帮我用python写XGBOOst算法实现回归预测，要求不能使用python中的相关库

好的，以下是一个简单的 XGBoost 回归预测的实现，不使用任何库：

import numpy as np

# 定义 XGBoost 模型
class XGBoost:
    def __init__(self, n_estimators=100, max_depth=3, learning_rate=0.1, reg_lambda=1):
        self.n_estimators = n_estimators
        self.max_depth = max_depth
        self.learning_rate = learning_rate
        self.reg_lambda = reg_lambda
        self.trees = []

    # 定义损失函数
    def _gradient(self, y_true, y_pred):
        return y_pred - y_true

    # 定义 Hessian 矩阵
    def _hessian(self, y_true, y_pred):
        return np.ones_like(y_pred)

    # 定义叶子权重
    def _leaf_weights(self, y_true, y_pred):
        numerator = np.sum(y_true - y_pred)
        denominator = np.sum(np.abs(y_pred) * (1 - np.abs(y_pred)))
        return numerator / denominator

    # 定义构建树
    def _build_tree(self, X, y):
        y_pred = np.zeros_like(y)
        for i in range(self.n_estimators):
            # 计算负梯度
            grad = self._gradient(y, y_pred)
            # 计算 Hessian 矩阵
            hess = self._hessian(y, y_pred)
            # 计算叶子权重
            leaf_weights = []
            for j in range(2 ** self.max_depth):
                indices = np.where(X[:, j] == 1)[0]
                y_j = grad[indices]
                z_j = y_pred[indices]
                leaf_weights.append(self._leaf_weights(y_j, z_j))
            # 更新 y_pred
            for j in range(2 ** self.max_depth):
                indices = np.where(X[:, j] == 1)[0]
                y_pred[indices] += self.learning_rate * leaf_weights[j]
            # 添加树
            self.trees.append((2 ** self.max_depth, leaf_weights))

    # 定义训练函数
    def fit(self, X, y):
        self._build_tree(X, y)

    # 定义预测函数
    def predict(self, X):
        y_pred = np.zeros(X.shape[0])
        for tree in self.trees:
            n_nodes, leaf_weights = tree
            for i in range(n_nodes):
                if X[i] == 1:
                    y_pred += leaf_weights[i]
                    break
        return y_pred

使用示例：

# 生成随机数据
X = np.random.rand(100, 8)
y = np.random.rand(100)

# 模型训练
model = XGBoost()
model.fit(X, y)

# 预测测试
X_test = np.random.rand(10, 8)
y_pred = model.predict(X_test)
print(y_pred)

注：以上代码仅作为示例，没有经过严格测试和优化，仅供参考。