tree_node.h

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#ifndef TREE_NODE_H
#define TREE_NODE_H
#include <tuple>
#include <unordered_map>
 
#include "../init.h"
#include "../data/data.h"
#include "node.h"
#include "functions.h"
#include "nodetype.h"
#include "../../thirdparty/tree.hh"
 
using std::string;
using Brush::Data::Dataset;
using Brush::Node;
 
template<>
class tree_node_<Node> { // size: 5*4=20 bytes (on 32 bit arch), can be reduced by 8.
    public:
        tree_node_()
            : parent(0), first_child(0), last_child(0), prev_sibling(0), next_sibling(0)
            {}
 
        tree_node_(const Node& val)
            : parent(0), first_child(0), last_child(0), prev_sibling(0), next_sibling(0), data(val)
            {}
 
        tree_node_(Node&& val)
            : parent(0), first_child(0), last_child(0), prev_sibling(0), next_sibling(0), data(val)
            {}
 
        tree_node_<Node> *parent;
        tree_node_<Node> *first_child, *last_child;
        tree_node_<Node> *prev_sibling, *next_sibling;
        Node data;
 
        template<typename T>
        auto fit(const Dataset& d); 
 
        template<typename T> 
        auto predict(const Dataset& d, const float** weights=nullptr); 
 
        template<typename T, typename W>
        auto predict(const Dataset& d, const W** weights); 
 
        string get_model(bool pretty=false) const;
        string get_tree_model(bool pretty=false, string offset="") const;
 
        int get_complexity() const;
        int get_size(bool include_weight=true) const;
}; 
using TreeNode = class tree_node_<Node>; 
 
// fit, eval, predict
 
#include "dispatch_table.h"
 
template<typename T>
auto TreeNode::fit(const Dataset& d)
{ 
    auto F = dtable_fit.template Get<T>(data.node_type, data.sig_hash);
    return F(d, (*this));
};
 
template<typename T> 
auto TreeNode::predict(const Dataset& d, const float** weights)
{ 
    auto F = dtable_predict.template Get<T>(data.node_type, data.sig_hash);
    return F(d, (*this), weights);
};
 
template<typename T, typename W> 
auto TreeNode::predict(const Dataset& d, const W** weights)
{ 
    auto F = dtable_predict.template Get<T>(data.node_type, data.sig_dual_hash);
    return F(d, (*this), weights);
};
 
// serialization functions
void to_json(json &j, const tree<Node> &t);
void from_json(const json &j, tree<Node> &t);
 
// namespace node{
 
//     template<NodeType NT=0>
//     string get_model(const Node& data, const vector<string>& children)
//     {
//         string args = "";
//         for (int i = 0; i < children.size(); ++i){
//             args += children.at(i);
//             if (i < children.size()-1)
//                 args += ",";
//         }
 
//         return fmt::format("{}({})", data.get_name(), args);
        
//     }
 
//     template<>
//     string get_model<NodeType::SplitBest>(const Node& data, const vector<string>& children)
//     {
//         return fmt::format("IF-THEN-ELSE({}>{:.3f},{},{})",
//             data.get_feature(),
//             data.W,
//             children.at(0),
//             children.at(1)
//             );
        
//     }
 
//     template<>
//     string get_model<NodeType::SplitOn>(const Node& data, const vector<string>& children)
//     {
//         return fmt::format("IF-THEN-ELSE({}>{:.3f},{},{})",
//             children.at(0),
//             data.W,
//             children.at(1),
//             children.at(2)
//             );
        
//     }
// }
#endif