generated from LufsX/Experiment-kNN
83 lines
2.6 KiB
Python
83 lines
2.6 KiB
Python
import numpy as np
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import os
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from scipy.spatial.distance import mahalanobis
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from collections import Counter
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# 学号和姓名
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id = "202210423111"
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name = ""
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def classify(input_vector, dataSet, labels, k=1):
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"""
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KNN 分类算法，使用马氏距离。
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"""
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# 计算协方差矩阵及其逆
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cov_matrix = np.cov(dataSet.T)
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if np.linalg.det(cov_matrix) == 0: # 检查奇异矩阵
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cov_matrix += np.eye(cov_matrix.shape[0]) * 1e-8 # 添加正则化
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inv_cov_matrix = np.linalg.inv(cov_matrix)
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# 计算马氏距离
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distances = [mahalanobis(input_vector, point, inv_cov_matrix) for point in dataSet]
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# 获取距离最近的 k 个样本的索引
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k_indices = np.argsort(distances)[:k]
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# 获取最近邻样本的标签
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k_nearest_labels = [labels[i] for i in k_indices]
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# 找出出现次数最多的标签
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most_common = Counter(k_nearest_labels).most_common(1)[0][0]
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return most_common
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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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"""
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features = []
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labels = []
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# 遍历文件夹中的所有文件
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for filename in os.listdir(directory):
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if filename.endswith(".txt"): # 假设文件是 .txt 格式
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filepath = os.path.join(directory, filename)
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try:
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label = int(filename.split('_')[0])
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image_vector = img_to_vector(filepath)
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features.append(image_vector)
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labels.append(label)
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except Exception as e:
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print(f"Error processing file {filepath}: {e}")
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return np.array(features), 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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"""
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try:
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with open(filename) as file:
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lines = file.readlines()
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image_vector = np.array([int(char) for line in lines for char in line.strip()])
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return image_vector
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except Exception as e:
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print(f"Error reading file {filename}: {e}")
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return np.zeros(1024) # 假设有一个预期的向量长度，此处设为1024
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if __name__ == "__main__":
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# 测试
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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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# ... (省略的测试数据)
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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}") |