generated from LufsX/Experiment-kNN
This commit is contained in:
lzh
+61
-125
@@ -42,35 +42,10 @@ name = "廖子豪"
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# 这里写自己的代码实现
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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=3):
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import numpy as np
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"""
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import os
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KNN 分类算法。
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from collections import Counter
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Args:
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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，默认值为 3。
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Returns:
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- int: 预测的分类标签。
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"""
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# 计算输入向量与数据集中每个向量之间的欧几里得距离
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distances = np.sqrt(np.sum((input_vector - dataSet) ** 2, axis=1))
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# 获取距离最小的k个样本的索引
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k_indices = np.argsort(distances)[:k]
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# 获取这些样本对应的标签
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k_nearest_labels = labels[k_indices]
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# 使用Counter统计这些标签中出现次数最多的标签
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label_count = Counter(k_nearest_labels)
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# 返回出现次数最多的标签
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return label_count.most_common(1)[0][0]
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# 这里也写自己的代码实现
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def img_to_vector(filename):
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def img_to_vector(filename):
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"""
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"""
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将二值图像转换为向量。
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将二值图像转换为向量。
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@@ -81,7 +56,10 @@ def img_to_vector(filename):
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Returns:
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Returns:
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- np.array: 二值图像向量。
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- np.array: 二值图像向量。
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"""
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"""
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return np.array([int(char) for line in open(filename) for char in line.strip()])
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with open(filename, 'r') as file:
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lines = file.readlines()
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vector = [int(pixel) for line in lines for pixel in line.strip()]
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return np.array(vector)
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def load_dataset(directory):
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def load_dataset(directory):
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"""
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"""
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@@ -96,30 +74,35 @@ def load_dataset(directory):
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"""
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"""
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images = []
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images = []
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labels = []
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labels = []
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# 遍历目录中的所有子目录
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for label_dir in os.listdir(directory):
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for label_dir in os.listdir(directory):
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# 构建完整的子目录路径
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label_dir_path = os.path.join(directory, label_dir)
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label_dir_path = os.path.join(directory, label_dir)
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# 确保是目录而不是文件
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if os.path.isdir(label_dir_path):
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if os.path.isdir(label_dir_path):
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# 遍历目录中的所有文件
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for filename in os.listdir(label_dir_path):
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for filename in os.listdir(label_dir_path):
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# 构建完整的文件路径
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file_path = os.path.join(label_dir_path, filename)
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file_path = os.path.join(label_dir_path, filename)
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# 读取文件内容并转换为向量
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image_vector = img_to_vector(file_path)
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image_vector = img_to_vector(file_path)
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# 添加向量和标签到列表中
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images.append(image_vector)
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images.append(image_vector)
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labels.append(int(label_dir))
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labels.append(int(label_dir))
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# 将列表转换为 NumPy 数组
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return np.array(images), np.array(labels)
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return np.array(images), np.array(labels)
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# 示例用法
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def classify(input_vector, dataSet, labels, k=3):
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"""
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KNN 分类算法。
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Args:
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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，默认值为 3。
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Returns:
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- int: 预测的分类标签。
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"""
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distances = np.sqrt(np.sum((input_vector - dataSet) ** 2, axis=1))
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k_indices = np.argsort(distances)[:k]
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k_nearest_labels = labels[k_indices]
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label_count = Counter(k_nearest_labels)
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return label_count.most_common(1)[0][0]
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if __name__ == "__main__":
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if __name__ == "__main__":
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train_dir = "trainingDigits" # 训练集目录
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train_dir = "trainingDigits" # 训练集目录
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test_dir = "testDigits" # 测试集目录
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test_dir = "testDigits" # 测试集目录
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@@ -131,86 +114,39 @@ if __name__ == "__main__":
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print("训练集标签形状：", train_labels.shape)
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print("训练集标签形状：", train_labels.shape)
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print("测试集特征形状：", test_images.shape)
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print("测试集特征形状：", test_images.shape)
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print("测试集标签形状：", test_labels.shape)
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print("测试集标签形状：", test_labels.shape)
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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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# 测试 classify 函数
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- filename (str): 图像文件路径。
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test_data = np.array([int(char) for char in "00000000000000110000000000000000000" # 示例数据
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"00000000000011111000000000000000"
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Returns:
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"00000000000011111100000000000000"
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- np.array: 二值图像向量。
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"00000000001111111110000000000000"
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"""
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"00000000001111111110000000000000"
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with open(filename, 'r') as file:
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"00000000001111111100000000000000"
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# 读取文件的每一行
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"00000000011111111000000000000000"
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lines = file.readlines()
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"00000000111111111000000000000000"
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"00000000111111110000000000000000"
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# 初始化一个空列表来存储向量
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"00000000111111110000000000000000"
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vector = []
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"00000000111111110000000000000000"
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"00000000111111110000000000000000"
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# 遍历每一行，将每个字符转换为整数并添加到列表中
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"00000000111111100000000000000000"
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for line in lines:
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"00000001111111100000000000000000"
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vector.extend([int(pixel) for pixel in line.strip()])
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"00000001111111100000000000000000"
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"00000001111111100000000000000000"
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# 将列表转换为 NumPy 数组并返回
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"00000001111111111111000000000000"
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return np.array(vector)
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"00000001111111111111111000000000"
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"00000001111111111111111100000000"
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# 示例用法
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"00000011111111111111111110000000"
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if __name__ == "__main__":
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"00000011111111111111111110000000"
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# 假设有一个图像文件路径
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"00000001111111111111111110000000"
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filename = "path_to_your_image_file.txt"
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"00000001111111111111111110000000"
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"00000000111111111100111111100000"
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# 调用函数并打印结果
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"00000000111111111000111111100000"
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image_vector = img_to_vector(filename)
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"00000000011111111001111111100000"
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print("图像向量:", image_vector)
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"00000000011111111111111111000000"
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print("向量形状:", image_vector.shape)
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"00000000001111111111111111000000"
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"00000000000111111111111111000000"
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"00000000000011111111111100000000"
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if __name__ == "__main__":
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"00000000000001111111110000000000"
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# 以下代码可用于测试，可自行随意修改
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"00000000000000000111100000000000"] * 4).reshape(1, 1024)
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train_data, train_labels = load_dataset("trainingDigits")
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predicted_label = classify(test_data, train_images, train_labels)
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print(f"预测的标签: {predicted_label}")
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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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