请根据cotraining算法自行编写代码，实现半监督学习数字识别任务

好的，我可以为您提供cotraining算法的代码实现。

首先，我们需要导入所需的库和模块：

import numpy as np
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.svm import SVC
from sklearn.naive_bayes import GaussianNB

然后，我们需要加载手写数字数据集，并将其分成训练集和测试集：

digits = load_digits()

X_unlabeled, X_labeled, y_unlabeled, y_labeled = train_test_split(digits.data, digits.target, test_size=0.9, stratify=digits.target, random_state=42)

X_train, X_test, y_train, y_test = train_test_split(X_labeled, y_labeled, test_size=0.5, stratify=y_labeled, random_state=42)

接下来，我们需要定义两个分类器，这里我们采用SVM和朴素贝叶斯分类器：

clf1 = SVC(kernel='linear', random_state=42)
clf2 = GaussianNB()

然后，我们需要定义cotraining算法的实现代码：

def cotraining(X_train, y_train, X_unlabeled, clf1, clf2, n_iter=5, r=0.5, u=10):
    X_labeled = X_train.copy()
    y_labeled = y_train.copy()
    for i in range(n_iter):
        # Train classifiers on labeled data
        clf1.fit(X_labeled, y_labeled)
        clf2.fit(X_labeled, y_labeled)
        
        # Use classifiers to predict labels for unlabeled data
        y_pred1 = clf1.predict(X_unlabeled)
        y_pred2 = clf2.predict(X_unlabeled)
        
        # Select high-confidence predictions
        p1 = clf1.decision_function(X_unlabeled)
        p2 = clf2.predict_proba(X_unlabeled)
        high_confidence1 = np.argsort(-p1)[:int(r * len(X_unlabeled))]
        high_confidence2 = np.argsort(-p2.max(axis=1))[:int(r * len(X_unlabeled))]
        high_confidence = set(high_confidence1).intersection(high_confidence2)
        
        # Add high-confidence predictions to labeled data
        X_labeled = np.concatenate([X_labeled, X_unlabeled[list(high_confidence)]])
        y_labeled = np.concatenate([y_labeled, y_pred1[list(high_confidence)]])
        
        # Remove high-confidence predictions from unlabeled data
        X_unlabeled = np.delete(X_unlabeled, list(high_confidence), axis=0)
        
        # Select random samples from remaining unlabeled data
        random_samples = np.random.choice(len(X_unlabeled), size=u, replace=False)
        
        # Add random samples to labeled data
        X_labeled = np.concatenate([X_labeled, X_unlabeled[random_samples]])
        y_labeled = np.concatenate([y_labeled, y_pred2[random_samples]])
        
        # Remove random samples from unlabeled data
        X_unlabeled = np.delete(X_unlabeled, random_samples, axis=0)
        
    # Train final classifiers on all labeled data
    clf1.fit(X_labeled, y_labeled)
    clf2.fit(X_labeled, y_labeled)
    
    return clf1, clf2

最后，我们需要调用cotraining函数来训练分类器，并用测试集评估分类器的性能：

clf1, clf2 = cotraining(X_train, y_train, X_unlabeled, clf1, clf2)

y_pred1 = clf1.predict(X_test)
y_pred2 = clf2.predict(X_test)

print("SVM Accuracy:", accuracy_score(y_test, y_pred1))
print("Naive Bayes Accuracy:", accuracy_score(y_test, y_pred2))

完整代码如下：