python实现adaboost

以下是Python实现AdaBoost的示例代码：

import numpy as np

class AdaBoost:
    def __init__(self, n_estimators=50):
        self.n_estimators = n_estimators
        self.alpha = []
        self.estimators = []

    def fit(self, X, y):
        n_samples = X.shape[0]
        weights = np.ones(n_samples) / n_samples

        for i in range(self.n_estimators):
            tree = DecisionTreeClassifier(max_depth=1)
            tree.fit(X, y, sample_weight=weights)
            predictions = tree.predict(X)

            error = weights.dot(predictions != y)
            alpha = 0.5 * np.log((1 - error) / error)

            self.alpha.append(alpha)
            self.estimators.append(tree)

            weights *= np.exp(-alpha * y * predictions)
            weights /= np.sum(weights)

    def predict(self, X):
        predictions = np.zeros(X.shape[0])
        for alpha, tree in zip(self.alpha, self.estimators):
            predictions += alpha * tree.predict(X)
        return np.sign(predictions)

该代码使用scikit-learn中的DecisionTreeClassifier作为基本分类器，使用样本权重来训练每个分类器，并使用指数损失函数来计算每个分类器的权重。最终的预测是每个分类器加权的和，其中权重由每个分类器的权重系数alpha给出。