From 5c195a7b1964f7303fcb3723436e494d10c5cb24 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Asr=C4=B1n=20Do=C4=9Fan?=
 <33391270+Syntriax@users.noreply.github.com>
Date: Sun, 15 Dec 2019 16:55:17 +0300
Subject: [PATCH] Randomize and Mutate

---
 .gitignore  |   1 +
 Genetic.cpp | 478 ++++++++++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 479 insertions(+)
 create mode 100644 Genetic.cpp

diff --git a/.gitignore b/.gitignore
index 582b0ae..fd0338e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,3 +2,4 @@
 *.txt
 *.cpp
 !main.cpp
+!Genetic.cpp
diff --git a/Genetic.cpp b/Genetic.cpp
new file mode 100644
index 0000000..908ab22
--- /dev/null
+++ b/Genetic.cpp
@@ -0,0 +1,478 @@
+#include <iostream>
+#include <time.h>
+
+#define RandomRange 1
+#define InitialSynapseValue 1.0
+#define MutationRate 0.0001
+
+class Synapse;
+class Neuron;
+class Layer;
+class Input;
+class Output;
+class NeuralNetwork;
+
+float RandomFloat(int min, int max)
+{
+    float result;
+    int value;
+    static unsigned long int counter = time(0);
+    srand(time(0) * counter++);
+    value = ((rand() * counter) % ((max - min) * 100000));
+    result = (float)value / 100000.0 + (float)min;
+    return result;
+}
+
+#pragma region Synapse
+    class Synapse
+    {
+        private:
+            float weight;
+            float value;
+            float bias;
+        public:
+            Synapse();
+            void SetValue(float);
+            void SetWeight(float);
+            void SetBias(float);
+            float Fire();
+    };
+    
+    Synapse::Synapse()
+    {
+        this -> value = this -> weight = this -> bias = InitialSynapseValue;
+    }
+
+    void Synapse::SetValue(float value)
+    {
+        this -> value = value;
+    }
+
+    void Synapse::SetWeight(float weight)
+    {
+        this -> weight = weight;
+    }
+
+    void Synapse::SetBias(float bias)
+    {
+        this -> bias = bias;
+    }
+
+    float Synapse::Fire()
+    {
+        float result = 0.0;
+
+        result = this -> value * this -> weight + this -> bias;
+        
+        return result;
+    }
+#pragma endregion
+#pragma region Neuron
+    class Neuron
+    {
+        private:
+            Synapse *incomings;
+            Synapse *forwards;
+            int incomingsSize;
+            int forwardsSize;
+            int layerSize;
+        public:
+            Neuron();
+            void ConnectIncomings(Synapse *, int);
+            void ConnectForwards(Synapse *, int, int);
+            void SetValue(float);
+            float GetValue();
+    };
+    
+    Neuron::Neuron()
+    {
+        incomings = forwards = NULL;
+        incomingsSize = forwardsSize = layerSize = 0;
+    }
+    
+    void Neuron::SetValue(float value)
+    {
+        for (int i = 0; i < forwardsSize; i++)
+            (forwards + i) -> SetValue(value);
+    }
+    
+    void Neuron::ConnectIncomings(Synapse *incomings, int incomingsSize)
+    {
+        this -> incomings = incomings;
+        this -> incomingsSize = incomingsSize;
+    }
+    
+    void Neuron::ConnectForwards(Synapse *forwards, int forwardsSize, int layerSize)
+    {
+        this -> forwards = forwards;
+        this -> forwardsSize = forwardsSize;
+        this -> layerSize = layerSize;
+    }
+
+    float Neuron::GetValue()
+    {
+        float result = 0.0;
+
+        if(!incomings) return result;
+
+        for (int i = 0; i < incomingsSize; i++)
+            result += (incomings + i) -> Fire();
+
+
+        if(!forwards) return result;
+
+        for (int i = 0; i < forwardsSize; i++)
+            (forwards + i * layerSize) -> SetValue(result);
+
+        return result;
+    }
+#pragma endregion
+#pragma region Layer
+    class Layer
+    {
+        protected:
+            Neuron *neurons;
+            Synapse *synapses;
+            int neuronSize;
+            int synapseSize;
+            Neuron *_CreateNeurons(int);
+        public:
+            Layer();
+            Layer(int);
+            ~Layer();
+            void FireLayer();
+            void Mutate();
+            void RandomizeValues();
+            bool CreateNeurons(int);
+            bool ConnectPrevious(Layer *);
+            bool ConnectForwards(Layer *);
+            int GetSize();
+    };
+    
+    Layer::Layer()
+    {
+        neuronSize = synapseSize = 0;
+        neurons = NULL;
+        synapses = NULL;
+    }
+    
+    Layer::Layer(int size)
+    {
+        neuronSize = synapseSize = 0;
+        synapses = NULL;
+        neurons = _CreateNeurons(size);
+    }
+    
+    Layer::~Layer()
+    {
+        if(neurons)  delete neurons;
+        if(synapses) delete synapses;
+    }
+
+    Neuron *Layer::_CreateNeurons(int size)
+    {
+        Neuron *newNeurons = NULL;
+        newNeurons = (Neuron *) new char[sizeof(Neuron) * size];
+        
+        if(newNeurons)
+            for (int i = 0; i < size; i++)
+                *(newNeurons + i) = Neuron();
+
+        return newNeurons;
+    }
+
+    void Layer::FireLayer()
+    {
+        for (int i = 0; i < neuronSize; i++)
+            (neurons + i) -> GetValue();
+    }
+
+    void Layer::RandomizeValues()
+    {
+        float bias;
+        float weight;
+        for (int i = 0; i < synapseSize; i++)
+        {
+            bias = RandomFloat(-RandomRange, RandomRange);
+            weight = RandomFloat(-RandomRange, RandomRange); 
+            (synapses + i) -> SetBias(bias);
+            (synapses + i) -> SetWeight(weight);
+        }
+    }
+
+    void Layer::Mutate()
+    {
+        float bias = 0.0;
+        float weight = 0.0;
+        float mutationValue = 0.0;
+
+        for (int i = 0; i < synapseSize; i++)
+        {
+            mutationValue = RandomFloat(0, 1);
+            if(mutationValue <= MutationRate)
+            {
+                bias   = RandomFloat(-RandomRange, RandomRange);
+                weight = RandomFloat(-RandomRange, RandomRange);
+                (synapses + i) -> SetBias(bias);
+                (synapses + i) -> SetWeight(weight);
+            }
+        }
+    }
+
+    bool Layer::CreateNeurons(int size)
+    {
+        if(neurons = _CreateNeurons(size))
+            neuronSize = size;
+        return neurons;
+    }
+
+    bool Layer::ConnectPrevious(Layer *previous)
+    {
+        int previousSize = previous -> GetSize();
+        int synapseCount = (this -> neuronSize) * previousSize;
+        int currentIndex = 0;
+        Synapse *currentSynapse = NULL;
+        Neuron *currentNeuron = NULL;
+
+        if(synapses) delete synapses;
+        synapses = (Synapse *) new char[sizeof(Synapse) * synapseCount];
+        if(!synapses) return false;
+
+
+        for (int thisNeuron = 0; thisNeuron < this -> neuronSize; thisNeuron++)
+        {
+            for (int prevNeuron = 0; prevNeuron < previousSize; prevNeuron++)
+            {
+                currentIndex = thisNeuron * previousSize + prevNeuron;
+                currentSynapse = (synapses + currentIndex);
+                currentNeuron = (previous -> neurons) + prevNeuron;
+                
+                *currentSynapse = Synapse();
+            }
+
+            currentNeuron = (neurons + thisNeuron);
+            currentNeuron -> ConnectIncomings((synapses + thisNeuron * previousSize), previousSize);
+        }
+        
+        synapseSize = synapseCount;
+        return previous -> ConnectForwards(this);
+    }
+
+    bool Layer::ConnectForwards(Layer *forwards)
+    {
+        int forwardsSize = forwards -> neuronSize;
+        Neuron *currentNeuron = NULL;
+        
+        for (int thisNeuron = 0; thisNeuron < this -> neuronSize; thisNeuron++)
+        {
+            currentNeuron = (neurons + thisNeuron);
+            for (int forwardNeuron = 0; forwardNeuron < forwardsSize; forwardNeuron++)
+                currentNeuron -> ConnectForwards(forwards -> synapses + thisNeuron, forwardsSize, this -> neuronSize);
+        }
+        return true;
+    }
+
+    int Layer::GetSize()
+    {
+        return neuronSize;
+    }
+    #pragma region Input-Output
+        class Input : public Layer
+        {
+            public:
+                Input();
+                void SetValue(int, float);
+        };
+        
+        Input::Input() : Layer() {}
+        void Input::SetValue(int index, float value)
+        {
+            if(index >= this -> neuronSize || index < 0)
+                return;
+
+            (neurons + index) -> SetValue(value);
+        }
+
+        class Output : public Layer
+        {
+            public:
+                Output();
+                float GetValue(int);
+        };
+        
+        Output::Output() : Layer() {}
+        float Output::GetValue(int index)
+        {
+            float result = 0.0;
+
+            if(index >= this -> neuronSize || index < 0)
+                return result;
+
+            result = (neurons + index) -> GetValue();
+            return result;
+        }
+    #pragma endregion
+#pragma endregion
+#pragma region NeuralNetwork
+    class NeuralNetwork
+    {
+        private:
+            Input *input;
+            Layer *hidden;
+            Output *output;
+            int hiddenSize;
+        public:
+            NeuralNetwork();
+            NeuralNetwork(int);
+            ~NeuralNetwork();
+            void FireNetwork();
+            void RandomizeValues();
+            void MutateNetwork();
+            bool SetInputNeurons(int);
+            bool SetHiddenNeurons(int, int);
+            bool SetOutputNeurons(int);
+            bool ConnectLayers();
+            float GetOutput(int);
+            void SetInput(int, float);
+    };
+    
+    NeuralNetwork::NeuralNetwork()
+    {
+        hiddenSize = 0;
+        input = NULL;
+        hidden = NULL;
+        output = NULL;
+    }
+    
+    NeuralNetwork::NeuralNetwork(int hiddenSize)
+    {
+        this -> hiddenSize = hiddenSize;
+        input  = new Input();
+        hidden = new Layer(hiddenSize);
+        output = new Output();
+    }
+    
+    NeuralNetwork::~NeuralNetwork()
+    {
+        if(input)  delete input;
+        if(hidden) delete hidden;
+        if(output) delete output;
+    }
+
+    void NeuralNetwork::FireNetwork()
+    {
+        for (int i = 0; i < hiddenSize; i++)
+            (hidden + i) -> FireLayer();
+
+        output -> FireLayer();
+    }
+    void NeuralNetwork::MutateNetwork()
+    {
+        input -> Mutate();
+
+        for (int i = 0; i < hiddenSize; i++)
+            (hidden + i) -> Mutate();
+
+        output -> Mutate();
+        
+    }
+
+    void NeuralNetwork::RandomizeValues()
+    {
+        input -> RandomizeValues();
+
+        for (int i = 0; i < hiddenSize; i++)
+            (hidden + i) -> RandomizeValues();
+
+        output -> RandomizeValues();
+    }
+
+    bool NeuralNetwork::SetInputNeurons(int size)
+    {
+        return input -> CreateNeurons(size);
+    }
+
+    bool NeuralNetwork::SetHiddenNeurons(int index, int size)
+    {
+        return (hidden + index) -> CreateNeurons(size);
+    }
+
+    bool NeuralNetwork::SetOutputNeurons(int size)
+    {
+        return output -> CreateNeurons(size);
+    }
+
+    bool NeuralNetwork::ConnectLayers()
+    {
+        if(!hidden -> ConnectPrevious(input))
+            return false;
+        
+        for (int i = 1; i < hiddenSize; i++)
+            if(!(hidden + i) -> ConnectPrevious((hidden + i - 1)))
+                return false;
+
+        if(output -> ConnectPrevious((hidden + hiddenSize - 1)))
+            return false;
+        
+        return true;
+    }
+    
+    float NeuralNetwork::GetOutput(int index)
+    {
+        return output -> GetValue(index);
+    }
+    
+    void NeuralNetwork::SetInput(int index, float value)
+    {
+        input -> SetValue(index, value);
+    }
+#pragma endregion
+
+
+int main(int argc, char const *argv[])
+{
+    NeuralNetwork network(3);
+
+    #pragma region Initialization
+        network.SetInputNeurons(1);
+        network.SetHiddenNeurons(0, 2);
+        network.SetHiddenNeurons(1, 3);
+        network.SetHiddenNeurons(2, 2);
+        network.SetOutputNeurons(1);
+
+        network.ConnectLayers();
+    #pragma endregion
+
+    #pragma region Fixed Bias&Weight
+        network.SetInput(0, 1);
+        network.FireNetwork();
+        std::cout << "Result = " << network.GetOutput(0) << "\n";
+
+        network.SetInput(0, 2);
+        network.FireNetwork();
+        std::cout << "Result = " << network.GetOutput(0) << "\n";
+
+        network.SetInput(0, 3);
+        network.FireNetwork();
+        std::cout << "Result = " << network.GetOutput(0) << "\n";
+    #pragma endregion
+    
+    #pragma region Randomized Bias&Weight
+        network.RandomizeValues();
+        std::cout << "Randomize Called!" << "\n";
+        network.FireNetwork();
+        std::cout << "Result = " << network.GetOutput(0) << "\n";
+        
+        network.MutateNetwork();
+        std::cout << "Mutate Called!" << "\n";
+        network.FireNetwork();
+        std::cout << "Result = " << network.GetOutput(0) << "\n";
+        
+        network.MutateNetwork();
+        std::cout << "Mutate Called!" << "\n";
+        network.FireNetwork();
+        std::cout << "Result = " << network.GetOutput(0) << "\n";
+    #pragma endregion
+    
+    return 0;
+}