Demand.hpp

#pragma once
#include <eris/learning/BayesianLinearRestricted.hpp>
#include <eris/SharedMember.hpp>
#include <Eigen/Core>
#include <string>
#include <utility>

namespace creativity { class Book; }

namespace creativity { namespace belief {

class Demand : public eris::learning::BayesianLinearRestricted {
    public:
        Demand() : Demand(parameters()) {}

        template <typename ...Args>
        explicit Demand(Args &&...args) : BayesianLinearRestricted(std::forward<Args>(args)...)
        {
            // Add restrictions:
            restrict(1) <= -0.05; // beta_price <= 0 (higher price <-> lower quantity)
            restrict(2) >= 0.0; // beta_q >= 0
            restrict(4) <= -1.0; // beta_{nosales_periods} <= 0
//            restrict(7) <= 0.0; // beta_{age} <= 0
//            restrict(9) <= 0.0; // beta_{market_size} <= 0

            // Set beta names for nicer output
            names({"const", "price", "quality", /*u8"quality²",*/ "prevSales", "noSales"});//, "I(new)", "age", "otherBooks", "marketBooks"});
        }

        static unsigned int parameters() { return 5; }

        virtual unsigned int fixedModelSize() const override;

        double predict(unsigned int draws, double P, double q, unsigned int S, unsigned int nosales, unsigned int age, unsigned int otherBooks, unsigned int lag_marketBooks);

        using BayesianLinearRestricted::predict;

        static Eigen::RowVectorXd row(double P, double q, unsigned S, unsigned nosales, unsigned age, unsigned otherBooks, unsigned lag_marketBooks);

        std::pair<double, double> argmaxP(unsigned int draws, double q, unsigned int s, unsigned int z, unsigned int n, unsigned int nosales, unsigned int age, unsigned int otherBooks, unsigned int marketBooks, double c, double max_price);

        static Eigen::RowVectorXd bookRow(eris::SharedMember<Book> book, double quality, unsigned int lag_market_books);

        virtual std::string display_name() const override { return "Demand"; }
};

}}