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diff --git a/fuzzylite/src/defuzzifier/WeightedAverageCustom.cpp b/fuzzylite/src/defuzzifier/WeightedAverageCustom.cpp
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+++ b/fuzzylite/src/defuzzifier/WeightedAverageCustom.cpp
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+/*
+ fuzzylite (R), a fuzzy logic control library in C++.
+ Copyright (C) 2010-2017 FuzzyLite Limited. All rights reserved.
+ Author: Juan Rada-Vilela, Ph.D. <jcrada@fuzzylite.com>
+
+ This file is part of fuzzylite.
+
+ fuzzylite is free software: you can redistribute it and/or modify it under
+ the terms of the FuzzyLite License included with the software.
+
+ You should have received a copy of the FuzzyLite License along with
+ fuzzylite. If not, see <http://www.fuzzylite.com/license/>.
+
+ fuzzylite is a registered trademark of FuzzyLite Limited.
+ */
+
+#include "fl/defuzzifier/WeightedAverageCustom.h"
+
+#include "fl/term/Aggregated.h"
+
+#include <map>
+
+namespace fl {
+
+    WeightedAverageCustom::WeightedAverageCustom(Type type) : WeightedDefuzzifier(type) { }
+
+    WeightedAverageCustom::WeightedAverageCustom(const std::string& type) : WeightedDefuzzifier(type) { }
+
+    WeightedAverageCustom::~WeightedAverageCustom() { }
+
+    std::string WeightedAverageCustom::className() const {
+        return "WeightedAverageCustom";
+    }
+
+    Complexity WeightedAverageCustom::complexity(const Term* term) const {
+        Complexity result;
+        result.comparison(3).arithmetic(1).function(1);
+        const Aggregated* fuzzyOutput = dynamic_cast<const Aggregated*> (term);
+        if (fuzzyOutput) {
+            result += term->complexity().arithmetic(3).comparison(2)
+                    .multiply(scalar(fuzzyOutput->numberOfTerms()));
+        }
+        return result;
+    }
+
+    scalar WeightedAverageCustom::defuzzify(const Term* term,
+            scalar minimum, scalar maximum) const {
+        const Aggregated* fuzzyOutput = dynamic_cast<const Aggregated*> (term);
+        if (not fuzzyOutput) {
+            std::ostringstream ss;
+            ss << "[defuzzification error]"
+                    << "expected an Aggregated term instead of"
+                    << "<" << (term ? term->toString() : "null") << ">";
+            throw Exception(ss.str(), FL_AT);
+        }
+
+        if (fuzzyOutput->isEmpty()) return fl::nan;
+
+        minimum = fuzzyOutput->getMinimum();
+        maximum = fuzzyOutput->getMaximum();
+
+        SNorm* aggregation = fuzzyOutput->getAggregation();
+
+        Type type = getType();
+        if (type == Automatic) {
+            type = inferType(&(fuzzyOutput->terms().front()));
+        }
+
+        scalar sum = 0.0;
+        scalar weights = 0.0;
+        const std::size_t numberOfTerms = fuzzyOutput->numberOfTerms();
+        if (type == TakagiSugeno) {
+            //Provides Takagi-Sugeno and Inverse Tsukamoto of Functions
+            scalar w, z, wz;
+            for (std::size_t i = 0; i < numberOfTerms; ++i) {
+                const Activated& activated = fuzzyOutput->getTerm(i);
+                w = activated.getDegree();
+                z = activated.getTerm()->membership(w);
+                const TNorm* implication = activated.getImplication();
+                wz = implication ? implication->compute(w, z) : (w * z);
+                if (aggregation) {
+                    sum = aggregation->compute(sum, wz);
+                    weights = aggregation->compute(weights, w);
+                } else {
+                    sum += wz;
+                    weights += w;
+                }
+            }
+        } else {
+            scalar w, z, wz;
+            for (std::size_t i = 0; i < numberOfTerms; ++i) {
+                const Activated& activated = fuzzyOutput->getTerm(i);
+                w = activated.getDegree();
+                z = activated.getTerm()->tsukamoto(w, minimum, maximum);
+                const TNorm* implication = activated.getImplication();
+                wz = implication ? implication->compute(w, z) : (w * z);
+                if (aggregation) {
+                    sum = aggregation->compute(sum, wz);
+                    weights = aggregation->compute(weights, w);
+                } else {
+                    sum += wz;
+                    weights += w;
+                }
+            }
+        }
+        return sum / weights;
+    }
+
+    WeightedAverageCustom* WeightedAverageCustom::clone() const {
+        return new WeightedAverageCustom(*this);
+    }
+
+    Defuzzifier* WeightedAverageCustom::constructor() {
+        return new WeightedAverageCustom;
+    }
+
+}