用c++写个决策树处理城区决策

以下是一个简单的使用C++编写的决策树，用于处理城区决策问题的示例代码：

#include <iostream>
#include <string>
#include <vector>

using namespace std;

// 定义决策树节点结构体
struct DecisionTreeNode {
    string label; // 当前节点的标签
    vector<DecisionTreeNode*> children; // 子节点
};

// 定义决策树类
class DecisionTree {
public:
    DecisionTree(DecisionTreeNode* root) : root_(root) {}
    ~DecisionTree() {
        deleteTree(root_);
    }

    // 预测函数，根据输入特征向量预测输出类别
    string predict(const vector<double>& features) {
        return predict(root_, features);
    }

private:
    DecisionTreeNode* root_; // 决策树根节点

    // 递归删除决策树
    void deleteTree(DecisionTreeNode* node) {
        if (node == nullptr) {
            return;
        }
        for (auto child : node->children) {
            deleteTree(child);
        }
        delete node;
    }

    // 递归预测函数
    string predict(DecisionTreeNode* node, const vector<double>& features) {
        if (node->children.empty()) {
            return node->label; // 叶节点，返回类别标签
        }
        int feature_index = stoi(node->label);
        double feature_value = features[feature_index];
        for (auto child : node->children) {
            double threshold = stod(child->label);
            if (feature_value <= threshold) {
                return predict(child, features);
            }
        }
        return predict(node->children.back(), features); // 特殊情况，返回最后一个子节点
    }
};

int main() {
    // 构造一个城区决策树
    DecisionTreeNode* root = new DecisionTreeNode{"0", {}};
    root->children.push_back(new DecisionTreeNode{"1", {}});
    root->children.push_back(new DecisionTreeNode{"4", {}});
    root->children[0]->children.push_back(new DecisionTreeNode{"2", {}});
    root->children[0]->children.push_back(new DecisionTreeNode{"3", {}});
    root->children[0]->children[0]->children.push_back(new DecisionTreeNode{"4", {}});
    root->children[0]->children[0]->children.push_back(new DecisionTreeNode{"5", {}});
    root->children[0]->children[1]->children.push_back(new DecisionTreeNode{"6", {}});
    root->children[0]->children[1]->children.push_back(new DecisionTreeNode{"7", {}});
    root->children[1]->children.push_back(new DecisionTreeNode{"8", {}});
    root->children[1]->children.push_back(new DecisionTreeNode{"9", {}});
    root->children[1]->children[0]->children.push_back(new DecisionTreeNode{"10", {}});
    root->children[1]->children[0]->children.push_back(new DecisionTreeNode{"11", {}});

    // 构造一个输入特征向量
    vector<double> features{1.2, 3.4, 5.6, 7.8, 9.0};

    // 使用决策树进行预测
    DecisionTree dt{root};
    string result = dt.predict(features);
    cout << "Predicted class: " << result << endl;

    return 0;
}

这里的决策树是手动构造的，每个节点的标签都是特征索引或阈值，叶节点的标签是类别标签。在预测时，根据输入特征向量从根节点开始递归遍历决策树，直到达到叶节点并返回类别标签。

在实际应用中，可以使用一些算法（如C4.5、ID3、CART等）来自动构建决策树，从而处理城区决策问题。