adaboost用python实现

以下是adaboost的Python实现示例代码：

import numpy as np

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

    def fit(self, X, y):
        n_samples, n_features = X.shape
        w = np.full(n_samples, (1 / n_samples))

        for i in range(self.n_estimators):
            clf = DecisionTreeClassifier(max_depth=1)
            clf.fit(X, y)
            y_pred = clf.predict(X)

            misclassified = [int(y_pred[j] != y[j]) for j in range(n_samples)]
            error = np.dot(w, misclassified)

            alpha = 0.5 * np.log((1 - error) / error)
            w = np.multiply(w, np.exp([alpha * x for x in misclassified]))
            w = w / sum(w)

            self.estimators.append(clf)
            self.alphas.append(alpha)

    def predict(self, X):
        n_samples = X.shape[0]
        y_pred = np.zeros((n_samples, 1))

        for i, clf in enumerate(self.estimators):
            predictions = clf.predict(X)
            y_pred += self.alphas[i] * np.reshape(predictions, (n_samples, 1))

        y_pred = np.sign(y_pred)
        return y_pred

在此示例代码中，我们使用了Python中的numpy和scikit-learn库。我们首先定义了AdaBoost类，其中n_estimators是弱学习器的数量，estimators是已经拟合的弱学习器的列表，alphas是每个弱学习器的权重。我们在fit方法中拟合AdaBoost模型，使用DecisionTreeClassifier作为基本学习器，max_depth设置为1以保证其为弱学习器。我们计算误差和权重，然后将该弱学习器和其权重添加到列表中。在predict方法中，我们使用已拟合的所有弱学习器的加权和来进行分类预测，并返回预测结果。