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/*
* Errorrate.h
*
* Created on: Jan 22, 2015
* Author: Quentin Marcou
*
* This source code is distributed as part of the IGoR software.
* IGoR (Inference and Generation of Repertoires) is a versatile software to analyze and model immune receptors
* generation, selection, mutation and all other processes.
* Copyright (C) 2017 Quentin Marcou
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
* You should have received a copy of the GNU General Public License
* along with this program. If not, see <https://www.gnu.org/licenses/>.
*/
#ifndef ERRORRATE_H_
#define ERRORRATE_H_
#include "Utils.h"
#include "IntStr.h"
#include <unordered_map>
#include <utility>
#include <string>
#include <fstream>
#include <vector>
#include <stdexcept>
#include <random>
#include <queue>
#include <memory>
//Debug
#include <iostream>
#include <cmath>
//Forward declare Rec_event
class Rec_Event;
/**
* \class Error_rate Error_rate.h
* \brief Abstract class for generic error models behavior.
* \author Q.Marcou
* \version 1.0
*
* Base class for defining different error models such as additive or non-additive hypermutation models.
* Errors are assessed when all RecEvent iterate have been processed (terminal leaf of the scenario tree)
*
*/
class Error_rate {
public:
Error_rate();
virtual ~Error_rate();
virtual double compare_sequences_error_prob( double ,const std::string& , Seq_type_str_p_map& , const Seq_offsets_map& , const std::unordered_map<std::tuple<Event_type,Gene_class,Seq_side>, std::shared_ptr<Rec_Event>>& , Mismatch_vectors_map& , double& , double& )=0;
virtual void update()=0;
virtual void initialize(const std::unordered_map<std::tuple<Event_type,Gene_class,Seq_side>, std::shared_ptr<Rec_Event>>&);
bool is_updated() const {return updated;}
void update_value(bool update_status) {updated = update_status;};
virtual void add_to_norm_counter()=0;
virtual void clean_seq_counters()=0;
void norm_weights_by_seq_likelihood(Marginal_array_p&, const size_t, const double seq_weight=1);
virtual void write2txt(std::ofstream&)=0;
virtual std::shared_ptr<Error_rate> copy() const = 0;
virtual std::string type() const =0;
virtual Error_rate* add_checked(Error_rate*) = 0;
double get_model_likelihood() const{return model_log_likelihood;}
double get_seq_likelihood() const{return seq_likelihood;}
double get_seq_probability() const{return seq_probability;}
double get_seq_mean_error_number() const;
virtual const double& get_err_rate_upper_bound(size_t,size_t) =0;
virtual void build_upper_bound_matrix(size_t,size_t) =0;
virtual int get_number_non_zero_likelihood_seqs() const =0;
virtual std::queue<int> generate_errors(std::string& , std::mt19937_64&) const =0;
void set_viterbi_run(bool viterbi_like){viterbi_run = viterbi_like;}
int debug_number_scenarios;
protected:
bool updated;
long double model_log_likelihood;
int number_seq;
long double seq_likelihood;
double seq_mean_error_number;
long double scenario_new_proba;//TODO rename this guy
long double seq_probability; //Probability of generating one sequence without taking errors into account
bool viterbi_run;
Matrix<double> upper_bound_proba_mat; //Store the value of the error cost of i errors and j no errors
size_t max_err;
size_t max_noerr;
};
void add_to_err_rate(Error_rate*,Error_rate*);
#endif /* ERRORRATE_H_ */
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