Ricardo Montañana Gómez
7b27d5c1f3
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117 lines
3.9 KiB
C++
117 lines
3.9 KiB
C++
#pragma once
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#include "types.hpp"
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#include <nlohmann/json.hpp>
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namespace svm_classifier {
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/**
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* @brief Kernel parameters configuration class
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*
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* This class manages all parameters for SVM kernels including kernel type,
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* regularization parameters, optimization settings, and kernel-specific parameters.
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*/
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class KernelParameters {
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public:
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/**
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* @brief Default constructor with default parameters
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*/
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KernelParameters();
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/**
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* @brief Constructor with JSON configuration
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* @param config JSON configuration object
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*/
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explicit KernelParameters(const nlohmann::json& config);
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/**
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* @brief Set parameters from JSON configuration
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* @param config JSON configuration object
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* @throws std::invalid_argument if parameters are invalid
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*/
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void set_parameters(const nlohmann::json& config);
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/**
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* @brief Get current parameters as JSON
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* @return JSON object with current parameters
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*/
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nlohmann::json get_parameters() const;
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// Kernel type
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void set_kernel_type(KernelType kernel);
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KernelType get_kernel_type() const { return kernel_type_; }
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// Multiclass strategy
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void set_multiclass_strategy(MulticlassStrategy strategy);
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MulticlassStrategy get_multiclass_strategy() const { return multiclass_strategy_; }
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// Common parameters
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void set_C(double c);
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double get_C() const { return C_; }
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void set_tolerance(double tol);
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double get_tolerance() const { return tolerance_; }
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void set_max_iterations(int max_iter);
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int get_max_iterations() const { return max_iterations_; }
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void set_probability(bool probability);
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bool get_probability() const { return probability_; }
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void set_cache_size(double cache_size);
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double get_cache_size() const { return cache_size_; }
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// Kernel-specific parameters
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void set_gamma(double gamma);
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double get_gamma() const { return gamma_; }
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bool is_gamma_auto() const { return gamma_ == -1.0; }
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void set_gamma_auto();
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void set_degree(int degree);
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int get_degree() const { return degree_; }
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void set_coef0(double coef0);
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double get_coef0() const { return coef0_; }
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/**
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* @brief Validate all parameters for consistency
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* @throws std::invalid_argument if parameters are invalid
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*/
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void validate() const;
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/**
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* @brief Get default parameters for a specific kernel type
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* @param kernel Kernel type
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* @return JSON object with default parameters
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*/
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static nlohmann::json get_default_parameters(KernelType kernel);
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/**
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* @brief Reset all parameters to defaults for current kernel type
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*/
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void reset_to_defaults();
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private:
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KernelType kernel_type_; ///< Kernel type
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MulticlassStrategy multiclass_strategy_; ///< Multiclass strategy
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// Common parameters
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double C_; ///< Regularization parameter
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double tolerance_; ///< Convergence tolerance
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int max_iterations_; ///< Maximum iterations (-1 for no limit)
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bool probability_; ///< Enable probability estimates
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double cache_size_; ///< Cache size in MB
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// Kernel-specific parameters
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double gamma_; ///< Gamma parameter (-1 for auto)
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int degree_; ///< Polynomial degree
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double coef0_; ///< Independent term in polynomial/sigmoid
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/**
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* @brief Validate kernel-specific parameters
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* @throws std::invalid_argument if kernel parameters are invalid
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*/
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void validate_kernel_parameters() const;
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};
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} // namespace svm_classifier
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