72 lines
2.1 KiB
Plaintext
72 lines
2.1 KiB
Plaintext
/**
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* @file krls.h
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* @brief krls
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* @author Cat (null.null.null@qq.com)
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* @version 0.1
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* @date 2021-09-17
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*
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* Copyright: Baosight Co. Ltd.
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* DO NOT COPY/USE WITHOUT PERMISSION
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*
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*/
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#pragma once
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#include <eqpalg/regression/train_result.h>
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#include "mix_cc/algorithm/split.h"
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#include <vector>
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#include <memory>
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namespace regression {
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using namespace dlib;
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/**
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* @brief krls 回归预测
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* @tparam rows
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* @tparam cols
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*/
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template <size_t rows = 0, size_t cols = 0>
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class krls_t {
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public:
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using SampleType = matrix<double, rows, cols>;
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using KernelType = radial_basis_kernel<SampleType>;
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using SamplesType = std::vector<SampleType>;
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using LabelsType = std::vector<double>;
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using TrainedResult = TrainResult<krls<KernelType>, rows, cols>;
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private:
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double split_precent_;
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public:
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krls_t() { this->split_precent_ = 0.7; }
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explicit krls_t(double split_percent) {
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this->split_precent_ = split_percent;
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}
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/**
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* @brief 学习样本，产生判断函数
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* @param gama My Param doc
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* @param tr_samples My Param doc
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* @param tr_labels My Param doc
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* @return TrainedResult
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*/
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TrainedResult learn(const double gama, const SamplesType& tr_samples,
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const LabelsType& tr_labels) {
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krls<KernelType> trainer(KernelType(0.1), 0.001);
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size_t split_size = tr_samples.size() * (split_precent_);
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auto splitted_sample = mix_cc::split(tr_samples, {split_size});
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auto splitted_label = mix_cc::split(tr_labels, {split_size});
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auto train_size = splitted_sample[0].size();
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for (size_t i = 0; i < train_size; i++) {
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trainer.train(splitted_sample[0][i], splitted_label[0][i]);
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}
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TrainedResult result;
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result.set_predict_func(std::make_unique<krls<KernelType>>(trainer));
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std::vector<double> loo_values;
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auto valide_size = splitted_sample[1].size();
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for (auto i = 0; i < valide_size; i++) {
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loo_values.push_back(trainer(splitted_sample[1][i]));
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}
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result.err_rate = mean_squared_error(splitted_label[1], loo_values);
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return result;
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}
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};
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} // namespace regression
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