基学习器为lnn的boosting五分类代码

好的，以下是基学习器为LNN的五分类Boosting代码示例（Python实现）：

import numpy as np
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# Sigmoid函数
def sigmoid(x):
    return 1 / (1 + np.exp(-x))

# LNN的定义
class LNN:
    def __init__(self, input_dim, learning_rate=0.01):
        self.weights = np.random.randn(input_dim, 1)
        self.bias = np.random.randn(1)
        self.lr = learning_rate

    # 训练一个基学习器
    def train(self, X_train, y_train, iterations):
        for i in range(iterations):
            # 前向传播
            z = np.dot(X_train, self.weights) + self.bias
            a = sigmoid(z)

            # 反向传播
            dz = a - y_train.reshape(-1, 1)
            dw = np.dot(X_train.T, dz)
            db = np.sum(dz)

            # 更新权重和偏置
            self.weights -= self.lr * dw
            self.bias -= self.lr * db

    # 预测
    def predict(self, X_test):
        z = np.dot(X_test, self.weights) + self.bias
        a = sigmoid(z)
        return np.round(a).astype(int)

# 定义一个Boosting分类器
class BoostingClassifier:
    def __init__(self, base_estimator, n_estimators):
        self.base_estimator = base_estimator
        self.n_estimators = n_estimators
        self.estimators = []

    # 训练模型
    def fit(self, X, y):
        # 初始化权重
        sample_size = X.shape[0]
        weights = np.ones(sample_size) / sample_size

        for i in range(self.n_estimators):
            # 基于样本权重抽取样本
            indices = np.random.choice(sample_size, sample_size, p=weights)

            # 训练一个基学习器
            estimator = self.base_estimator(input_dim=X.shape[1])
            estimator.train(X[indices], y[indices], iterations=100)

            # 记录基学习器，并更新样本权重
            self.estimators.append(estimator)
            y_pred = estimator.predict(X)
            error = np.sum(weights * (y != y_pred))
            alpha = np.log((1 - error) / error) / 2
            weights = weights * np.exp(-alpha * y * y_pred)

    # 预测
    def predict(self, X):
        y_pred = np.zeros(X.shape[0])
        for estimator in self.estimators:
            y_pred += estimator.predict(X).reshape(-1) * alpha
        return np.round(y_pred).astype(int)

# 生成样本数据
X, y = make_classification(n_samples=1000, n_features=20, n_informative=10, n_classes=5, random_state=42)

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 定义基学习器
base_estimator = LNN

# 定义Boosting分类器
clf = BoostingClassifier(base_estimator=base_estimator, n_estimators=100)

# 训练模型
clf.fit(X_train, y_train)

# 预测测试集结果
y_pred = clf.predict(X_test)

# 输出模型准确率
print("Accuracy:", accuracy_score(y_test, y_pred))

在这个例子中，我们使用LNN作为基学习器，并将其传递给Boosting分类器。我们训练了一个100个基学习器的分类器，并在测试集上进行了预测。最后，我们输出了模型的准确率。

同样，这也只是一个简单的示例，你可以根据自己的需求进行修改和调整。