generated from LufsX/Experiment-kNN
164 lines
5.2 KiB
Python
164 lines
5.2 KiB
Python
import numpy as np
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import os
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from sklearn.preprocessing import StandardScaler
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from sklearn.neighbors import KDTree
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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 = "202210423213"
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name = "熊浩燃"
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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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def classify(input_vector, dataSet, labels, k=1, distance_metric='euclidean', weighted=True):
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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 k=1。
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- distance_metric (str): 距离度量方法，默认欧氏距离。
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- weighted (bool): 是否使用加权投票机制，默认加权。
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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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if distance_metric == 'euclidean':
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# 欧氏距离
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distances = np.linalg.norm(dataSet - input_vector, axis=1)
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elif distance_metric == 'manhattan':
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# 曼哈顿距离
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distances = np.sum(np.abs(dataSet - input_vector), axis=1)
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# 使用KD树加速最近邻查找
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tree = KDTree(dataSet)
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dist, indices = tree.query(input_vector.reshape(1, -1), k=k)
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nearest_labels = np.array(labels)[indices[0]]
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if weighted:
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# 使用距离的倒数作为权重
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weights = 1 / (dist[0] + 1e-5)
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label_weights = {}
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for i, label in enumerate(nearest_labels):
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label_weights[label] = label_weights.get(label, 0) + weights[i]
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# 返回加权得分最高的标签
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predicted_label = max(label_weights, key=label_weights.get)
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else:
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# 直接使用多数投票
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predicted_label = np.bincount(nearest_labels).argmax()
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return predicted_label
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def load_dataset(directory):
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"""
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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。
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Args:
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- directory (str): 数据文件夹路径。
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Returns:
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- np.array: 标准化后的特征数据。
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- np.array: 标签数据。
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"""
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labels = []
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data_list = []
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for filename in os.listdir(directory):
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if filename.endswith(".txt"): # 确保读取的是 txt 文件
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label = int(filename.split('_')[0])
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labels.append(label)
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vector = img_to_vector(os.path.join(directory, filename))
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data_list.append(vector)
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data_array = np.array(data_list)
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# 标准化数据
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scaler = StandardScaler()
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scaled_data = scaler.fit_transform(data_array)
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return scaled_data, np.array(labels)
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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。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。
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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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with open(filename, 'r') as f: # 确保使用文本模式读取
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lines = f.readlines()
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vector = np.zeros((1024,), dtype=int) # 将其改为 (1024,) 的一维数组
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for i in range(32):
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for j in range(32):
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vector[32 * i + j] = int(lines[i][j])
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return vector
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if __name__ == "__main__":
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# 测试代码
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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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test_vector = np.array([int(char) for char in test_data]).reshape(1, 1024)
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predicted_label = classify(test_vector, train_data, train_labels)
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print(f"预测的标签: {predicted_label}")
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