不使用机器学习包进行adaboost算法的python实现

可以通过以下代码实现不使用机器学习包的Adaboost算法的Python实现：

import numpy as np

# 生成训练数据
def getData():
    X = np.array([
        [-1, -1], 
        [-1, 1], 
        [1, -1], 
        [1, 1]
    ])
    y = np.array([1, 1, -1, -1])
    return X, y

# 实现Adaboost算法
def Adaboost(X, y, T):
    n_samples, n_features = X.shape
    w = np.ones(n_samples) / n_samples
    models = []
    for t in range(T):
        # 训练弱分类器
        model = {'feature': None, 'threshold': None, 'alpha': None}
        min_error = float('inf')
        for f in range(n_features):
            feature_vals = np.unique(X[:, f])
            for threshold in feature_vals:
                predicted = np.ones(n_samples)
                predicted[X[:, f] < threshold] = -1
                error = np.sum(w[y != predicted])
                if error < min_error:
                    min_error = error
                    model['feature'] = f
                    model['threshold'] = threshold
                    model['alpha'] = 0.5 * np.log((1 - min_error) / min_error)
        
        # 更新权重和模型
        predicted = np.ones(n_samples)
        predicted[X[:, model['feature']] < model['threshold']] = -1
        w *= np.exp(-model['alpha'] * y * predicted)
        w /= np.sum(w)
        models.append(model)
        
    return models

# 对测试数据进行预测
def predict(X_test, models):
    n_samples = X_test.shape[0]
    y_pred = np.zeros(n_samples)
    for model in models:
        predicted = np.ones(n_samples)
        predicted[X_test[:, model['feature']] < model['threshold']] = -1
        y_pred += model['alpha'] * predicted
    y_pred = np.sign(y_pred)
    return y_pred

# 测试
X_train, y_train = getData()
models = Adaboost(X_train, y_train, T=3)
X_test = np.array([[-1, -1], [-1, 1], [1, -1], [1, 1]])
y_pred = predict(X_test, models)
print(y_pred)

注意，这只是一个简单的Adaboost实现，仅供参考。在实际应用中，我们通常会使用机器学习包中已经实现好的Adaboost算法，以获得更好的性能和更广泛的应用场景。