Remove remaining irrelevant comments from eqpalg (glitch_detection, roller, STA)
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224c2c45c4
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@ -153,7 +153,6 @@ bool GlitchDetection::get_prr() {
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<< ",rulename:" << this->rule_name_
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<< ",get_prr():" << prr_result
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<< ",data_index_:" << data_index_ << std::endl;
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// exp_mpdule_ptr_->print_value();
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}
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return prr_result;
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}
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@ -9,7 +9,6 @@
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#include <utility>
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#include <vector>
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#include "mix_cc/ihyper_db.h"
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// 炉辊组算法无需额外的算法初始化步骤
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int Roller::init() {
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int res = 0;
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try {
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@ -436,8 +436,8 @@ bool STA::reset_data(double range, double init_value) {
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is_need_ = true;
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is_init_ = true;
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is_task_ = true;
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this->range_ = range; ///<间距
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this->init_value_ = init_value; ///< a0,an = a0 + n*range_
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this->range_ = range;
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this->init_value_ = init_value;
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this->dist_data_.clear();
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this->sample_1d_data_.clear();
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logger_->Info() << "STA::reset_data,range:" << range
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@ -457,22 +457,22 @@ int STA::task_update_ci_dist() {
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double dump_size = this->running_stat_.current_n();
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if (dump_size > 3) {
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this->scale_ = (double)this->k_dest_dump_size / dump_size;
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vector<double> data_value; ///<解压缩后的数据
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vector<double> data_value;
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for (auto item : this->sample_1d_data_) {
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int count_now = std::ceil(
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scale_ * item.Count); //根据该条记录的频次 得到解压到数据个数
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scale_ * item.Count);
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for (int i = 0; i < count_now; i++) {
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data_value.push_back(RandMinMax(item.X1 - range_, item.X1 + range_));
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}
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}
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std::sort(data_value.begin(), data_value.end()); ///< 从小到大排序
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std::sort(data_value.begin(), data_value.end());
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logger_->Debug() << "解压缩数据量:" << data_value.size() << ",data[0]"
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<< data_value[0] << ",data[-1]" << *data_value.rbegin()
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<< endl;
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this->dist_1d_.auto_test(this->running_stat_, data_value);
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if (this->dist_1d_.valid()) {
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dist_range_ci_ = this->dist_1d_.get_range();
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res += this->update_t_sample_mag(); ///< 更新 T_SAMPLE_MAG
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res += this->update_t_sample_mag();
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} else {
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logger_->Debug() << "区间不合法!" << endl;
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res = -1;
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