generated from LufsX/Experiment-kNN
82 lines
3.0 KiB
Python
82 lines
3.0 KiB
Python
import numpy as np
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import os
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# KNN分类算法
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def classify(input_vector, dataSet, labels, k=3, distance_metric='euclidean', weighted=False):
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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 参数，默认为3。
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- distance_metric (str): 距离度量方法，默认为'euclidean'。可选'manhattan', 'mahalanobis'。
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- weighted (bool): 是否使用加权KNN。默认为False。
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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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distances = np.linalg.norm(dataSet - input_vector, axis=1)
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elif distance_metric == 'manhattan':
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distances = np.sum(np.abs(dataSet - input_vector), axis=1)
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elif distance_metric == 'mahalanobis':
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cov_matrix = np.cov(dataSet.T)
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inv_cov_matrix = np.linalg.inv(cov_matrix)
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delta = dataSet - input_vector
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distances = np.sqrt(np.einsum('ij,jk,ik->i', delta, inv_cov_matrix, delta))
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else:
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raise ValueError("Unsupported distance metric")
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nearest_neighbors_indices = distances.argsort()[:k]
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nearest_labels = np.array(labels)[nearest_neighbors_indices]
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if weighted:
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weights = 1 / (distances[nearest_neighbors_indices] + 1e-5) # 防止除0错误
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weighted_votes = np.bincount(nearest_labels, weights=weights)
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predicted_label = weighted_votes.argmax()
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else:
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predicted_label = np.bincount(nearest_labels).argmax()
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return predicted_label
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# 从文件夹加载数据
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def load_dataset(directory):
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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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return np.array(data_list), np.array(labels)
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# 将二值图像转换为向量
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def img_to_vector(filename):
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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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# 测试代码
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if __name__ == "__main__":
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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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# ... 中间数据省略 ...
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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, k=5, distance_metric='manhattan', weighted=True)
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print(f"预测的标签: {predicted_label}")
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