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/*
Author: Juan Rada-Vilela, Ph.D.
Copyright (C) 2010-2014 FuzzyLite Limited
All rights reserved
This file is part of fuzzylite.
fuzzylite is free software: you can redistribute it and/or modify it under
the terms of the GNU Lesser General Public License as published by the Free
Software Foundation, either version 3 of the License, or (at your option)
any later version.
fuzzylite 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 Lesser General Public License
for more details.
You should have received a copy of the GNU Lesser General Public License
along with fuzzylite. If not, see <http://www.gnu.org/licenses/>.
fuzzylite™ is a trademark of FuzzyLite Limited.
*/
#include "fl/defuzzifier/WeightedAverage.h"
#include "fl/term/Accumulated.h"
#include "fl/term/Activated.h"
#include "fl/norm/Norm.h"
#include "fl/norm/SNorm.h"
#include "fl/norm/TNorm.h"
#include <map>
namespace fl {
WeightedAverage::WeightedAverage(Type type) : WeightedDefuzzifier(type) {
}
WeightedAverage::WeightedAverage(const std::string& type) : WeightedDefuzzifier(type) {
}
WeightedAverage::~WeightedAverage() {
}
std::string WeightedAverage::className() const {
return "WeightedAverage";
}
scalar WeightedAverage::defuzzify(const Term* term,
scalar minimum, scalar maximum) const {
const Accumulated* fuzzyOutput = dynamic_cast<const Accumulated*> (term);
if (not fuzzyOutput) {
std::ostringstream ss;
ss << "[defuzzification error]"
<< "expected an Accumulated term instead of"
<< "<" << term->toString() << ">";
throw fl::Exception(ss.str(), FL_AT);
}
minimum = fuzzyOutput->getMinimum();
maximum = fuzzyOutput->getMaximum();
scalar sum = 0.0;
scalar weights = 0.0;
if (not fuzzyOutput->getAccumulation()) {
Type type = _type;
for (int i = 0; i < fuzzyOutput->numberOfTerms(); ++i) {
Activated* activated = fuzzyOutput->getTerm(i);
scalar w = activated->getDegree();
if (type == Automatic) type = inferType(activated->getTerm());
scalar z = (type == TakagiSugeno)
//? activated.getTerm()->membership(fl::nan) Would ensure no Tsukamoto applies, but Inverse Tsukamoto with Functions would not work.
? activated->getTerm()->membership(w) //Provides Takagi-Sugeno and Inverse Tsukamoto of Functions
: tsukamoto(activated->getTerm(), w, minimum, maximum);
sum += w * z;
weights += w;
}
} else {
typedef std::map<const Term*, std::vector<Activated*> > TermGroup;
TermGroup groups;
for (int i = 0; i < fuzzyOutput->numberOfTerms(); ++i) {
Activated* value = fuzzyOutput->getTerm(i);
const Term* key = value->getTerm();
groups[key].push_back(value);
}
TermGroup::const_iterator it = groups.begin();
Type type = _type;
while (it != groups.end()) {
const Term* activatedTerm = it->first;
scalar accumulatedDegree = 0.0;
for (std::size_t i = 0; i < it->second.size(); ++i)
accumulatedDegree = fuzzyOutput->getAccumulation()->compute(
accumulatedDegree, it->second.at(i)->getDegree());
if (type == Automatic) type = inferType(activatedTerm);
scalar z = (type == TakagiSugeno)
//? activated.getTerm()->membership(fl::nan) Would ensure no Tsukamoto applies, but Inverse Tsukamoto with Functions would not work.
? activatedTerm->membership(accumulatedDegree) //Provides Takagi-Sugeno and Inverse Tsukamoto of Functions
: tsukamoto(activatedTerm, accumulatedDegree, minimum, maximum);
sum += accumulatedDegree * z;
weights += accumulatedDegree;
++it;
}
}
return sum / weights;
}
WeightedAverage* WeightedAverage::clone() const {
return new WeightedAverage(*this);
}
Defuzzifier* WeightedAverage::constructor() {
return new WeightedAverage;
}
}
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