#include "svm_classifier/kernel_parameters.hpp"
#include <stdexcept>
#include <cmath>

namespace svm_classifier {

    KernelParameters::KernelParameters()
        : kernel_type_(KernelType::LINEAR)
        , multiclass_strategy_(MulticlassStrategy::ONE_VS_REST)
        , C_(1.0)
        , tolerance_(1e-3)
        , max_iterations_(-1)
        , probability_(false)
        , gamma_(-1.0)  // Auto gamma
        , degree_(3)
        , coef0_(0.0)
        , cache_size_(200.0)
    {
    }

    KernelParameters::KernelParameters(const nlohmann::json& config) : KernelParameters()
    {
        set_parameters(config);
    }

    void KernelParameters::set_parameters(const nlohmann::json& config)
    {
        // Set kernel type first as it affects validation
        if (config.contains("kernel")) {
            if (config["kernel"].is_string()) {
                set_kernel_type(string_to_kernel_type(config["kernel"]));
            } else {
                throw std::invalid_argument("Kernel must be a string");
            }
        }

        // Set multiclass strategy
        if (config.contains("multiclass_strategy")) {
            if (config["multiclass_strategy"].is_string()) {
                set_multiclass_strategy(string_to_multiclass_strategy(config["multiclass_strategy"]));
            } else {
                throw std::invalid_argument("Multiclass strategy must be a string");
            }
        }

        // Set common parameters
        if (config.contains("C")) {
            if (config["C"].is_number()) {
                set_C(config["C"]);
            } else {
                throw std::invalid_argument("C must be a number");
            }
        }

        if (config.contains("tolerance")) {
            if (config["tolerance"].is_number()) {
                set_tolerance(config["tolerance"]);
            } else {
                throw std::invalid_argument("Tolerance must be a number");
            }
        }

        if (config.contains("max_iterations")) {
            if (config["max_iterations"].is_number_integer()) {
                set_max_iterations(config["max_iterations"]);
            } else {
                throw std::invalid_argument("Max iterations must be an integer");
            }
        }

        if (config.contains("probability")) {
            if (config["probability"].is_boolean()) {
                set_probability(config["probability"]);
            } else {
                throw std::invalid_argument("Probability must be a boolean");
            }
        }

        // Set kernel-specific parameters
        if (config.contains("gamma")) {
            if (config["gamma"].is_number()) {
                set_gamma(config["gamma"]);
            } else if (config["gamma"].is_string() && config["gamma"] == "auto") {
                set_gamma(-1.0); // Auto gamma
            } else {
                throw std::invalid_argument("Gamma must be a number or 'auto'");
            }
        }

        if (config.contains("degree")) {
            if (config["degree"].is_number_integer()) {
                set_degree(config["degree"]);
            } else {
                throw std::invalid_argument("Degree must be an integer");
            }
        }

        if (config.contains("coef0")) {
            if (config["coef0"].is_number()) {
                set_coef0(config["coef0"]);
            } else {
                throw std::invalid_argument("Coef0 must be a number");
            }
        }

        if (config.contains("cache_size")) {
            if (config["cache_size"].is_number()) {
                set_cache_size(config["cache_size"]);
            } else {
                throw std::invalid_argument("Cache size must be a number");
            }
        }

        // Validate all parameters
        validate();
    }

    nlohmann::json KernelParameters::get_parameters() const
    {
        nlohmann::json params = {
            {"kernel", kernel_type_to_string(kernel_type_)},
            {"multiclass_strategy", multiclass_strategy_to_string(multiclass_strategy_)},
            {"C", C_},
            {"tolerance", tolerance_},
            {"max_iterations", max_iterations_},
            {"probability", probability_},
            {"cache_size", cache_size_}
        };

        // Add kernel-specific parameters
        switch (kernel_type_) {
            case KernelType::LINEAR:
                // No additional parameters for linear kernel
                break;

            case KernelType::RBF:
                if (is_gamma_auto()) {
                    params["gamma"] = "auto";
                } else {
                    params["gamma"] = gamma_;
                }
                break;

            case KernelType::POLYNOMIAL:
                params["degree"] = degree_;
                if (is_gamma_auto()) {
                    params["gamma"] = "auto";
                } else {
                    params["gamma"] = gamma_;
                }
                params["coef0"] = coef0_;
                break;

            case KernelType::SIGMOID:
                if (is_gamma_auto()) {
                    params["gamma"] = "auto";
                } else {
                    params["gamma"] = gamma_;
                }
                params["coef0"] = coef0_;
                break;
        }

        return params;
    }

    void KernelParameters::set_kernel_type(KernelType kernel)
    {
        kernel_type_ = kernel;

        // Reset kernel-specific parameters to defaults when kernel changes
        auto defaults = get_default_parameters(kernel);

        if (defaults.contains("gamma")) {
            gamma_ = defaults["gamma"];
        }
        if (defaults.contains("degree")) {
            degree_ = defaults["degree"];
        }
        if (defaults.contains("coef0")) {
            coef0_ = defaults["coef0"];
        }
    }

    void KernelParameters::set_C(double c)
    {
        if (c <= 0.0) {
            throw std::invalid_argument("C must be positive (C > 0)");
        }
        C_ = c;
    }

    void KernelParameters::set_gamma(double gamma)
    {
        // Allow negative values for auto gamma (-1.0)
        if (gamma > 0.0 || gamma == -1.0) {
            gamma_ = gamma;
        } else {
            throw std::invalid_argument("Gamma must be positive or -1 for auto");
        }
    }

    void KernelParameters::set_degree(int degree)
    {
        if (degree < 1) {
            throw std::invalid_argument("Degree must be >= 1");
        }
        degree_ = degree;
    }

    void KernelParameters::set_coef0(double coef0)
    {
        coef0_ = coef0;
    }

    void KernelParameters::set_tolerance(double tol)
    {
        if (tol <= 0.0) {
            throw std::invalid_argument("Tolerance must be positive (tolerance > 0)");
        }
        tolerance_ = tol;
    }

    void KernelParameters::set_max_iterations(int max_iter)
    {
        if (max_iter <= 0 && max_iter != -1) {
            throw std::invalid_argument("Max iterations must be positive or -1 for no limit");
        }
        max_iterations_ = max_iter;
    }

    void KernelParameters::set_cache_size(double cache_size)
    {
        if (cache_size < 0.0) {
            throw std::invalid_argument("Cache size must be non-negative");
        }
        cache_size_ = cache_size;
    }

    void KernelParameters::set_probability(bool probability)
    {
        probability_ = probability;
    }

    void KernelParameters::set_multiclass_strategy(MulticlassStrategy strategy)
    {
        multiclass_strategy_ = strategy;
    }

    void KernelParameters::validate() const
    {
        // Validate common parameters
        if (C_ <= 0.0) {
            throw std::invalid_argument("C must be positive");
        }

        if (tolerance_ <= 0.0) {
            throw std::invalid_argument("Tolerance must be positive");
        }

        if (max_iterations_ <= 0 && max_iterations_ != -1) {
            throw std::invalid_argument("Max iterations must be positive or -1");
        }

        if (cache_size_ < 0.0) {
            throw std::invalid_argument("Cache size must be non-negative");
        }

        // Validate kernel-specific parameters
        validate_kernel_parameters();
    }

    void KernelParameters::validate_kernel_parameters() const
    {
        switch (kernel_type_) {
            case KernelType::LINEAR:
                // Linear kernel has no additional parameters to validate
                break;

            case KernelType::RBF:
                if (gamma_ > 0.0 || gamma_ == -1.0) {
                    // Valid gamma (positive or auto)
                } else {
                    throw std::invalid_argument("RBF kernel gamma must be positive or auto (-1)");
                }
                break;

            case KernelType::POLYNOMIAL:
                if (degree_ < 1) {
                    throw std::invalid_argument("Polynomial degree must be >= 1");
                }
                if (gamma_ > 0.0 || gamma_ == -1.0) {
                    // Valid gamma
                } else {
                    throw std::invalid_argument("Polynomial kernel gamma must be positive or auto (-1)");
                }
                // coef0 can be any real number
                break;

            case KernelType::SIGMOID:
                if (gamma_ > 0.0 || gamma_ == -1.0) {
                    // Valid gamma
                } else {
                    throw std::invalid_argument("Sigmoid kernel gamma must be positive or auto (-1)");
                }
                // coef0 can be any real number
                break;
        }
    }

    nlohmann::json KernelParameters::get_default_parameters(KernelType kernel)
    {
        nlohmann::json defaults = {
            {"C", 1.0},
            {"tolerance", 1e-3},
            {"max_iterations", -1},
            {"probability", false},
            {"multiclass_strategy", "ovr"},
            {"cache_size", 200.0}
        };

        switch (kernel) {
            case KernelType::LINEAR:
                defaults["kernel"] = "linear";
                break;

            case KernelType::RBF:
                defaults["kernel"] = "rbf";
                defaults["gamma"] = -1.0; // Auto gamma
                break;

            case KernelType::POLYNOMIAL:
                defaults["kernel"] = "polynomial";
                defaults["degree"] = 3;
                defaults["gamma"] = -1.0; // Auto gamma
                defaults["coef0"] = 0.0;
                break;

            case KernelType::SIGMOID:
                defaults["kernel"] = "sigmoid";
                defaults["gamma"] = -1.0; // Auto gamma
                defaults["coef0"] = 0.0;
                break;
        }

        return defaults;
    }

    void KernelParameters::reset_to_defaults()
    {
        auto defaults = get_default_parameters(kernel_type_);
        set_parameters(defaults);
    }

    void KernelParameters::set_gamma_auto()
    {
        gamma_ = -1.0;
    }

} // namespace svm_classifier