import numpy as np from sklearn.neighbors import KNeighborsClassifier from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score from Bio import SeqIO

k-mer 方法将 DNA 序列转换为特征向量

def kmer_to_feature(sequence, k=3): features = [] for i in range(len(sequence) - k + 1): kmer = sequence[i:i+k] feature = [0] * 4 ** k if 'N' not in kmer: index = 0 for base in kmer: if base == 'A': index = index * 4 + 0 elif base == 'C': index = index * 4 + 1 elif base == 'G': index = index * 4 + 2 elif base == 'T': index = index * 4 + 3 feature[index] = 1 features.extend(feature) return features

读取 FASTA 文件

def read_fasta_file(file_path, chunk_size=10000): sequences = [] labels = [] for record in SeqIO.parse(file_path, 'fasta'): sequences.append(str(record.seq)) labels.append(record.id) return sequences, labels

读取数据集

sequences, labels = read_fasta_file('jieguo1.fasta') X = [kmer_to_feature(seq) for seq in sequences] y = np.array(labels)

划分训练集和测试集

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=43)

训练和测试模型

k_values = [1, 3, 5, 7, 9] for k in k_values: knn = KNeighborsClassifier(n_neighbors=k) knn.fit(X_train, y_train) y_pred = knn.predict(X_test) accuracy = accuracy_score(y_test, y_pred) print('For k =', k, ', accuracy is', accuracy)

错误分析：MemoryError

出现 'MemoryError' 错误意味着程序在尝试扩展列表 'features' 时遇到了内存不足问题。

这可能是由于 k-mer 方法为每个序列生成了大量特征，而程序无法将所有特征存储在内存中导致的。

解决方法：

1. 使用更有效的特征生成方法，例如稀疏矩阵或哈希表。

2. 减少 k-mer 大小或限制每个序列生成的特征数量。