├── JNF_NEAT
    ├── trainable.h
    ├── JNF_NEAT.layout
    ├── xor_solver.h
    ├── gene.cpp
    ├── gene.h
    ├── trained_neural_network.h
    ├── trained_neural_network.cpp
    ├── xor_solver.cpp
    ├── main.cpp
    ├── neuron.h
    ├── training_parameters.h
    ├── neuron.cpp
    ├── species.h
    ├── individual.h
    ├── individual.cpp
    ├── neural_network.h
    ├── neural_network_trainer.h
    ├── genome.h
    ├── JNF_NEAT.vcxproj.filters
    ├── species.cpp
    ├── genome.cpp
    ├── neural_network.cpp
    ├── neural_network_trainer.cpp
    └── JNF_NEAT.vcxproj
├── CMakeLists.txt
├── JNF_NEAT.sln
├── README.md
├── .gitignore
└── LICENSE


/JNF_NEAT/trainable.h:
--------------------------------------------------------------------------------
 1 | #pragma once
 2 | #include <vector>
 3 | 
 4 | class ITrainable
 5 | {
 6 | 	public:
 7 | 		virtual ~ITrainable() = default;
 8 | 
 9 | 		virtual void Reset() = 0;
10 | 		virtual void Update(const std::vector<float>& networkOutputs) = 0;
11 | 		virtual int GetOrCalculateFitness() = 0;
12 | 
13 | 		virtual std::vector<float> ProvideNetworkWithInputs() = 0;
14 | };


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/JNF_NEAT/JNF_NEAT.layout:
--------------------------------------------------------------------------------
 1 | <?xml version="1.0" encoding="UTF-8" standalone="yes" ?>
 2 | <CodeBlocks_layout_file>
 3 | 	<ActiveTarget name="Debug x64" />
 4 | 	<File name="specimen.h" open="1" top="1" tabpos="1" split="0" active="1" splitpos="0" zoom_1="0" zoom_2="0">
 5 | 		<Cursor>
 6 | 			<Cursor1 position="68" topLine="0" />
 7 | 		</Cursor>
 8 | 	</File>
 9 | </CodeBlocks_layout_file>
10 | 


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/JNF_NEAT/xor_solver.h:
--------------------------------------------------------------------------------
 1 | #pragma once
 2 | #include "trainable.h"
 3 | class XORSolver : public ITrainable {
 4 | 	public:
 5 | 		virtual void Reset() override;
 6 | 		virtual void Update(const std::vector<float>&  networkOutputs) override;
 7 | 		virtual int GetOrCalculateFitness() override;
 8 | 
 9 | 		virtual std::vector<float> ProvideNetworkWithInputs() override;
10 | 	private:
11 | 		int fitness = 0;
12 | 		std::vector<float> lastInputs;
13 | };


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/JNF_NEAT/gene.cpp:
--------------------------------------------------------------------------------
 1 | #include "gene.h"
 2 | #include <iostream>
 3 | 
 4 | size_t Gene::numberOfExistingGenes = 0U;
 5 | 
 6 | Gene::Gene()
 7 | {
 8 | 	SetRandomWeight();
 9 | 	numberOfExistingGenes++;
10 | 	std::cout << "New Gene created with historical marking: " << historicalMarking << "\t Number of total Genes: " << numberOfExistingGenes << std::endl;
11 | }
12 | 
13 | void Gene::SetRandomWeight()
14 | {
15 | 	weight = (float)(rand() % 10'000) / 10'000.0f;
16 | }
17 | 


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/JNF_NEAT/gene.h:
--------------------------------------------------------------------------------
 1 | #pragma once
 2 | #include "neuron.h"
 3 | 
 4 | struct Gene {
 5 | 	public:
 6 | 		Gene();
 7 | 		~Gene() = default;
 8 | 		Gene(const Gene& other) = default;
 9 |         Gene(Gene&& other) = default;
10 | 		Gene& operator=(const Gene& other) = default;
11 | 
12 |         std::size_t from = 0;
13 | 		std::size_t to = 0;
14 | 		float weight = 0.0f;
15 | 		std::size_t historicalMarking = numberOfExistingGenes;
16 | 		bool isEnabled = true;
17 | 		void SetRandomWeight();
18 | 	private:
19 | 		static std::size_t numberOfExistingGenes;
20 | };


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/JNF_NEAT/trained_neural_network.h:
--------------------------------------------------------------------------------
 1 | #pragma once
 2 | #include "neural_network.h"
 3 | #include <string>
 4 | 
 5 | class TrainedNeuralNetwork {
 6 | 	public:
 7 | 		TrainedNeuralNetwork() = default;
 8 | 		explicit TrainedNeuralNetwork(const NeuralNetwork& trainedNetwork);
 9 | 		TrainedNeuralNetwork(const TrainedNeuralNetwork& other) = default;
10 | 		~TrainedNeuralNetwork() = default;
11 | 
12 | 		void LoadFromFile(const std::string& fileName);
13 | 		void SaveToFile(const std::string& fileName) const;
14 | 
15 | 		std::vector<float> GetOutputs(const std::vector<float>& inputs);
16 | 
17 | 	private:
18 | 		NeuralNetwork trainedNetwork;
19 | };


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/JNF_NEAT/trained_neural_network.cpp:
--------------------------------------------------------------------------------
 1 | #include "trained_neural_network.h"
 2 | 
 3 | TrainedNeuralNetwork::TrainedNeuralNetwork(const NeuralNetwork& trainedNetwork):
 4 | 	trainedNetwork(trainedNetwork)
 5 | {
 6 | }
 7 | 
 8 | std::vector<float> TrainedNeuralNetwork::GetOutputs(const std::vector<float>& inputs)
 9 | {
10 | 	trainedNetwork.SetInputs(inputs);
11 | 	return trainedNetwork.GetOutputs();
12 | }
13 | 
14 | void TrainedNeuralNetwork::LoadFromFile(const std::string& fileName)
15 | {
16 | 	// TODO jnf
17 | 	// Implementation
18 | }
19 | 
20 | void TrainedNeuralNetwork::SaveToFile(const std::string& fileName) const
21 | {
22 | 	// TODO jnf
23 | 	// Implementation
24 | }
25 | 


--------------------------------------------------------------------------------
/JNF_NEAT/xor_solver.cpp:
--------------------------------------------------------------------------------
 1 | #include "xor_solver.h"
 2 | #include <stdlib.h>
 3 | 
 4 | void XORSolver::Reset()
 5 | {
 6 | 	fitness = 0;
 7 | }
 8 | 
 9 | void XORSolver::Update(const std::vector<float>&  networkOutputs)
10 | {
11 | 	int xorResult = (int)lastInputs[0] ^ (int)lastInputs[1];
12 | 	int networksXorResult = int(networkOutputs[0] > 0.5f);
13 | 
14 | 	if (xorResult == networksXorResult) {
15 | 		fitness += 10;
16 | 	}
17 | }
18 | 
19 | int XORSolver::GetOrCalculateFitness()
20 | {
21 | 	return fitness;
22 | }
23 | 
24 | std::vector<float> XORSolver::ProvideNetworkWithInputs()
25 | {
26 | 	lastInputs.clear();
27 | 	lastInputs.push_back(float(rand() % 2));
28 | 	lastInputs.push_back(float(rand() % 2));
29 | 
30 | 	return lastInputs;
31 | }
32 | 


--------------------------------------------------------------------------------
/JNF_NEAT/main.cpp:
--------------------------------------------------------------------------------
 1 | #include "neural_network_trainer.h"
 2 | #include "xor_solver.h"
 3 | #include <iostream>
 4 | #include <time.h>
 5 | 
 6 | int main() {
 7 |     srand((unsigned)time(0U));
 8 | 	std::cout << "Hello World" << std::endl;
 9 | 
10 | 	TrainingParameters params;
11 | 	params.numberOfInputs = 2;
12 | 	params.numberOfOutputs = 1;
13 | 	params.updatesPerGeneration = 10;
14 | 
15 | 	std::vector<ITrainable*> population;
16 | 	for (int i = 0; i < 20; ++i) {
17 | 		population.push_back(new XORSolver());
18 | 	}
19 | 	NeuralNetworkTrainer trainer(population, params);
20 | 	trainer.TrainUntilFitnessEquals(100);
21 | 	auto& champ = trainer.GetFittestSpecimen();
22 | 
23 |     for (auto& individuum : population) {
24 |         delete individuum;
25 |     }
26 | 
27 | 	return 0;
28 | }


--------------------------------------------------------------------------------
/CMakeLists.txt:
--------------------------------------------------------------------------------
 1 | cmake_minimum_required(VERSION 3.5)
 2 | project(JNF_NEAT)
 3 | 
 4 | set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++14")
 5 | 
 6 | set(SOURCE_FILES
 7 |     JNF_NEAT/main.cpp
 8 |     JNF_NEAT/gene.h
 9 |     JNF_NEAT/gene.cpp
10 |     JNF_NEAT/neural_network.h
11 |     JNF_NEAT/neural_network.cpp
12 |     JNF_NEAT/neural_network_trainer.h
13 |     JNF_NEAT/neural_network_trainer.cpp
14 |     JNF_NEAT/neuron.h
15 |     JNF_NEAT/neuron.cpp
16 |     JNF_NEAT/trainable.h
17 |     JNF_NEAT/xor_solver.h
18 |     JNF_NEAT/xor_solver.cpp
19 |     JNF_NEAT/individual.h
20 |     JNF_NEAT/individual.cpp
21 |     JNF_NEAT/species.h
22 |     JNF_NEAT/species.cpp
23 |     JNF_NEAT/genome.h
24 |     JNF_NEAT/genome.cpp)
25 | 
26 | add_definitions(-std=c++14)
27 | add_executable(JNF_NEAT ${SOURCE_FILES})


--------------------------------------------------------------------------------
/JNF_NEAT/neuron.h:
--------------------------------------------------------------------------------
 1 | #pragma once
 2 | #include <vector>
 3 | 
 4 | class Neuron {
 5 | 	public: 
 6 | 		struct IncomingConnection {
 7 | 			Neuron* incoming = nullptr;
 8 | 			float weight = 1.0f;
 9 | 		};
10 | 	private:
11 | 		using Connections = std::vector<IncomingConnection>;
12 | 		Connections connections;
13 | 		bool isSensor = false;
14 | 		float lastActionPotential = 0.0f;
15 | 
16 |     public:
17 |         Neuron() = default;
18 | 		explicit Neuron(const Connections& connections);
19 |         Neuron(const Neuron& other) = default;
20 |         ~Neuron() = default;
21 | 
22 | 		void SetInput(float input);
23 | 		void AddConnection(const IncomingConnection& connection);
24 | 		void AddConnection(IncomingConnection&& connection);
25 | 		float RequestDataAndGetActionPotential();
26 | 
27 | 	private:
28 | 		static float sigmoid(float d);
29 | };


--------------------------------------------------------------------------------
/JNF_NEAT/training_parameters.h:
--------------------------------------------------------------------------------
 1 | #pragma once
 2 | #include <limits>
 3 | struct TrainingParameters {
 4 | 	unsigned int numberOfInputs = 0U;
 5 | 	unsigned int numberOfOutputs = 0U;
 6 | 	unsigned int updatesPerGeneration = 1U;
 7 | 
 8 | 	struct Advanced {
 9 |         struct Ranges {
10 |             int minFitness = std::numeric_limits<int>::min();
11 |             int maxFitness = 100;
12 |             float minNeuralCharge = 0.0f;
13 |             float maxNeuralCharge = 1.0f;
14 |         } ranges;
15 |         struct Mutation {
16 |             float chanceForWeightMutation = 0.8f;
17 |             float chanceForConnectionalMutation = 0.05f;
18 |             float chanceForNeuralMutation = 0.03f;
19 |         } mutation;
20 |         struct Speciation {
21 |             float importanceOfDisjointGenes = 1.0f;
22 |             float importanceOfAverageWeightDifference = 0.3f;
23 | 			float compatibilityThreshold = 3.0f;
24 |         } speciation;
25 | 	} advanced;
26 | };


--------------------------------------------------------------------------------
/JNF_NEAT/neuron.cpp:
--------------------------------------------------------------------------------
 1 | #include "neuron.h"
 2 | #include <cmath>
 3 | 
 4 | Neuron::Neuron(const Connections& connections) :
 5 | connections(connections) {
 6 | 
 7 | }
 8 | 
 9 | void Neuron::AddConnection(const Neuron::IncomingConnection& connection)
10 | {
11 | 	connections.push_back(connection);
12 | }
13 | 
14 | void Neuron::AddConnection(Neuron::IncomingConnection&& connection)
15 | {
16 | 	connections.push_back(connection);
17 | }
18 | 
19 | float Neuron::RequestDataAndGetActionPotential() {
20 |     if (isSensor){
21 |         return lastActionPotential;
22 |     }
23 | 
24 |     float incomingPotentials = 0.0f;
25 |     for (auto& in : connections){
26 |         incomingPotentials += in.incoming->RequestDataAndGetActionPotential() * in.weight;
27 |     }
28 |     lastActionPotential = sigmoid(incomingPotentials);
29 |     return lastActionPotential;
30 | }
31 | 
32 | float Neuron::sigmoid(float d) {
33 |     return (float)tanh(d);
34 | }
35 | 
36 | void Neuron::SetInput(float input) {
37 |     isSensor = true;
38 |     lastActionPotential = input;
39 | }


--------------------------------------------------------------------------------
/JNF_NEAT/species.h:
--------------------------------------------------------------------------------
 1 | #pragma once
 2 | #include "training_parameters.h"
 3 | #include "neural_network.h"
 4 | 
 5 | class Species {
 6 | 	private:
 7 | 		const TrainingParameters& parameters;
 8 | 		std::vector<NeuralNetwork*> population;
 9 | 		NeuralNetwork representative;
10 | 	public:
11 | 		Species() = delete;
12 | 		explicit Species(const TrainingParameters& parameters);
13 | 		Species(const Species& other) = default;
14 | 		Species(Species&& other) = default;
15 | 		~Species() = default;
16 | 
17 | 		void AddIndividual(NeuralNetwork& individual);
18 | 		void SetPopulation(std::vector<NeuralNetwork>& population);
19 | 
20 | 		bool IsCompatible(const NeuralNetwork& network) const;
21 | 		bool IsCompatible(const Genome& genome) const;
22 | 
23 | 		float GetFitnessSharingModifier() const;
24 | 
25 | 	private:
26 | 		double GetGeneticalDistance(const Genome& leftGenome, const Genome& rightGenome) const;
27 | 		void ElectRepresentative();
28 | 		template<class T>
29 | 		constexpr bool IsAboveCompatibilityThreshold(T t) const;
30 | };


--------------------------------------------------------------------------------
/JNF_NEAT/individual.h:
--------------------------------------------------------------------------------
 1 | #pragma once
 2 | #include "neural_network.h"
 3 | #include "trainable.h"
 4 | #include "training_parameters.h"
 5 | #include "species.h"
 6 | #include "genome.h"
 7 | 
 8 | class Individual {
 9 | 	private:
10 | 		ITrainable* trainable = nullptr;
11 | 		NeuralNetwork network;
12 | 		int fitness = 0;
13 | 		bool isFitnessUpToDate = false;
14 | 		const Species * species;
15 | 
16 | 	public:
17 | 		Individual(ITrainable* trainable, const TrainingParameters& parameters);
18 | 		Individual(ITrainable* trainable, NeuralNetwork network);
19 | 		Individual(const Individual& other) = default;
20 | 		Individual(Individual&& other) = default;
21 | 		~Individual() = default;
22 | 
23 | 		Individual& operator=(const Individual& other) = default;
24 | 
25 | 
26 | 		void Reset() { trainable->Reset(); }
27 | 		void Update();
28 | 		void ModifyFitness(float factor);
29 | 		int GetOrCalculateFitness();
30 | 		void CoupleWithSpecies(Species& species);
31 | 		const Genome& GetGenome() const { return network.GetGenome(); }
32 | };


--------------------------------------------------------------------------------
/JNF_NEAT/individual.cpp:
--------------------------------------------------------------------------------
 1 | #include "individual.h"
 2 | 
 3 | Individual::Individual(ITrainable* trainable, const TrainingParameters& parameters) :
 4 | 	trainable(trainable),
 5 | 	network(parameters)
 6 | {
 7 | }
 8 | 
 9 | Individual::Individual(ITrainable* trainable, NeuralNetwork network) :
10 | 	trainable(trainable),
11 | 	network(network)
12 | {
13 | }
14 | 
15 | void Individual::Update()
16 | {
17 | 	network.SetInputs(trainable->ProvideNetworkWithInputs());
18 | 	trainable->Update(network.GetOutputs());
19 | 	isFitnessUpToDate = false;
20 | }
21 | 
22 | void Individual::ModifyFitness(float factor)
23 | {
24 | 	fitness = (int)((float)fitness * factor);
25 | }
26 | 
27 | int Individual::GetOrCalculateFitness()
28 | {
29 | 	if (!isFitnessUpToDate) {
30 | 		fitness = (int)((float)trainable->GetOrCalculateFitness() * species->GetFitnessSharingModifier());
31 | 		isFitnessUpToDate = true;
32 | 	}
33 | 	return fitness;
34 | }
35 | 
36 | void Individual::CoupleWithSpecies(Species& species)
37 | {
38 | 	species.AddIndividual(network);
39 | 	this->species = &species;
40 | }


--------------------------------------------------------------------------------
/JNF_NEAT/neural_network.h:
--------------------------------------------------------------------------------
 1 | #pragma once
 2 | #include "gene.h"
 3 | #include "neuron.h"
 4 | #include "genome.h"
 5 | #include <vector>
 6 | 
 7 | class NeuralNetwork {
 8 | 	private:
 9 | 		Genome genome;
10 | 		std::vector<Neuron> neurons;
11 | 		std::vector<Neuron*> inputNeurons;
12 | 		std::vector<Neuron*> outputNeurons;
13 | 		const TrainingParameters& parameters;
14 | 
15 | 	public:
16 | 		explicit NeuralNetwork(const TrainingParameters & parameters);
17 |         explicit NeuralNetwork(const TrainingParameters & parameters, const Genome& genome);
18 |         explicit NeuralNetwork(const TrainingParameters & parameters, Genome&& genome);
19 | 		explicit NeuralNetwork(const TrainingParameters & parameters, const Genome&& genome) = delete;
20 | 
21 | 		NeuralNetwork(const NeuralNetwork& other);
22 | 		NeuralNetwork(NeuralNetwork&& other);
23 | 
24 | 		~NeuralNetwork() = default;
25 | 
26 | 		NeuralNetwork& operator= (const NeuralNetwork& other);
27 | 
28 | 		const Genome& GetGenome() const { return genome; }
29 | 
30 | 		void SetInputs(const std::vector<float>& inputs);
31 | 		std::vector<float> GetOutputs();
32 | 
33 | 	private:
34 | 		void ReadNumberOfInputsAndOutputsFromGenes();
35 | 		void BuildNetworkFromGenes();
36 | 		void InterpretInputsAndOutputs();
37 | 		void DeleteAllNeurons();
38 | };


--------------------------------------------------------------------------------
/JNF_NEAT/neural_network_trainer.h:
--------------------------------------------------------------------------------
 1 | #pragma once
 2 | #include "neural_network.h"
 3 | #include "trainable.h"
 4 | #include "training_parameters.h"
 5 | #include "genome.h"
 6 | #include "individual.h"
 7 | #include "species.h"
 8 | #include <vector>
 9 | 
10 | class NeuralNetworkTrainer {
11 | 	private:
12 | 		std::vector <Individual> population;
13 | 		std::vector <Species> species;
14 | 		TrainingParameters parameters;
15 | 
16 | 	// Methods
17 | 	public:
18 | 		NeuralNetworkTrainer() = delete;
19 |         explicit NeuralNetworkTrainer(std::vector<ITrainable*>& population, const TrainingParameters& parameters);
20 |         explicit NeuralNetworkTrainer(std::vector<ITrainable*>& population, TrainingParameters&& parameters);
21 | 
22 | 		NeuralNetworkTrainer(const NeuralNetworkTrainer& other) = default;
23 | 
24 |         ~NeuralNetworkTrainer() = default;
25 | 
26 | 
27 | 		void TrainUntilFitnessEquals(int fitnessToReach);
28 | 		void TrainUntilGenerationEquals(unsigned int generationsToTrain);
29 | 
30 | 		Individual& GetFittestSpecimen();
31 | 
32 | 	private:
33 | 		void SetPopulation(std::vector<ITrainable*>& population);
34 | 		NeuralNetwork Breed(ITrainable* mother, ITrainable* father) const;
35 | 		
36 | 		void ResetPopulation();
37 | 		void Repopulate();        
38 | 		void CategorizePopulationIntoSpecies();
39 | 		void LetGenerationLive();
40 | };


--------------------------------------------------------------------------------
/JNF_NEAT.sln:
--------------------------------------------------------------------------------
 1 | 
 2 | Microsoft Visual Studio Solution File, Format Version 12.00
 3 | # Visual Studio 14
 4 | VisualStudioVersion = 14.0.24720.0
 5 | MinimumVisualStudioVersion = 10.0.40219.1
 6 | Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "JNF_NEAT", "JNF_NEAT\JNF_NEAT.vcxproj", "{5D622DAF-A669-4390-B6DC-8BD3C9BAF04C}"
 7 | EndProject
 8 | Global
 9 | 	GlobalSection(SolutionConfigurationPlatforms) = preSolution
10 | 		Debug|x64 = Debug|x64
11 | 		Debug|x86 = Debug|x86
12 | 		Release|x64 = Release|x64
13 | 		Release|x86 = Release|x86
14 | 	EndGlobalSection
15 | 	GlobalSection(ProjectConfigurationPlatforms) = postSolution
16 | 		{5D622DAF-A669-4390-B6DC-8BD3C9BAF04C}.Debug|x64.ActiveCfg = Debug|x64
17 | 		{5D622DAF-A669-4390-B6DC-8BD3C9BAF04C}.Debug|x64.Build.0 = Debug|x64
18 | 		{5D622DAF-A669-4390-B6DC-8BD3C9BAF04C}.Debug|x86.ActiveCfg = Debug|Win32
19 | 		{5D622DAF-A669-4390-B6DC-8BD3C9BAF04C}.Debug|x86.Build.0 = Debug|Win32
20 | 		{5D622DAF-A669-4390-B6DC-8BD3C9BAF04C}.Release|x64.ActiveCfg = Release|x64
21 | 		{5D622DAF-A669-4390-B6DC-8BD3C9BAF04C}.Release|x64.Build.0 = Release|x64
22 | 		{5D622DAF-A669-4390-B6DC-8BD3C9BAF04C}.Release|x86.ActiveCfg = Release|Win32
23 | 		{5D622DAF-A669-4390-B6DC-8BD3C9BAF04C}.Release|x86.Build.0 = Release|Win32
24 | 	EndGlobalSection
25 | 	GlobalSection(SolutionProperties) = preSolution
26 | 		HideSolutionNode = FALSE
27 | 	EndGlobalSection
28 | EndGlobal
29 | 


--------------------------------------------------------------------------------
/JNF_NEAT/genome.h:
--------------------------------------------------------------------------------
 1 | #pragma once
 2 | 
 3 | #include "gene.h"
 4 | #include "training_parameters.h"
 5 | #include <vector>
 6 | 
 7 | class Genome {
 8 | 	// Members
 9 | 	private:
10 | 		std::vector<Gene> genes;
11 | 		const TrainingParameters& parameters;
12 | 
13 | 	// Methods
14 | 	public:
15 | 		Genome() = delete;
16 | 		explicit Genome(const TrainingParameters& parameters);
17 | 		Genome(const Genome& other) = default;
18 | 		Genome(Genome&& other) = default;
19 | 		~Genome() = default;
20 | 
21 | 		Genome& operator=(const Genome& other);
22 | 		Gene& operator[](std::size_t index) { return genes[index]; }
23 | 		auto begin() { return genes.begin(); }
24 | 		auto end() { return genes.end(); }
25 | 		
26 | 		std::size_t ExtrapolateNeuronCount() const;
27 | 		std::size_t GetGeneCount() const;
28 | 		void MutateGenes();
29 | 
30 | 	private:
31 | 		static bool DidChanceOccure(float chance);
32 | 		bool ShouldAddNeuron() const { return DidChanceOccure(parameters.advanced.mutation.chanceForNeuralMutation); }
33 | 		bool ShouldAddConnection() const { return DidChanceOccure(parameters.advanced.mutation.chanceForConnectionalMutation); }
34 | 		bool ShouldMutateWeight() const { return DidChanceOccure(parameters.advanced.mutation.chanceForWeightMutation); }
35 | 		void AddRandomNeuron();
36 | 		void AddRandomConnection();
37 | 		void ShuffleWeights();
38 | 		void MutateWeightOfGeneAt(std::size_t index);
39 | 		void PerturbWeightAt(std::size_t index);
40 | };


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/JNF_NEAT/JNF_NEAT.vcxproj.filters:
--------------------------------------------------------------------------------
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17 |   <ItemGroup>
18 |     <ClCompile Include="main.cpp">
19 |       <Filter>Source Files</Filter>
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34 |       <Filter>Source Files</Filter>
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39 |     <ClCompile Include="species.cpp">
40 |       <Filter>Source Files</Filter>
41 |     </ClCompile>
42 |     <ClCompile Include="individual.cpp">
43 |       <Filter>Source Files</Filter>
44 |     </ClCompile>
45 |     <ClCompile Include="genome.cpp">
46 |       <Filter>Source Files</Filter>
47 |     </ClCompile>
48 |   </ItemGroup>
49 |   <ItemGroup>
50 |     <ClInclude Include="neural_network.h">
51 |       <Filter>Header Files</Filter>
52 |     </ClInclude>
53 |     <ClInclude Include="neural_network_trainer.h">
54 |       <Filter>Header Files</Filter>
55 |     </ClInclude>
56 |     <ClInclude Include="gene.h">
57 |       <Filter>Header Files</Filter>
58 |     </ClInclude>
59 |     <ClInclude Include="neuron.h">
60 |       <Filter>Header Files</Filter>
61 |     </ClInclude>
62 |     <ClInclude Include="trainable.h">
63 |       <Filter>Header Files</Filter>
64 |     </ClInclude>
65 |     <ClInclude Include="trained_neural_network.h">
66 |       <Filter>Header Files</Filter>
67 |     </ClInclude>
68 |     <ClInclude Include="training_parameters.h">
69 |       <Filter>Header Files</Filter>
70 |     </ClInclude>
71 |     <ClInclude Include="xor_solver.h">
72 |       <Filter>Header Files</Filter>
73 |     </ClInclude>
74 |     <ClInclude Include="species.h">
75 |       <Filter>Header Files</Filter>
76 |     </ClInclude>
77 |     <ClInclude Include="individual.h">
78 |       <Filter>Header Files</Filter>
79 |     </ClInclude>
80 |     <ClInclude Include="genome.h">
81 |       <Filter>Header Files</Filter>
82 |     </ClInclude>
83 |   </ItemGroup>
84 | </Project>


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/JNF_NEAT/species.cpp:
--------------------------------------------------------------------------------
 1 | #include "species.h"
 2 | #include <algorithm>
 3 | 
 4 | Species::Species(const TrainingParameters& parameters) :
 5 | 	parameters(parameters),
 6 | 	representative(parameters)
 7 | {
 8 | }
 9 | 
10 | void Species::AddIndividual(NeuralNetwork& individual)
11 | {
12 | 	population.push_back(&individual);
13 | 	ElectRepresentative();
14 | }
15 | 
16 | void Species::SetPopulation(std::vector<NeuralNetwork>& population)
17 | {
18 | 	this->population.empty();
19 | 	this->population.reserve(population.size());
20 | 	for (auto& individual : population) {
21 | 		this->population.push_back(&individual);
22 | 	}
23 | 	ElectRepresentative();
24 | }
25 | 
26 | bool Species::IsCompatible(const NeuralNetwork& network) const {
27 | 	return IsCompatible(network.GetGenome());
28 | }
29 | 
30 | bool Species::IsCompatible(const Genome& genome) const {
31 | 	auto distanceToSpecies = GetGeneticalDistance(representative.GetGenome(), genome);
32 | 	return !IsAboveCompatibilityThreshold(distanceToSpecies);
33 | }
34 | 
35 | float Species::GetFitnessSharingModifier() const{
36 | 	unsigned int fitnessSharingDivisor = 0;
37 | 
38 | 	for (auto& lhs : population) {
39 | 		for (auto& rhs : population) {
40 | 			auto distance = GetGeneticalDistance(lhs->GetGenome(), rhs->GetGenome());
41 | 			if (IsAboveCompatibilityThreshold(distance)) {
42 | 				++fitnessSharingDivisor;
43 | 			}
44 | 		}
45 | 	}
46 | 
47 | 	float fitnessSharingFactor = 1.0f / (float) fitnessSharingDivisor;
48 | 	return fitnessSharingFactor;
49 | }
50 | 
51 | double Species::GetGeneticalDistance(const Genome& leftGenome, const Genome& rightGenome) const
52 | {
53 | 	double totalWeightDifference = 0.0;
54 | 	size_t numberOfOverlapingGenes = 0;
55 | 
56 | 	size_t sizeOfSmallerGenome = std::min(leftGenome.GetGeneCount(), rightGenome.GetGeneCount());
57 | 	auto areSame = [&](size_t i) {
58 | 		return const_cast<Genome&>(leftGenome)[i].historicalMarking == const_cast<Genome&>(rightGenome)[i].historicalMarking; 
59 | 	};
60 | 
61 | 	for (size_t i = 0; i < sizeOfSmallerGenome && areSame(i); ++i) {
62 | 		totalWeightDifference += 
63 | 			(double)std::abs(const_cast<Genome&>(leftGenome)[i].weight - const_cast<Genome&>(rightGenome)[i].weight);
64 | 
65 | 		++numberOfOverlapingGenes;
66 | 	}
67 | 
68 | 	auto numberOfDisjointGenes = leftGenome.GetGeneCount() + rightGenome.GetGeneCount() - (size_t)2 * numberOfOverlapingGenes;
69 | 	auto sizeOfBiggerGenome = std::max(leftGenome.GetGeneCount(), rightGenome.GetGeneCount());
70 | 	auto disjointGenesInfluence = (double)numberOfDisjointGenes / (double)sizeOfBiggerGenome;
71 | 
72 | 	auto averageWeightDifference = totalWeightDifference / (double)numberOfOverlapingGenes;
73 | 
74 | 	disjointGenesInfluence *= (double)parameters.advanced.speciation.importanceOfDisjointGenes;
75 | 	averageWeightDifference *= (double)parameters.advanced.speciation.importanceOfAverageWeightDifference;
76 | 
77 | 	return disjointGenesInfluence + averageWeightDifference;
78 | }
79 | 
80 | void Species::ElectRepresentative()
81 | {
82 | 	auto randomMember = rand() % population.size();
83 | 	representative = *population[randomMember];
84 | }
85 | 
86 | template <class T>
87 | constexpr bool Species::IsAboveCompatibilityThreshold(T t) const
88 | {
89 | 	return t > parameters.advanced.speciation.compatibilityThreshold;
90 | }
91 | 


--------------------------------------------------------------------------------
/JNF_NEAT/genome.cpp:
--------------------------------------------------------------------------------
  1 | #include "genome.h"
  2 | #include <vector>
  3 | #include <algorithm>
  4 | 
  5 | Genome::Genome(const TrainingParameters& parameters) :
  6 | 	parameters(parameters)
  7 | {
  8 | 	genes.resize(parameters.numberOfInputs * parameters.numberOfOutputs);
  9 | 	auto *currentGene = &genes.front();
 10 | 	for (auto in = 0U; in < parameters.numberOfInputs; ++in) {
 11 | 		for (auto out = 0U; out < parameters.numberOfOutputs; ++out) {
 12 | 			currentGene->from = in;
 13 | 			currentGene->to = out + parameters.numberOfInputs;
 14 | 			++currentGene;
 15 | 		}
 16 | 	}
 17 | }
 18 | 
 19 | std::size_t Genome::ExtrapolateNeuronCount() const {
 20 | 	auto CompareToNeuron = [](const Gene& lhs, const Gene& rhs) {
 21 | 		return lhs.to < rhs.to;
 22 | 	};
 23 | 	auto maxNeuronGene = std::max_element(genes.begin(), genes.end(), CompareToNeuron);
 24 | 	// TODO jnf
 25 | 	// Maybe add lookup table
 26 | 	return maxNeuronGene->to + 1U;
 27 | }
 28 | 
 29 | std::size_t Genome::GetGeneCount() const {
 30 | 	return genes.size();
 31 | }
 32 | 
 33 | Genome& Genome::operator=(const Genome& other)
 34 | {
 35 | 	this->genes = other.genes; 
 36 | 	const_cast<TrainingParameters&>(this->parameters) = other.parameters;
 37 | 	return *this;
 38 | }
 39 | 
 40 | void Genome::MutateGenes() {
 41 | 	if (ShouldAddConnection()) {
 42 | 		AddRandomConnection();
 43 | 	}
 44 | 	else
 45 | 	if (ShouldAddNeuron()) {
 46 | 		AddRandomNeuron();
 47 | 	}
 48 | 	else {
 49 | 		ShuffleWeights();
 50 | 	}
 51 | }
 52 | 
 53 | bool Genome::DidChanceOccure(float chance)
 54 | {
 55 | 	auto num = rand() % 100;
 56 | 	return num < int(100.0f * chance);
 57 | }
 58 | 
 59 | void Genome::AddRandomNeuron()
 60 | {
 61 | 	Gene* randGene = nullptr;
 62 | 	do {
 63 | 		int num = rand() % genes.size();
 64 | 		randGene = &genes[num];
 65 | 	} while (!randGene->isEnabled);
 66 | 
 67 | 	auto numberOfNeurons = ExtrapolateNeuronCount();
 68 | 
 69 | 	Gene g1(*randGene);
 70 | 	g1.to = numberOfNeurons;
 71 | 	genes.push_back(std::move(g1));
 72 | 
 73 | 	Gene g2(*randGene);
 74 | 	g2.from = numberOfNeurons;
 75 | 	genes.push_back(std::move(g2));
 76 | 
 77 | 	randGene->isEnabled = false;
 78 | }
 79 | 
 80 | void Genome::AddRandomConnection()
 81 | {
 82 | 	auto GetRandomNumberBetween = [](size_t min, size_t max) {
 83 | 		return rand() % (max - min) + min;
 84 | 	};
 85 | 
 86 | 	Gene newConnection;
 87 | 	auto numberOfNeurons = ExtrapolateNeuronCount() - 1U;
 88 | 
 89 | 	newConnection.from = GetRandomNumberBetween(0U, numberOfNeurons - 1U);
 90 | 	newConnection.to = GetRandomNumberBetween(newConnection.from + 1, numberOfNeurons);
 91 | 
 92 | 	genes.push_back(newConnection);
 93 | }
 94 | 
 95 | void Genome::ShuffleWeights()
 96 | {
 97 | 	for (size_t i = 0; i < genes.size(); i++) {
 98 | 		if (ShouldMutateWeight()) {
 99 | 			MutateWeightOfGeneAt(i);
100 | 		}
101 | 	}
102 | }
103 | 
104 | void Genome::MutateWeightOfGeneAt(size_t index)
105 | {
106 | 	constexpr float chanceOfTotalWeightReset = 0.1f;
107 | 	if (DidChanceOccure(chanceOfTotalWeightReset)) {
108 | 		genes[index].SetRandomWeight();
109 | 	}
110 | 	else {
111 | 		PerturbWeightAt(index);
112 | 	}
113 | }
114 | 
115 | void Genome::PerturbWeightAt(size_t index)
116 | {
117 | 	constexpr float perturbanceBoundaries = 0.2f;
118 | 	auto perturbance = (float)(rand() % 10'000) / 10'000.0f * perturbanceBoundaries;
119 | 	if (rand() % 2) {
120 | 		perturbance = -perturbance;
121 | 	}
122 | 
123 | 	genes[index].weight *= perturbance;
124 | }
125 | 
126 | 


--------------------------------------------------------------------------------
/JNF_NEAT/neural_network.cpp:
--------------------------------------------------------------------------------
  1 | #include "neural_network.h"
  2 | #include <cmath>
  3 | #include <stdexcept>
  4 | 
  5 | 
  6 | NeuralNetwork::NeuralNetwork(const TrainingParameters & parameters):
  7 | 	parameters(parameters),
  8 | 	genome(parameters),
  9 | 	inputNeurons(parameters.numberOfInputs),
 10 | 	outputNeurons(parameters.numberOfOutputs)
 11 | {
 12 | 	BuildNetworkFromGenes();
 13 | }
 14 | 
 15 | NeuralNetwork::NeuralNetwork(const TrainingParameters & parameters, const Genome& genome):
 16 | 	parameters(parameters),
 17 |     genome(genome),
 18 | 	inputNeurons(parameters.numberOfInputs),
 19 | 	outputNeurons(parameters.numberOfOutputs)
 20 | {
 21 |     BuildNetworkFromGenes();
 22 | }
 23 | 
 24 | NeuralNetwork::NeuralNetwork(const TrainingParameters & parameters, Genome&& genome):
 25 | 	parameters(parameters),
 26 | 	genome(genome),
 27 | 	inputNeurons(parameters.numberOfInputs),
 28 | 	outputNeurons(parameters.numberOfOutputs)
 29 | {
 30 | 	BuildNetworkFromGenes();
 31 | }
 32 | 
 33 | NeuralNetwork::NeuralNetwork(const NeuralNetwork& other) :
 34 | 	parameters(other.parameters),
 35 | 	genome(other.genome),
 36 | 	neurons(other.neurons),
 37 | 	inputNeurons(other.inputNeurons.size()),
 38 | 	outputNeurons(other.outputNeurons.size())
 39 | {
 40 | 	InterpretInputsAndOutputs();
 41 | }
 42 | 
 43 | NeuralNetwork::NeuralNetwork(NeuralNetwork&& other) :
 44 | 	parameters(std::move(other.parameters)),
 45 | 	genome(std::move(other.genome)),
 46 | 	neurons(std::move(other.neurons)),
 47 | 	inputNeurons(std::move(other.inputNeurons.size())),
 48 | 	outputNeurons(std::move(other.outputNeurons.size()))
 49 | {
 50 | 	InterpretInputsAndOutputs();
 51 | }
 52 | 
 53 | NeuralNetwork& NeuralNetwork::operator=(const NeuralNetwork& other)
 54 | {
 55 | 	genome = other.genome;
 56 | 	neurons = other.neurons;
 57 | 	inputNeurons.resize(other.inputNeurons.size());
 58 | 	outputNeurons.resize(other.outputNeurons.size());
 59 | 
 60 | 	InterpretInputsAndOutputs();
 61 | 	return *this;
 62 | }
 63 | 
 64 | 
 65 | void NeuralNetwork::BuildNetworkFromGenes() {
 66 |     DeleteAllNeurons();
 67 | 
 68 |     neurons.resize(genome.ExtrapolateNeuronCount());
 69 |     for (const auto& gene : genome) {
 70 |         if (gene.isEnabled) {
 71 |             Neuron::IncomingConnection connection;
 72 |             connection.incoming = &neurons[gene.from];
 73 |             connection.weight = gene.weight;
 74 |             neurons[gene.to].AddConnection(std::move(connection));
 75 |         }
 76 |     }
 77 | 
 78 |     InterpretInputsAndOutputs();
 79 | }
 80 | 
 81 | void NeuralNetwork::SetInputs(const std::vector<float>& inputs)
 82 | {
 83 | 	if (inputNeurons.size() != inputs.size())
 84 | 	{
 85 | 		throw std::out_of_range("Number of inputs provided doesn't match genetic information");
 86 | 	}
 87 | 	for(size_t i = 0U; i < inputNeurons.size(); ++i){
 88 | 		inputNeurons[i]->SetInput(inputs[i]);
 89 | 	};
 90 | }
 91 | 
 92 | std::vector<float> NeuralNetwork::GetOutputs()
 93 | {
 94 |     std::vector<float> outputs(outputNeurons.size());
 95 | 	for(size_t i = 0U; i < outputs.size(); ++i){
 96 | 		outputs[i] = outputNeurons[i]->RequestDataAndGetActionPotential();
 97 | 	}
 98 | 	return outputs;
 99 | }
100 | 
101 | void NeuralNetwork::DeleteAllNeurons() {
102 | 	neurons.clear();
103 | 	for (auto in : inputNeurons) {
104 | 		in = nullptr;
105 | 	}
106 | 	for (auto out : outputNeurons) {
107 | 		out = nullptr;
108 | 	}
109 | }
110 | 
111 | void NeuralNetwork::InterpretInputsAndOutputs()
112 | {
113 | 	for (auto i = 0U; i < parameters.numberOfInputs; i++) {
114 | 		inputNeurons[i] = &neurons[i];
115 | 	}
116 | 	for (auto i = 0U; i < parameters.numberOfOutputs; i++) {
117 | 		outputNeurons[i] = &neurons[genome[i * parameters.numberOfOutputs].to];
118 | 	}
119 | }


--------------------------------------------------------------------------------
/JNF_NEAT/neural_network_trainer.cpp:
--------------------------------------------------------------------------------
  1 | #include <algorithm>
  2 | #include <stdexcept>
  3 | #include "neural_network_trainer.h"
  4 | 
  5 | NeuralNetworkTrainer::NeuralNetworkTrainer(std::vector<ITrainable*>& population, const TrainingParameters& parameters) :
  6 | 	parameters(parameters)
  7 | {
  8 | 	SetPopulation(population);
  9 | }
 10 | 
 11 | NeuralNetworkTrainer::NeuralNetworkTrainer(std::vector<ITrainable*>& population, TrainingParameters&& parameters) :
 12 |     parameters(parameters)
 13 | {
 14 | 	SetPopulation(population);
 15 | }
 16 | 
 17 | NeuralNetwork NeuralNetworkTrainer::Breed(ITrainable* mother, ITrainable* father) const
 18 | {
 19 | 	Genome childGenome(parameters);
 20 | 
 21 | 	if (mother->GetOrCalculateFitness() == father->GetOrCalculateFitness()) {
 22 | 		// TODO jnf
 23 | 		// Do Stuff with the genes
 24 | 	} else {
 25 | 		// TODO jnf
 26 | 		// Do Stuff with the genes
 27 | 	}
 28 | 
 29 | 	childGenome.MutateGenes();
 30 | 
 31 | 	NeuralNetwork child(parameters, std::move(childGenome));
 32 | 	// It may look ineffective to return this by value, accessing the copy constructor
 33 | 	// But don't worry, RVO will take care of this.
 34 | 	// If your compiler doesn't optimize this, I'd recommend using what you'd call an "out parameter" in C#
 35 | 	return child;
 36 | }
 37 | 
 38 | 
 39 | 
 40 | void NeuralNetworkTrainer::ResetPopulation()
 41 | {
 42 | 	for (auto& individuum : population) {
 43 | 		individuum.Reset();
 44 | 	}
 45 | }
 46 | 
 47 | void NeuralNetworkTrainer::SetPopulation(std::vector<ITrainable*>& population)
 48 | {
 49 |     this->population.clear();
 50 | 	this->population.reserve(population.size());
 51 |     for (auto& currTrainable : population) {
 52 | 		// TODO jnf
 53 | 		// Every Individual generates Genes with historical markings up to population.size() * parameters.numberOfInputs * parameters.NumberOfOutputs
 54 | 		// Instead, the historical markings should only go to parameters.numberOfInputs * parameters.NumberOfOutputs
 55 | 		// and be identical to every other individuals genes (but still having random weights)
 56 | 		this->population.push_back({ currTrainable, parameters });
 57 |     }
 58 | 	CategorizePopulationIntoSpecies();
 59 | }
 60 | 
 61 | 
 62 | void NeuralNetworkTrainer::TrainUntilFitnessEquals(int fitnessToReach) {
 63 | 	LetGenerationLive();
 64 | 	while (GetFittestSpecimen().GetOrCalculateFitness() < fitnessToReach) {
 65 | 		Repopulate();
 66 | 		ResetPopulation();
 67 | 		for (unsigned int i = 0; i < parameters.updatesPerGeneration; ++i) {
 68 | 			LetGenerationLive();
 69 | 		}
 70 | 	}
 71 | }
 72 | 
 73 | void NeuralNetworkTrainer::TrainUntilGenerationEquals(unsigned int generationsToTrain) {
 74 | 	for(auto generation = 0U; generation < generationsToTrain; generation++){
 75 | 		Repopulate();
 76 | 		LetGenerationLive();
 77 | 	}
 78 | }
 79 | 
 80 | Individual& NeuralNetworkTrainer::GetFittestSpecimen() {
 81 | 	if (population.empty()) {
 82 | 		throw std::out_of_range("Your population is empty");
 83 | 	}
 84 | 
 85 | 	auto compareFitness = [](Individual& lhs, Individual& rhs) {
 86 | 		return lhs.GetOrCalculateFitness() < rhs.GetOrCalculateFitness();
 87 | 	};
 88 | 	// TODO jnf
 89 | 	// cache this
 90 | 	return *std::max_element(population.begin(), population.end(), compareFitness);
 91 | }
 92 | 
 93 | void NeuralNetworkTrainer::LetGenerationLive() {
 94 | 	for (auto& individual : population){
 95 | 		individual.Update();
 96 | 	}
 97 | }
 98 | 
 99 | void NeuralNetworkTrainer::Repopulate() {
100 | 	// TODO jnf
101 | 	// Implementation
102 | 	CategorizePopulationIntoSpecies();
103 |     // TODO jnf
104 |     // Add Concurrency
105 | }
106 | 
107 | 
108 | void NeuralNetworkTrainer::CategorizePopulationIntoSpecies()
109 | {
110 | 	for (auto& individual : population) {
111 | 		bool isCompatibleWithExistingSpecies = false;
112 | 		for (auto& currSpecies : species) {
113 | 			if (currSpecies.IsCompatible(individual.GetGenome())) {
114 | 				individual.CoupleWithSpecies(currSpecies);
115 | 				isCompatibleWithExistingSpecies = true;
116 | 				break;
117 | 			}
118 | 		}
119 | 		if (!isCompatibleWithExistingSpecies) {
120 | 			Species newSpecies(parameters);
121 | 			species.push_back(std::move(newSpecies));
122 | 			individual.CoupleWithSpecies(species.back());
123 | 		}
124 | 	}
125 | 	// TODO jnf
126 | 	// Clear empty species
127 | }


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
  1 | # JNF_NEAT
  2 | 
  3 | My implementation of Kenneth Stanley and Risto Miikkulainen's NEAT (NeuroEvolution
  4 | of Augmenting Topologies, http://nn.cs.utexas.edu/downloads/papers/stanley.ec02.pdf).
  5 | 
  6 | It focuses (in contrast to other implementations) on
  7 | 
  8 | - Speed - through modern and efficient C++14
  9 | - Clean Code - through constant ongoing refactoring and a deep care for aesthetics
 10 | - Usability - through being able to be used without much knowledge of Neural Networks
 11 | - Platform Independence - written on three different operating systems (Windows, Ubuntu, MacOS X) and two different IDEs (Visual Studio 2015, CLion), it is safe to say, that it will work on multiple platforms, flawlessly.
 12 | 
 13 | ##Foreword
 14 | 
 15 | ###Motivation
 16 | One of the problems of machine learning is its steep learning curve. If you want to let your code learn,
 17 | you've got few choices: Use expensive proprietary modules or use libraries that are aimed at people with experience in AI.
 18 | If you want to learn how machine learning works, you've got to read scientific papers with tons of buzzwords.
 19 | The provided code is often made by people who are brilliant at researching, but have only got poor programming skills.
 20 | 
 21 | We think this is a shame. A programmer shouldn't have to learn about the internals of a class.
 22 | He should be able to use it intuitively, in a matter that it relieves him from work, not set him up for more.
 23 | That's the age old principle of encapsulation!
 24 | 
 25 | Another problem we've encountered, was platform dependency. Despite being conceived as a platform independent language,
 26 | we encountered various implementations of NEAT, that managed to break this. Examples included code with for loops looking like this:
 27 | ```sh
 28 | for( i = 0; i < 10; i++ ){
 29 |     ...
 30 | }
 31 | ```
 32 | Notice the `i` not having been declared? Some compilers permit this. Another, more subtle one was the `abstract` keyword
 33 | of Microsoft Visual Studio. It's actually not part of the language. The official way is to write an (admittedly more arbitrary) `= 0`
 34 | at the end of a function.
 35 | 
 36 | ###What is NEAT?
 37 | 
 38 | TODO
 39 | 
 40 | ##Usage
 41 | First, you have to instantiate a `NeuralNetworkTrainer`. This class will take care of everything. It uses standard training values if not provided with a parameter.
 42 | If you know what you're doing, you can provide it with a `NeuralNetworkTrainer::RuleSet` instance to tweak the learning process.
 43 | 
 44 | You then have to provide an implementation of the `ITrainable` interface. It's methods are
 45 | - Update()
 46 | - GetOrCalculateFitness()
 47 | - ReceiveNetworkOutputs()
 48 | - ProvideNetworkWithInputs()
 49 | 
 50 | ###Update()
 51 | This method gets called automatically multiple times during training.
 52 | > default updatesPerGeneration: 1
 53 | > Imagine this value as **number of actions per lifetime**
 54 | 
 55 | The actions of your object should take place here. This almost always boils down to **executing the command the Neural Network decides to use**. (Remember: You get this Information by calling `LoadNeuralNetworkOutputs()`).
 56 | 
 57 | **Example**: Say you want to train an artificial player for Super Mario World. This method should then take care of actually pressing the buttons your network want you to. In this specific case, it should also update the whole game for a frame, so enemies and items can react to Mario.
 58 | 
 59 | ###GetOrCalculateFitness()
 60 | This method tells the trainer how good this specific instance is compared to others.
 61 | It gets called automatically when the `ITrainable` object dies
 62 | > It's used to generate this objects offspring, with a fitness score of zero or lower meaning that this individuals genes are not going to get passed on
 63 | 
 64 | > default minFitness: -2147483646
 65 | > default maxFitness: 100
 66 | 
 67 | Note that in very analog programs such as real world simulations, true perfection should be unreachable
 68 | 
 69 | **Example**: A simulated chess player could have a fitness method implemented like this:
 70 | ```sh
 71 | unsigned int ChessSim::GetOrCalculateFitness() {
 72 |   unsigned int fitness = 0;
 73 |   for (const auto & piece : enemyKilledPieces) {
 74 |     fitness += piece.GetImportance();
 75 |   }
 76 |   for (const auto & piece : ownKilledPieces) {
 77 |     fitness -= piece.GetImportance();
 78 |   }
 79 |   return fitness;
 80 | }
 81 | ```
 82 | 
 83 | ###ReceiveNetworkOutputs()
 84 | This method returns the conclusions of your neural network as a series of floats
 85 | > default minNeuralCharge = 0.0;
 86 | > default maxNeuralCharge = 1.0;
 87 | > It is **highly** recommended to leave these values like this (see advanced FAQ for details)
 88 | 
 89 | Almost always you'll want to translate these values into something your program can work with and store in a member
 90 | 
 91 | **Example**: TODO
 92 | 
 93 | ###ProvideNetworkWithInputs()
 94 | This method describes what your network "sees". It get's called automatically whenever your network needs updated real world knowledge.
 95 | Remember that it works (just like `ReceiveNetworkOutputs()`) with a vector of doubles, so most of the time you'll want to translate your
 96 | inputs by dividing them by their maximally possible values.
 97 | > default minNeuralCharge = 0.0;  
 98 | > default maxNeuralCharge = 1.0;  
 99 | > It is **highly** recommended to leave these values like this (see advanced FAQ for details)
100 | 
101 | **Example**: Let's assume you want to create a handwriting reader.
102 | Your input would be a 20 pixels wide and 20 pixels high image of a hand drawn letter.
103 | In order to feed the neural network with this information,
104 | we'll create a vector with 20 * 20 = 400 inputs, each one having a value between 0.0 and 1.0 that represents this pixels blackness.
105 | We can do this in a variety of ways. One of them would be to add the RGB values of the pixel up and divide them by
106 | the maximum possible, 255+255+255 = 765. By this method, a bright red pixel(255,0,0) would be represented in our 400 element vector by
107 | the number 255 / 765 = 0.3333.
108 | 
109 | ###Full Example
110 |  
111 |  TODO
112 |  
113 | ## FAQ
114 | 
115 | TODO
116 | 
117 | ## Advanced FAQ
118 | 
119 | TODO
120 | 


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  3 | .DS_Store
  4 | .AppleDouble
  5 | .LSOverride
  6 | 
  7 | # Icon must end with two \r
  8 | Icon
  9 | 
 10 | # Thumbnails
 11 | ._*
 12 | 
 13 | # Files that might appear in the root of a volume
 14 | .DocumentRevisions-V100
 15 | .fseventsd
 16 | .Spotlight-V100
 17 | .TemporaryItems
 18 | .Trashes
 19 | .VolumeIcon.icns
 20 | 
 21 | # Directories potentially created on remote AFP share
 22 | .AppleDB
 23 | .AppleDesktop
 24 | Network Trash Folder
 25 | Temporary Items
 26 | .apdisk
 27 | ### Linux template
 28 | *~
 29 | 
 30 | # temporary files which can be created if a process still has a handle open of a deleted file
 31 | .fuse_hidden*
 32 | 
 33 | # KDE directory preferences
 34 | .directory
 35 | 
 36 | # Linux trash folder which might appear on any partition or disk
 37 | .Trash-*
 38 | ### VisualStudio template
 39 | ## Ignore Visual Studio temporary files, build results, and
 40 | ## files generated by popular Visual Studio add-ons.
 41 | 
 42 | # User-specific files
 43 | *.suo
 44 | *.user
 45 | *.userosscache
 46 | *.sln.docstates
 47 | 
 48 | # User-specific files (MonoDevelop/Xamarin Studio)
 49 | *.userprefs
 50 | 
 51 | # Build results
 52 | [Dd]ebug/
 53 | [Dd]ebugPublic/
 54 | [Rr]elease/
 55 | [Rr]eleases/
 56 | x64/
 57 | x86/
 58 | bld/
 59 | [Bb]in/
 60 | [Oo]bj/
 61 | [Ll]og/
 62 | 
 63 | # Visual Studio 2015 cache/options directory
 64 | .vs/
 65 | # Uncomment if you have tasks that create the project's static files in wwwroot
 66 | #wwwroot/
 67 | 
 68 | # MSTest test Results
 69 | [Tt]est[Rr]esult*/
 70 | [Bb]uild[Ll]og.*
 71 | 
 72 | # NUNIT
 73 | *.VisualState.xml
 74 | TestResult.xml
 75 | 
 76 | # Build Results of an ATL Project
 77 | [Dd]ebugPS/
 78 | [Rr]eleasePS/
 79 | dlldata.c
 80 | 
 81 | # DNX
 82 | project.lock.json
 83 | artifacts/
 84 | 
 85 | *_i.c
 86 | *_p.c
 87 | *_i.h
 88 | *.ilk
 89 | *.meta
 90 | *.obj
 91 | *.pch
 92 | *.pdb
 93 | *.pgc
 94 | *.pgd
 95 | *.rsp
 96 | *.sbr
 97 | *.tlb
 98 | *.tli
 99 | *.tlh
100 | *.tmp
101 | *.tmp_proj
102 | *.log
103 | *.vspscc
104 | *.vssscc
105 | .builds
106 | *.pidb
107 | *.svclog
108 | *.scc
109 | 
110 | # Chutzpah Test files
111 | _Chutzpah*
112 | 
113 | # Visual C++ cache files
114 | ipch/
115 | *.aps
116 | *.ncb
117 | *.opendb
118 | *.opensdf
119 | *.sdf
120 | *.cachefile
121 | *.VC.db
122 | *.VC.VC.opendb
123 | 
124 | # Visual Studio profiler
125 | *.psess
126 | *.vsp
127 | *.vspx
128 | *.sap
129 | 
130 | # TFS 2012 Local Workspace
131 | $tf/
132 | 
133 | # Guidance Automation Toolkit
134 | *.gpState
135 | 
136 | # ReSharper is a .NET coding add-in
137 | _ReSharper*/
138 | *.[Rr]e[Ss]harper
139 | *.DotSettings.user
140 | 
141 | # JustCode is a .NET coding add-in
142 | .JustCode
143 | 
144 | # TeamCity is a build add-in
145 | _TeamCity*
146 | 
147 | # DotCover is a Code Coverage Tool
148 | *.dotCover
149 | 
150 | # NCrunch
151 | _NCrunch_*
152 | .*crunch*.local.xml
153 | nCrunchTemp_*
154 | 
155 | # MightyMoose
156 | *.mm.*
157 | AutoTest.Net/
158 | 
159 | # Web workbench (sass)
160 | .sass-cache/
161 | 
162 | # Installshield output folder
163 | [Ee]xpress/
164 | 
165 | # DocProject is a documentation generator add-in
166 | DocProject/buildhelp/
167 | DocProject/Help/*.HxT
168 | DocProject/Help/*.HxC
169 | DocProject/Help/*.hhc
170 | DocProject/Help/*.hhk
171 | DocProject/Help/*.hhp
172 | DocProject/Help/Html2
173 | DocProject/Help/html
174 | 
175 | # Click-Once directory
176 | publish/
177 | 
178 | # Publish Web Output
179 | *.[Pp]ublish.xml
180 | *.azurePubxml
181 | # but database connection strings (with potential passwords) will be unencrypted
182 | *.pubxml
183 | *.publishproj
184 | 
185 | # Microsoft Azure Web App publish settings. Comment the next line if you want to
186 | # checkin your Azure Web App publish settings, but sensitive information contained
187 | # in these scripts will be unencrypted
188 | PublishScripts/
189 | 
190 | # NuGet Packages
191 | *.nupkg
192 | # The packages folder can be ignored because of Package Restore
193 | **/packages/*
194 | # except build/, which is used as an MSBuild target.
195 | !**/packages/build/
196 | # Uncomment if necessary however generally it will be regenerated when needed
197 | #!**/packages/repositories.config
198 | # NuGet v3's project.json files produces more ignoreable files
199 | *.nuget.props
200 | *.nuget.targets
201 | 
202 | # Microsoft Azure Build Output
203 | csx/
204 | *.build.csdef
205 | 
206 | # Microsoft Azure Emulator
207 | ecf/
208 | rcf/
209 | 
210 | # Windows Store app package directories and files
211 | AppPackages/
212 | BundleArtifacts/
213 | Package.StoreAssociation.xml
214 | _pkginfo.txt
215 | 
216 | # Visual Studio cache files
217 | # files ending in .cache can be ignored
218 | *.[Cc]ache
219 | # but keep track of directories ending in .cache
220 | !*.[Cc]ache/
221 | 
222 | # Others
223 | ClientBin/
224 | ~$*
225 | *.dbmdl
226 | *.dbproj.schemaview
227 | *.pfx
228 | *.publishsettings
229 | node_modules/
230 | orleans.codegen.cs
231 | 
232 | # Since there are multiple workflows, uncomment next line to ignore bower_components
233 | # (https://github.com/github/gitignore/pull/1529#issuecomment-104372622)
234 | #bower_components/
235 | 
236 | # RIA/Silverlight projects
237 | Generated_Code/
238 | 
239 | # Backup & report files from converting an old project file
240 | # to a newer Visual Studio version. Backup files are not needed,
241 | # because we have git ;-)
242 | _UpgradeReport_Files/
243 | Backup*/
244 | UpgradeLog*.XML
245 | UpgradeLog*.htm
246 | 
247 | # SQL Server files
248 | *.mdf
249 | *.ldf
250 | 
251 | # Business Intelligence projects
252 | *.rdl.data
253 | *.bim.layout
254 | *.bim_*.settings
255 | 
256 | # Microsoft Fakes
257 | FakesAssemblies/
258 | 
259 | # GhostDoc plugin setting file
260 | *.GhostDoc.xml
261 | 
262 | # Node.js Tools for Visual Studio
263 | .ntvs_analysis.dat
264 | 
265 | # Visual Studio 6 build log
266 | *.plg
267 | 
268 | # Visual Studio 6 workspace options file
269 | *.opt
270 | 
271 | # Visual Studio LightSwitch build output
272 | **/*.HTMLClient/GeneratedArtifacts
273 | **/*.DesktopClient/GeneratedArtifacts
274 | **/*.DesktopClient/ModelManifest.xml
275 | **/*.Server/GeneratedArtifacts
276 | **/*.Server/ModelManifest.xml
277 | _Pvt_Extensions
278 | 
279 | # Paket dependency manager
280 | .paket/paket.exe
281 | paket-files/
282 | 
283 | # FAKE - F# Make
284 | .fake/
285 | 
286 | # JetBrains Rider
287 | .idea/
288 | *.sln.iml
289 | ### C template
290 | # Object files
291 | *.o
292 | *.ko
293 | *.elf
294 | 
295 | # Precompiled Headers
296 | *.gch
297 | 
298 | # Libraries
299 | *.lib
300 | *.a
301 | *.la
302 | *.lo
303 | 
304 | # Shared objects (inc. Windows DLLs)
305 | *.dll
306 | *.so
307 | *.so.*
308 | *.dylib
309 | 
310 | # Executables
311 | *.exe
312 | *.out
313 | *.app
314 | *.i*86
315 | *.x86_64
316 | *.hex
317 | 
318 | # Debug files
319 | *.dSYM/
320 | ### JetBrains template
321 | # Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and Webstorm
322 | # Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
323 | 
324 | # User-specific stuff:
325 | .idea/workspace.xml
326 | .idea/tasks.xml
327 | .idea/dictionaries
328 | .idea/vcs.xml
329 | .idea/jsLibraryMappings.xml
330 | 
331 | # Sensitive or high-churn files:
332 | .idea/dataSources.ids
333 | .idea/dataSources.xml
334 | .idea/dataSources.local.xml
335 | .idea/sqlDataSources.xml
336 | .idea/dynamic.xml
337 | .idea/uiDesigner.xml
338 | 
339 | # Gradle:
340 | .idea/gradle.xml
341 | .idea/libraries
342 | 
343 | # Mongo Explorer plugin:
344 | .idea/mongoSettings.xml
345 | 
346 | ## File-based project format:
347 | *.iws
348 | 
349 | ## Plugin-specific files:
350 | 
351 | # IntelliJ
352 | /out/
353 | 
354 | # mpeltonen/sbt-idea plugin
355 | .idea_modules/
356 | 
357 | # JIRA plugin
358 | atlassian-ide-plugin.xml
359 | 
360 | # Crashlytics plugin (for Android Studio and IntelliJ)
361 | com_crashlytics_export_strings.xml
362 | crashlytics.properties
363 | crashlytics-build.properties
364 | fabric.properties
365 | ### C++ template
366 | # Compiled Object files
367 | *.slo
368 | 
369 | # Precompiled Headers
370 | 
371 | # Compiled Dynamic libraries
372 | 
373 | # Fortran module files
374 | *.mod
375 | 
376 | # Compiled Static libraries
377 | *.lai
378 | 
379 | # Executables
380 | ### Windows template
381 | # Windows image file caches
382 | Thumbs.db
383 | ehthumbs.db
384 | 
385 | # Folder config file
386 | Desktop.ini
387 | 
388 | # Recycle Bin used on file shares
389 | $RECYCLE.BIN/
390 | 
391 | # Windows Installer files
392 | *.cab
393 | *.msi
394 | *.msm
395 | *.msp
396 | 
397 | # Windows shortcuts
398 | *.lnk
399 | ### CMake template
400 | CMakeCache.txt
401 | CMakeFiles
402 | CMakeScripts
403 | Makefile
404 | cmake_install.cmake
405 | install_manifest.txt
406 | 


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/LICENSE:
--------------------------------------------------------------------------------
  1 |                     GNU GENERAL PUBLIC LICENSE
  2 |                        Version 3, 29 June 2007
  3 | 
  4 |  Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
  5 |  Everyone is permitted to copy and distribute verbatim copies
  6 |  of this license document, but changing it is not allowed.
  7 | 
  8 | 
  9 |                        TERMS AND CONDITIONS
 10 | 
 11 |   0. Definitions.
 12 | 
 13 |   "This License" refers to version 3 of the GNU General Public License.
 14 | 
 15 |   "Copyright" also means copyright-like laws that apply to other kinds of
 16 | works, such as semiconductor masks.
 17 | 
 18 |   "The Program" refers to any copyrightable work licensed under this
 19 | License.  Each licensee is addressed as "you".  "Licensees" and
 20 | "recipients" may be individuals or organizations.
 21 | 
 22 |   To "modify" a work means to copy from or adapt all or part of the work
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 24 | exact copy.  The resulting work is called a "modified version" of the
 25 | earlier work or a work "based on" the earlier work.
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 27 |   A "covered work" means either the unmodified Program or a work based
 28 | on the Program.
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 30 |   To "propagate" a work means to do anything with it that, without
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 33 | computer or modifying a private copy.  Propagation includes copying,
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134 | 
135 |   You may convey verbatim copies of the Program's source code as you
136 | receive it, in any medium, provided that you conspicuously and
137 | appropriately publish on each copy an appropriate copyright notice;
138 | keep intact all notices stating that this License and any
139 | non-permissive terms added in accord with section 7 apply to the code;
140 | keep intact all notices of the absence of any warranty; and give all
141 | recipients a copy of this License along with the Program.
142 | 
143 |   You may charge any price or no price for each copy that you convey,
144 | and you may offer support or warranty protection for a fee.
145 | 
146 |   5. Conveying Modified Source Versions.
147 | 
148 |   You may convey a work based on the Program, or the modifications to
149 | produce it from the Program, in the form of source code under the
150 | terms of section 4, provided that you also meet all of these conditions:
151 | 
152 |     a) The work must carry prominent notices stating that you modified
153 |     it, and giving a relevant date.
154 | 
155 |     b) The work must carry prominent notices stating that it is
156 |     released under this License and any conditions added under section
157 |     7.  This requirement modifies the requirement in section 4 to
158 |     "keep intact all notices".
159 | 
160 |     c) You must license the entire work, as a whole, under this
161 |     License to anyone who comes into possession of a copy.  This
162 |     License will therefore apply, along with any applicable section 7
163 |     additional terms, to the whole of the work, and all its parts,
164 |     regardless of how they are packaged.  This License gives no
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166 |     invalidate such permission if you have separately received it.
167 | 
168 |     d) If the work has interactive user interfaces, each must display
169 |     Appropriate Legal Notices; however, if the Program has interactive
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171 |     work need not make them do so.
172 | 
173 |   A compilation of a covered work with other separate and independent
174 | works, which are not by their nature extensions of the covered work,
175 | and which are not combined with it such as to form a larger program,
176 | in or on a volume of a storage or distribution medium, is called an
177 | "aggregate" if the compilation and its resulting copyright are not
178 | used to limit the access or legal rights of the compilation's users
179 | beyond what the individual works permit.  Inclusion of a covered work
180 | in an aggregate does not cause this License to apply to the other
181 | parts of the aggregate.
182 | 
183 |   6. Conveying Non-Source Forms.
184 | 
185 |   You may convey a covered work in object code form under the terms
186 | of sections 4 and 5, provided that you also convey the
187 | machine-readable Corresponding Source under the terms of this License,
188 | in one of these ways:
189 | 
190 |     a) Convey the object code in, or embodied in, a physical product
191 |     (including a physical distribution medium), accompanied by the
192 |     Corresponding Source fixed on a durable physical medium
193 |     customarily used for software interchange.
194 | 
195 |     b) Convey the object code in, or embodied in, a physical product
196 |     (including a physical distribution medium), accompanied by a
197 |     written offer, valid for at least three years and valid for as
198 |     long as you offer spare parts or customer support for that product
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200 |     copy of the Corresponding Source for all the software in the
201 |     product that is covered by this License, on a durable physical
202 |     medium customarily used for software interchange, for a price no
203 |     more than your reasonable cost of physically performing this
204 |     conveying of source, or (2) access to copy the
205 |     Corresponding Source from a network server at no charge.
206 | 
207 |     c) Convey individual copies of the object code with a copy of the
208 |     written offer to provide the Corresponding Source.  This
209 |     alternative is allowed only occasionally and noncommercially, and
210 |     only if you received the object code with such an offer, in accord
211 |     with subsection 6b.
212 | 
213 |     d) Convey the object code by offering access from a designated
214 |     place (gratis or for a charge), and offer equivalent access to the
215 |     Corresponding Source in the same way through the same place at no
216 |     further charge.  You need not require recipients to copy the
217 |     Corresponding Source along with the object code.  If the place to
218 |     copy the object code is a network server, the Corresponding Source
219 |     may be on a different server (operated by you or a third party)
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223 |     Corresponding Source, you remain obligated to ensure that it is
224 |     available for as long as needed to satisfy these requirements.
225 | 
226 |     e) Convey the object code using peer-to-peer transmission, provided
227 |     you inform other peers where the object code and Corresponding
228 |     Source of the work are being offered to the general public at no
229 |     charge under subsection 6d.
230 | 
231 |   A separable portion of the object code, whose source code is excluded
232 | from the Corresponding Source as a System Library, need not be
233 | included in conveying the object code work.
234 | 
235 |   A "User Product" is either (1) a "consumer product", which means any
236 | tangible personal property which is normally used for personal, family,
237 | or household purposes, or (2) anything designed or sold for incorporation
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239 | doubtful cases shall be resolved in favor of coverage.  For a particular
240 | product received by a particular user, "normally used" refers to a
241 | typical or common use of that class of product, regardless of the status
242 | of the particular user or of the way in which the particular user
243 | actually uses, or expects or is expected to use, the product.  A product
244 | is a consumer product regardless of whether the product has substantial
245 | commercial, industrial or non-consumer uses, unless such uses represent
246 | the only significant mode of use of the product.
247 | 
248 |   "Installation Information" for a User Product means any methods,
249 | procedures, authorization keys, or other information required to install
250 | and execute modified versions of a covered work in that User Product from
251 | a modified version of its Corresponding Source.  The information must
252 | suffice to ensure that the continued functioning of the modified object
253 | code is in no case prevented or interfered with solely because
254 | modification has been made.
255 | 
256 |   If you convey an object code work under this section in, or with, or
257 | specifically for use in, a User Product, and the conveying occurs as
258 | part of a transaction in which the right of possession and use of the
259 | User Product is transferred to the recipient in perpetuity or for a
260 | fixed term (regardless of how the transaction is characterized), the
261 | Corresponding Source conveyed under this section must be accompanied
262 | by the Installation Information.  But this requirement does not apply
263 | if neither you nor any third party retains the ability to install
264 | modified object code on the User Product (for example, the work has
265 | been installed in ROM).
266 | 
267 |   The requirement to provide Installation Information does not include a
268 | requirement to continue to provide support service, warranty, or updates
269 | for a work that has been modified or installed by the recipient, or for
270 | the User Product in which it has been modified or installed.  Access to a
271 | network may be denied when the modification itself materially and
272 | adversely affects the operation of the network or violates the rules and
273 | protocols for communication across the network.
274 | 
275 |   Corresponding Source conveyed, and Installation Information provided,
276 | in accord with this section must be in a format that is publicly
277 | documented (and with an implementation available to the public in
278 | source code form), and must require no special password or key for
279 | unpacking, reading or copying.
280 | 
281 |   7. Additional Terms.
282 | 
283 |   "Additional permissions" are terms that supplement the terms of this
284 | License by making exceptions from one or more of its conditions.
285 | Additional permissions that are applicable to the entire Program shall
286 | be treated as though they were included in this License, to the extent
287 | that they are valid under applicable law.  If additional permissions
288 | apply only to part of the Program, that part may be used separately
289 | under those permissions, but the entire Program remains governed by
290 | this License without regard to the additional permissions.
291 | 
292 |   When you convey a copy of a covered work, you may at your option
293 | remove any additional permissions from that copy, or from any part of
294 | it.  (Additional permissions may be written to require their own
295 | removal in certain cases when you modify the work.)  You may place
296 | additional permissions on material, added by you to a covered work,
297 | for which you have or can give appropriate copyright permission.
298 | 
299 |   Notwithstanding any other provision of this License, for material you
300 | add to a covered work, you may (if authorized by the copyright holders of
301 | that material) supplement the terms of this License with terms:
302 | 
303 |     a) Disclaiming warranty or limiting liability differently from the
304 |     terms of sections 15 and 16 of this License; or
305 | 
306 |     b) Requiring preservation of specified reasonable legal notices or
307 |     author attributions in that material or in the Appropriate Legal
308 |     Notices displayed by works containing it; or
309 | 
310 |     c) Prohibiting misrepresentation of the origin of that material, or
311 |     requiring that modified versions of such material be marked in
312 |     reasonable ways as different from the original version; or
313 | 
314 |     d) Limiting the use for publicity purposes of names of licensors or
315 |     authors of the material; or
316 | 
317 |     e) Declining to grant rights under trademark law for use of some
318 |     trade names, trademarks, or service marks; or
319 | 
320 |     f) Requiring indemnification of licensors and authors of that
321 |     material by anyone who conveys the material (or modified versions of
322 |     it) with contractual assumptions of liability to the recipient, for
323 |     any liability that these contractual assumptions directly impose on
324 |     those licensors and authors.
325 | 
326 |   All other non-permissive additional terms are considered "further
327 | restrictions" within the meaning of section 10.  If the Program as you
328 | received it, or any part of it, contains a notice stating that it is
329 | governed by this License along with a term that is a further
330 | restriction, you may remove that term.  If a license document contains
331 | a further restriction but permits relicensing or conveying under this
332 | License, you may add to a covered work material governed by the terms
333 | of that license document, provided that the further restriction does
334 | not survive such relicensing or conveying.
335 | 
336 |   If you add terms to a covered work in accord with this section, you
337 | must place, in the relevant source files, a statement of the
338 | additional terms that apply to those files, or a notice indicating
339 | where to find the applicable terms.
340 | 
341 |   Additional terms, permissive or non-permissive, may be stated in the
342 | form of a separately written license, or stated as exceptions;
343 | the above requirements apply either way.
344 | 
345 |   8. Termination.
346 | 
347 |   You may not propagate or modify a covered work except as expressly
348 | provided under this License.  Any attempt otherwise to propagate or
349 | modify it is void, and will automatically terminate your rights under
350 | this License (including any patent licenses granted under the third
351 | paragraph of section 11).
352 | 
353 |   However, if you cease all violation of this License, then your
354 | license from a particular copyright holder is reinstated (a)
355 | provisionally, unless and until the copyright holder explicitly and
356 | finally terminates your license, and (b) permanently, if the copyright
357 | holder fails to notify you of the violation by some reasonable means
358 | prior to 60 days after the cessation.
359 | 
360 |   Moreover, your license from a particular copyright holder is
361 | reinstated permanently if the copyright holder notifies you of the
362 | violation by some reasonable means, this is the first time you have
363 | received notice of violation of this License (for any work) from that
364 | copyright holder, and you cure the violation prior to 30 days after
365 | your receipt of the notice.
366 | 
367 |   Termination of your rights under this section does not terminate the
368 | licenses of parties who have received copies or rights from you under
369 | this License.  If your rights have been terminated and not permanently
370 | reinstated, you do not qualify to receive new licenses for the same
371 | material under section 10.
372 | 
373 |   9. Acceptance Not Required for Having Copies.
374 | 
375 |   You are not required to accept this License in order to receive or
376 | run a copy of the Program.  Ancillary propagation of a covered work
377 | occurring solely as a consequence of using peer-to-peer transmission
378 | to receive a copy likewise does not require acceptance.  However,
379 | nothing other than this License grants you permission to propagate or
380 | modify any covered work.  These actions infringe copyright if you do
381 | not accept this License.  Therefore, by modifying or propagating a
382 | covered work, you indicate your acceptance of this License to do so.
383 | 
384 |   10. Automatic Licensing of Downstream Recipients.
385 | 
386 |   Each time you convey a covered work, the recipient automatically
387 | receives a license from the original licensors, to run, modify and
388 | propagate that work, subject to this License.  You are not responsible
389 | for enforcing compliance by third parties with this License.
390 | 
391 |   An "entity transaction" is a transaction transferring control of an
392 | organization, or substantially all assets of one, or subdividing an
393 | organization, or merging organizations.  If propagation of a covered
394 | work results from an entity transaction, each party to that
395 | transaction who receives a copy of the work also receives whatever
396 | licenses to the work the party's predecessor in interest had or could
397 | give under the previous paragraph, plus a right to possession of the
398 | Corresponding Source of the work from the predecessor in interest, if
399 | the predecessor has it or can get it with reasonable efforts.
400 | 
401 |   You may not impose any further restrictions on the exercise of the
402 | rights granted or affirmed under this License.  For example, you may
403 | not impose a license fee, royalty, or other charge for exercise of
404 | rights granted under this License, and you may not initiate litigation
405 | (including a cross-claim or counterclaim in a lawsuit) alleging that
406 | any patent claim is infringed by making, using, selling, offering for
407 | sale, or importing the Program or any portion of it.
408 | 
409 |   11. Patents.
410 | 
411 |   A "contributor" is a copyright holder who authorizes use under this
412 | License of the Program or a work on which the Program is based.  The
413 | work thus licensed is called the contributor's "contributor version".
414 | 
415 |   A contributor's "essential patent claims" are all patent claims
416 | owned or controlled by the contributor, whether already acquired or
417 | hereafter acquired, that would be infringed by some manner, permitted
418 | by this License, of making, using, or selling its contributor version,
419 | but do not include claims that would be infringed only as a
420 | consequence of further modification of the contributor version.  For
421 | purposes of this definition, "control" includes the right to grant
422 | patent sublicenses in a manner consistent with the requirements of
423 | this License.
424 | 
425 |   Each contributor grants you a non-exclusive, worldwide, royalty-free
426 | patent license under the contributor's essential patent claims, to
427 | make, use, sell, offer for sale, import and otherwise run, modify and
428 | propagate the contents of its contributor version.
429 | 
430 |   In the following three paragraphs, a "patent license" is any express
431 | agreement or commitment, however denominated, not to enforce a patent
432 | (such as an express permission to practice a patent or covenant not to
433 | sue for patent infringement).  To "grant" such a patent license to a
434 | party means to make such an agreement or commitment not to enforce a
435 | patent against the party.
436 | 
437 |   If you convey a covered work, knowingly relying on a patent license,
438 | and the Corresponding Source of the work is not available for anyone
439 | to copy, free of charge and under the terms of this License, through a
440 | publicly available network server or other readily accessible means,
441 | then you must either (1) cause the Corresponding Source to be so
442 | available, or (2) arrange to deprive yourself of the benefit of the
443 | patent license for this particular work, or (3) arrange, in a manner
444 | consistent with the requirements of this License, to extend the patent
445 | license to downstream recipients.  "Knowingly relying" means you have
446 | actual knowledge that, but for the patent license, your conveying the
447 | covered work in a country, or your recipient's use of the covered work
448 | in a country, would infringe one or more identifiable patents in that
449 | country that you have reason to believe are valid.
450 | 
451 |   If, pursuant to or in connection with a single transaction or
452 | arrangement, you convey, or propagate by procuring conveyance of, a
453 | covered work, and grant a patent license to some of the parties
454 | receiving the covered work authorizing them to use, propagate, modify
455 | or convey a specific copy of the covered work, then the patent license
456 | you grant is automatically extended to all recipients of the covered
457 | work and works based on it.
458 | 
459 |   A patent license is "discriminatory" if it does not include within
460 | the scope of its coverage, prohibits the exercise of, or is
461 | conditioned on the non-exercise of one or more of the rights that are
462 | specifically granted under this License.  You may not convey a covered
463 | work if you are a party to an arrangement with a third party that is
464 | in the business of distributing software, under which you make payment
465 | to the third party based on the extent of your activity of conveying
466 | the work, and under which the third party grants, to any of the
467 | parties who would receive the covered work from you, a discriminatory
468 | patent license (a) in connection with copies of the covered work
469 | conveyed by you (or copies made from those copies), or (b) primarily
470 | for and in connection with specific products or compilations that
471 | contain the covered work, unless you entered into that arrangement,
472 | or that patent license was granted, prior to 28 March 2007.
473 | 
474 |   Nothing in this License shall be construed as excluding or limiting
475 | any implied license or other defenses to infringement that may
476 | otherwise be available to you under applicable patent law.
477 | 
478 |   12. No Surrender of Others' Freedom.
479 | 
480 |   If conditions are imposed on you (whether by court order, agreement or
481 | otherwise) that contradict the conditions of this License, they do not
482 | excuse you from the conditions of this License.  If you cannot convey a
483 | covered work so as to satisfy simultaneously your obligations under this
484 | License and any other pertinent obligations, then as a consequence you may
485 | not convey it at all.  For example, if you agree to terms that obligate you
486 | to collect a royalty for further conveying from those to whom you convey
487 | the Program, the only way you could satisfy both those terms and this
488 | License would be to refrain entirely from conveying the Program.
489 | 
490 |   13. Use with the GNU Affero General Public License.
491 | 
492 |   Notwithstanding any other provision of this License, you have
493 | permission to link or combine any covered work with a work licensed
494 | under version 3 of the GNU Affero General Public License into a single
495 | combined work, and to convey the resulting work.  The terms of this
496 | License will continue to apply to the part which is the covered work,
497 | but the special requirements of the GNU Affero General Public License,
498 | section 13, concerning interaction through a network will apply to the
499 | combination as such.
500 | 
501 |   14. Revised Versions of this License.
502 | 
503 |   The Free Software Foundation may publish revised and/or new versions of
504 | the GNU General Public License from time to time.  Such new versions will
505 | be similar in spirit to the present version, but may differ in detail to
506 | address new problems or concerns.
507 | 
508 |   Each version is given a distinguishing version number.  If the
509 | Program specifies that a certain numbered version of the GNU General
510 | Public License "or any later version" applies to it, you have the
511 | option of following the terms and conditions either of that numbered
512 | version or of any later version published by the Free Software
513 | Foundation.  If the Program does not specify a version number of the
514 | GNU General Public License, you may choose any version ever published
515 | by the Free Software Foundation.
516 | 
517 |   If the Program specifies that a proxy can decide which future
518 | versions of the GNU General Public License can be used, that proxy's
519 | public statement of acceptance of a version permanently authorizes you
520 | to choose that version for the Program.
521 | 
522 |   Later license versions may give you additional or different
523 | permissions.  However, no additional obligations are imposed on any
524 | author or copyright holder as a result of your choosing to follow a
525 | later version.
526 | 
527 |   15. Disclaimer of Warranty.
528 | 
529 |   THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
530 | APPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
531 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
532 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
533 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
534 | PURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
535 | IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
536 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
537 | 
538 |   16. Limitation of Liability.
539 | 
540 |   IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
541 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
542 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
543 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
544 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
545 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
546 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
547 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
548 | SUCH DAMAGES.
549 | 
550 |   17. Interpretation of Sections 15 and 16.
551 | 
552 |   If the disclaimer of warranty and limitation of liability provided
553 | above cannot be given local legal effect according to their terms,
554 | reviewing courts shall apply local law that most closely approximates
555 | an absolute waiver of all civil liability in connection with the
556 | Program, unless a warranty or assumption of liability accompanies a
557 | copy of the Program in return for a fee.
558 | 
559 |                      END OF TERMS AND CONDITIONS
560 | 


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