generated from LufsX/Experiment-kNN
130 lines
4.3 KiB
Python
130 lines
4.3 KiB
Python
import numpy as np
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import os
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import time
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def classify(input_vector, dataSet, labels, k=4):
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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 4。
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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的欧式距离
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diffMat = np.tile(input_vector, (dataSet.shape[0], 1)) - dataSet
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sqDiffMat = diffMat**2
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sqDistances = sqDiffMat.sum(axis=1)
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distances = sqDistances**0.5
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# 获取距离最近的 k 个样本的索引
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sortedDistIndices = distances.argsort()
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classCount = {}
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for i in range(k):
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voteIlabel = labels[sortedDistIndices[i]]
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# 统计每个类别的出现次数
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classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1
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# 返回出现次数最多的类别
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sortedClassCount = sorted(classCount.items(), key=lambda x: x[1], reverse=True)
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return sortedClassCount[0][0]
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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。
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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: 特征数据。
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- np.array: 标签数据。
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"""
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fileList = os.listdir(directory)
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m = len(fileList)
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dataSet = np.zeros((m, 1024)) # 28*28的二值图像数据
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labels = []
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for i in range(m):
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filename = fileList[i]
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filePath = os.path.join(directory, filename)
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vector = img_to_vector(filePath)
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dataSet[i, :] = vector
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# 假设文件名的格式为 "label_number.txt"
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label = int(filename.split('_')[0])
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labels.append(label)
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return dataSet, 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。
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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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returnVect = np.zeros((1, 1024))
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with open(filename, 'r') as file:
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lines = file.readlines()
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n = 0
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for line in lines:
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# 去除每行末尾的换行符
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line = line.strip()
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for i in range(len(line)):
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returnVect[0, n] = int(line[i])
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n = n + 1
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if n >= 1024:
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break
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if n >= 1024:
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break
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return returnVect
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if __name__ == "__main__":
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# 加载训练数据集
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train_data, train_labels = load_dataset("trainingDigits")
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# 测试数据
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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_data_vector = np.array([int(char) for char in test_data]).reshape(1, 1024)
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# 使用k=4进行分类
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start_time = time.time()
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predicted_label = classify(test_data_vector, train_data, train_labels, k=4)
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end_time = time.time()
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print(f"k = 4, 预测的标签: {predicted_label}, 执行时间: {end_time - start_time:.2f} 秒") |