Profit.hpp

#pragma once
#include <eris/learning/BayesianLinear.hpp>
#include <eris/learning/BayesianLinearRestricted.hpp>
#include <Eigen/Core>
#include <algorithm>
#include <functional>
#include <string>

namespace creativity { namespace belief {

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

        template <typename ...Args>
        explicit Profit(Args &&...args) : BayesianLinearRestricted(std::forward<Args>(args)...)
        {
            // Add restrictions:
            //restrict(1) >= 0; // beta_q >= 0 (higher quality <-> higher profits, at least for low quality)
            //restrict(2) <= 0; // beta_{q^2} <= 0 (quality effect is concave)
            //restrict(3) <= 0; // beta_{marketbooks} <= 0 (more competition <-> lower profit)

            // Set beta names for nicer output
            names({"const", "quality", u8"quality²", "marketBooks"});
        }

        static unsigned int parameters() { return 4; }

        virtual unsigned int fixedModelSize() const override;

        double predict(unsigned int draws, double q, unsigned long previousBooks, unsigned long marketBooks);

        using BayesianLinearRestricted::predict;

    std::pair<double, double> argmaxL(
                unsigned int draws,
                const std::function<double(const double &)> q,
                unsigned long previousBooks, unsigned long marketBooks,
                double l_min,
                double l_max
                );

        static Eigen::RowVectorXd profitRow(double quality, int previous_books, int avg_market_books);

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

}}