def load_dataset(directory):
"""
images = []
labels = []
# 遍历目录中的所有子目录
for label_dir in os.listdir(directory):
# 构建完整的子目录路径
label_dir_path = os.path.join(directory, label_dir)
# 确保是目录而不是文件
if os.path.isdir(label_dir_path):
# 遍历目录中的所有文件
for filename in os.listdir(label_dir_path):
# 构建完整的文件路径
file_path = os.path.join(label_dir_path, filename)
# 读取文件内容并转换为向量
image_vector = img_to_vector(file_path)
# 添加向量和标签到列表中
images.append(image_vector)
labels.append(int(label_dir))
# 将列表转换为 NumPy 数组
return np.array(images), np.array(labels)
# 示例用法
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。
"""
distances = np.sqrt(np.sum((input_vector - dataSet) ** 2, axis=1))
k_indices = np.argsort(distances)[:k]
k_nearest_labels = labels[k_indices]
label_count = Counter(k_nearest_labels)
return label_count.most_common(1)[0][0]
if __name__ == "__main__":
train_dir = "trainingDigits" # 训练集目录
test_dir = "testDigits" # 测试集目录
@@ -131,86 +114,39 @@ if __name__ == "__main__":
print("训练集标签形状：", train_labels.shape)
print("测试集特征形状：", test_images.shape)
print("测试集标签形状：", test_labels.shape)
# 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。
Args:
- filename (str): 图像文件路径。
Returns:
- np.array: 二值图像向量。
"""
with open(filename, 'r') as file:
# 读取文件的每一行
lines = file.readlines()
# 初始化一个空列表来存储向量
vector = []
# 遍历每一行，将每个字符转换为整数并添加到列表中
for line in lines:
vector.extend([int(pixel) for pixel in line.strip()])
# 将列表转换为 NumPy 数组并返回
return np.array(vector)
# 示例用法
if __name__ == "__main__":
# 假设有一个图像文件路径
filename = "path_to_your_image_file.txt"
# 调用函数并打印结果
image_vector = img_to_vector(filename)
print("图像向量:", image_vector)
print("向量形状:", image_vector.shape)
if __name__ == "__main__":
# 以下代码可用于测试，可自行随意修改
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}")
# 测试 classify 函数
test_data = np.array([int(char) for char in "00000000000000110000000000000000000" # 示例数据
"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"] * 4).reshape(1, 1024)
predicted_label = classify(test_data, train_images, train_labels)
print(f"预测的标签: {predicted_label}")