77 lines
1.9 KiB
C++
77 lines
1.9 KiB
C++
#include <eqpalg/oneClassSvm/frame.hpp>
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namespace oneClassSvm {
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/**
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* @brief 训练SVM 将结果赋值给svm_result_ 并返回结果的引用
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* @tparam dims
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* @param tr_samples My Param doc
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* @return TrainResult
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*/
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template <size_t dims>
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TrainResult& Frame<dims>::train_get_df_one_class_svm(SamplesType& tr_samples) {
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svm_result_ = decision_function_get(tr_samples);
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return this->svm_result_;
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}
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/**
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* @brief 预测
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* @param data My Param doc
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* @return double 越小越异常
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*/
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template <size_t dims>
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double Frame<dims>::predict(ExchangedMetaData& data) {
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matrix<double, 0, 1> tmp;
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for (size_t i = 0; i < data.size(); i++) {
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tmp(i) = data[i];
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}
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return svm_result_(tmp);
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}
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/**
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* @brief 载入数据
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* @tparam dims
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* @param data My Param doc
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* @return int
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*/
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template <size_t dims>
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int Frame<dims>::load(const InData& data) {
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SamplesType tr_samples;
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// 如果不存在数据，退出
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if (data.empty()) {
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return -1;
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}
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// 提前保留样本
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tr_samples.reserve(data.size());
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// 把输入转成d—lib样本
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for (const auto& meta_data : data) {
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matrix<double, 0, 1> tmp(dims);
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for (size_t i = 0; i < meta_data.size(); i++) {
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tmp(i) = meta_data[i];
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}
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tr_samples.emplace_back(tmp);
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}
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auto df = train_get_df_one_class_svm(tr_samples);
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double th = 0;
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for (auto x : tr_samples) th += df(x);
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th = th / tr_samples.size();
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standard_error = -fabs(th * 100);
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// standard_error =
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return 0;
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}
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template <size_t dims>
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bool Frame<dims>::valid() {
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if (standard_error < 0) {
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return true;
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}
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return false;
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}
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TrainResult decision_function_get(SamplesType samples)
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{
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svm_one_class_trainer<kernel_type> trainer;
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trainer.set_kernel(kernel_type(4.0));
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return trainer.train(samples);
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}
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} // namespace oneClassSvm
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template class oneClassSvm::Frame<1>;
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template class oneClassSvm::Frame<2>;
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template class oneClassSvm::Frame<3>; |