216 lines
7.3 KiB
C++
216 lines
7.3 KiB
C++
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#include "StdAfx.h"
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#include "Vovk.h"
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#include "Dsa.h"
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#include "Fuzzy.h"
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#include "FuzzyWithSets.h"
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#include "NoTrendNoSeasonality.h"
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#include "NoTrendNoSeasonality.h"
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#include "NoTrendAddSeasonality.h"
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#include "NoTrendMultSeasonality.h"
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#include "AddTrendNoSeasonality.h"
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#include "AddTrendAddSeasonality.h"
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#include "AddTrendMultSeasonality.h"
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#include "MultTrendNoSeasonality.h"
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#include "MultTrendAddSeasonality.h"
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#include "MultTrendMultSeasonality.h"
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#include "DATrendNoSeasonality.h"
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#include "DATrendAddSeasonality.h"
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#include "DATrendMultSeasonality.h"
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#include "DMTrendNoSeasonality.h"
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#include "DMTrendAddSeasonality.h"
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#include "DMTrendMultSeasonality.h"
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#include "TrackingControlSignal.h"
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#include <iostream>
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//Set ts at constructor, get first forecast
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//add value(update) ts
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//recalculate weights
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Vovk::Vovk(vector<double> _timeSeria, int _forecastHorizont, Method **m) {
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prep = new Preparator(true, true);
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useSingleTrashhold = false;
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forecastHorizont = 1;
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singleTrashhold = 0.35;
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useModelTrashhold = false;
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modelTrashhold = 0;
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activeExpertsCount = 0;
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beta = 0.5;
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step = 0;
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forecast = 0;
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addWeightFromSleepEx = 0.0;
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timeSeria = _timeSeria;
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calculateTimeSeriaDiff(_timeSeria);
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int n = 29; // <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>, <20><><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><> <20><><EFBFBD><EFBFBD><EFBFBD>
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expertsCount = n;
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forecastHorizont = _forecastHorizont;
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/*int p = 1;
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double alpha = 0.4;
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double delta = 0.9;
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double gamma = 0.4;
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double phi = 0.1;*/
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int countRulesIn = 1;
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int countFuzzyParts = 40;
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models = m;
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//models[0] = new NoTrendNoSeasonality(_timeSeria, _forecastHorizont);
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//models[1] = new NoTrendAddSeasonality(_timeSeria, _forecastHorizont);
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//models[2] = new NoTrendMultSeasonality(_timeSeria, _forecastHorizont);
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//models[3] = new AddTrendNoSeasonality(_timeSeria, _forecastHorizont);
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//models[4] = new MultTrendNoSeasonality(_timeSeria, _forecastHorizont);
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//models[5] = new DATrendNoSeasonality(_timeSeria, _forecastHorizont);
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//models[6] = new DMTrendNoSeasonality(_timeSeria, _forecastHorizont);
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//models[7] = new AddTrendAddSeasonality(_timeSeria, _forecastHorizont);
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//models[8] = new MultTrendAddSeasonality(_timeSeria, _forecastHorizont);
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//models[9] = new DATrendAddSeasonality(_timeSeria, _forecastHorizont);
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//models[10] = new DMTrendAddSeasonality(_timeSeria, _forecastHorizont);
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//models[11] = new AddTrendMultSeasonality(_timeSeria, _forecastHorizont);
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//models[12] = new MultTrendMultSeasonality(_timeSeria, _forecastHorizont);
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//models[13] = new DATrendMultSeasonality(_timeSeria, _forecastHorizont);
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//models[14] = new DMTrendMultSeasonality(_timeSeria, _forecastHorizont);
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//models[15] = new Dsa(_timeSeria, _forecastHorizont);
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//models[16] = new Fuzzy("None", "None", _timeSeria, _forecastHorizont);
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//models[17] = new Fuzzy("Add", "None", _timeSeria, _forecastHorizont);
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//models[18] = new Fuzzy("None", "Add", _timeSeria, _forecastHorizont);
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//models[19] = new Fuzzy("Add", "Add", _timeSeria, _forecastHorizont);
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//models[20] = new FuzzyWithSets("None", "None", _timeSeria, _forecastHorizont);
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//models[21] = new FuzzyWithSets("None", "Add", _timeSeria, _forecastHorizont);
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//models[22] = new FuzzyWithSets("None", "Mult", _timeSeria, _forecastHorizont);
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//models[23] = new FuzzyWithSets("Add", "None", _timeSeria, _forecastHorizont);
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//models[24] = new FuzzyWithSets("Add", "Add", _timeSeria, _forecastHorizont);
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//models[25] = new FuzzyWithSets("Add", "Mult", _timeSeria, _forecastHorizont);
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//models[26] = new FuzzyWithSets("Mult", "None", _timeSeria, _forecastHorizont);
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//models[27] = new FuzzyWithSets("Mult", "Add", _timeSeria, _forecastHorizont);
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//models[28] = new FuzzyWithSets("Mult", "Mult", _timeSeria, _forecastHorizont);
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////add models
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//for (int i = 0; i < 29; i++) {
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// // <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
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// // <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
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// models[i]->setParam("alpha", alpha);
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// models[i]->setParam("gamma", gamma);
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// models[i]->setParam("delta", delta);
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// models[i]->setParam("p", p);
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// models[i]->setParam("phi", phi);
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// models[i]->setParam("countRulesIn", countRulesIn);
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// models[i]->setParam("countFuzzyParts", countFuzzyParts);
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// models[i]->setParam("w", 0.5);
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// // <20> <20><><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>, <20><><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD>
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// // <20><><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
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// // <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD>: <20><><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> = 20, <20><><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD> c = 20
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// // <20><> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD> <20><> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
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// models[i]->setParam("c", 20);
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// // <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
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// models[i]->createModelForEstimation();
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//}
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//push models to Vovks object
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double check = 0;
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//set weights at first time
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for (int i = 0; i < expertsCount; i++)
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{
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models[i]->weight = (1.0 / expertsCount);
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check += models[i]->weight;
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models[i]->value = models[i]->getForecast()[step];
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//cout << models[i]->value << " " << models[i]->weight << " " << forecast << endl;
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}
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//check experts representation
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checkExperts();
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//calculate first forecast
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for (int i = 0; i < expertsCount; i++)
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{
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if (models[i]->isActive)
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forecast += models[i]->value * (models[i]->weight + addWeightFromSleepEx/activeExpertsCount);
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}
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getNewExpertsValues();
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//step++;
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}
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void Vovk::setBeta(double _beta){
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beta = _beta;
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}
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Vovk::~Vovk() {
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}
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void Vovk::calculateTimeSeriaDiff(vector<double> timeSeria){
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double diff = 0.0;
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for (int i = 0; i < timeSeria.size() - 1; i++){
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diff += fabs(timeSeria[i] - timeSeria[i + 1]);
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}
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timeSeriaDiff = (diff / (timeSeria.size() - 1)) * (1 + singleTrashhold);
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}
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void Vovk::checkExperts(){
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TrackingControlSignal *tcs = new TrackingControlSignal();
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activeExpertsCount = expertsCount;
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addWeightFromSleepEx = 0.0;
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vector<bool> expertsActivity = tcs->HistoricalExpertControl(models, timeSeriaDiff, expertsCount);
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for (int i = 0; i < expertsCount; i++)
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{
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models[i]->isActive = expertsActivity[i];
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if (!expertsActivity[i]){
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addWeightFromSleepEx += models[i]->weight;
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activeExpertsCount--;
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}
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}
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}
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void Vovk::getNewExpertsValues(){
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for (int i = 0; i < expertsCount; i++){
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//for 1 scale forecast
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models[i]->value = models[i]->getForecast()[step];
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}
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step++;
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}
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//ret gt(x)
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double Vovk::recalculateWeights(double nextRealValue){
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double weightsSum = 0;
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for (int i = 0; i < expertsCount; i++){
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models[i]->weight = models[i]->weight * pow(beta, fabs(models[i]->value - nextRealValue));
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weightsSum += models[i]->weight;
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}
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//normalize weights
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double check = 0.0;
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for (int i = 0; i < expertsCount; i++){
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models[i]->weight = models[i]->weight / weightsSum;
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check += models[i]->weight;
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}
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return log(weightsSum) / log(beta);//beta, weightsSum);
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}
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void Vovk::updateTS(double nextRealValue){
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//change models weights with new real value
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double gtx = recalculateWeights(nextRealValue);
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forecast = 0.0;
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//create new forecast from experts
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getNewExpertsValues();
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checkExperts();
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//create aggregated forecast
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double check = 0.0;
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for (int i = 0; i < expertsCount; i++)
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{
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/*cout << i << ") ";
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if (models[i]->isActive){
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cout << "true ";
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}
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else{
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cout << "false ";
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}
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cout << " " << models[i]->value << " " << models[i]->weight << endl;*/
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if (models[i]->isActive){
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check += models[i]->weight;
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forecast += models[i]->value * (models[i]->weight + addWeightFromSleepEx / activeExpertsCount);
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}
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}
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}
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double Vovk::getForecast(){
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return forecast;
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}
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//void Vovk::addMethod(AggModel model){
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// models.push_back(model);
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//}
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