From 9f649196764e6045400fddbbf94a7d882c099876 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Asr=C4=B1n=20Do=C4=9Fan?=
 <33391270+Syntriax@users.noreply.github.com>
Date: Fri, 13 Dec 2019 16:15:44 +0300
Subject: [PATCH] Initial Commit

---
 main.cpp | 483 +++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 483 insertions(+)
 create mode 100644 main.cpp

diff --git a/main.cpp b/main.cpp
new file mode 100644
index 0000000..3893bab
--- /dev/null
+++ b/main.cpp
@@ -0,0 +1,483 @@
+#include <iostream>
+#include <cstdlib>
+#include <time.h>
+
+
+float RandomFloat(int min, int max)
+{
+    static unsigned long int counter = 0;
+    srand(time(0) + counter++ * 50);
+    int value = (rand() % ((max - min) * 100));
+    return (float)value / 100.0 + (float)min + 1.0;
+}
+
+#pragma region Sinaps 
+    class Sinaps
+    {
+        private:
+            float weight; // Ağırlık
+            float value;  // Değer
+            float bias;   // Öteleme
+        public:
+            Sinaps();
+            Sinaps(float, float, float); // Kaydedilen değerleri yeniden yazabilmek için
+            void SetSinaps(float, float, float); // Sonradan tamamen değiştirebilmek için
+            void SetWeight(float);
+            void SetValue(float);
+            void SetBias(float);
+            float Fire();
+    };
+
+    Sinaps::Sinaps() { weight = value = bias = 0.0; }
+    Sinaps::Sinaps(float weight, float value, float bias)
+    {
+        this -> weight = weight;
+        this -> value = value;
+        this -> bias = bias;
+    }
+
+    void Sinaps::SetSinaps(float weight, float value, float bias) {
+        std::cout << "weight = " << weight << "\n";
+        std::cout << "value = " << value << "\n";
+        std::cout << "bias = " << bias << "\n";
+        Sinaps(weight, value, bias); }
+    void Sinaps::SetWeight(float weight) { this -> weight = weight; }
+    void Sinaps::SetValue(float value) { std::cout << value << "\n"; this -> value = value; }
+    void Sinaps::SetBias(float bias) { this -> bias = bias; }
+
+    float Sinaps::Fire() { return weight * value + bias; }
+#pragma endregion
+#pragma region Noron
+    class Noron
+    {
+        private:
+            Sinaps *forwards;
+            Sinaps *incoming;
+            int forwardsCount;
+            int incomingCount;
+        public:
+            Noron();
+            ~Noron();
+            bool SetForwards(Sinaps *, int);
+            bool SetIncoming(Sinaps *, int);
+            float GetStatus();
+    };
+    
+    Noron::Noron()
+    {
+        forwards = incoming = NULL;
+        forwardsCount = incomingCount = 0;
+    }
+    
+    Noron::~Noron()
+    {
+        delete forwards;
+        delete incoming;
+    }
+    
+    bool Noron::SetForwards(Sinaps *newForwards, int size)
+    {
+        forwards = (Sinaps *) new char[sizeof(Sinaps) * size];
+        
+        if(!forwards) return false;
+
+        for (int i = 0; i < size; i++)
+            *(forwards+i) = *(newForwards+i);
+        
+        forwardsCount = size;
+        return true;
+    }
+
+    bool Noron::SetIncoming(Sinaps *newIncoming, int size)
+    {
+        incoming = (Sinaps *) new char[sizeof(Sinaps) * size];
+        
+        if(!incoming) return false;
+
+        for (int i = 0; i < size; i++)
+            *(incoming+i) = *(newIncoming+i);
+        
+        incomingCount = size;
+        return true;
+    }
+
+    float Noron::GetStatus()
+    {
+        float toplam = 0.0;
+        std::cout << toplam << "\n";
+        for (int i = 0; i < incomingCount; i++)
+            toplam += (incoming + i) -> Fire();
+
+        for (int i = 0; i < forwardsCount; i++)
+            (forwards + i) -> SetValue(toplam);
+
+        return toplam;
+    }
+#pragma endregion
+#pragma region Katman
+    class Katman
+    {
+        protected:
+            Noron *neurons;
+            Katman *forward;
+            Sinaps *layerSinapses;
+            int size;
+            Sinaps *CreateSinapsSet(int size);
+        public:
+            Katman();
+            Katman(int);
+            ~Katman();
+            void FireLayer();
+            void RandomizeSinapsValues();
+            bool SetForward(Katman *);
+            bool SetIncoming(Sinaps *sinapsSet, int backwardsNeuronCount);
+            bool SetNoron(Noron *, int);
+            bool CreateNoron(int);
+            int GetSize();
+    };
+    
+    Katman::Katman() { neurons = NULL; this -> size = 0; }
+    Katman::Katman(int size) 
+    { 
+        Katman();
+        
+        if(!CreateNoron(size))
+            std::cout << "Katman Oluşturulamadı!";
+        else
+            this -> size = size;
+    }
+    Katman::~Katman() { delete neurons; }
+
+    
+    Sinaps *Katman::CreateSinapsSet(int size)
+    {
+        Sinaps* sinapses = (Sinaps *) new char[sizeof(Sinaps) * size];
+
+        if(sinapses)
+            for (int i = 0; i < size; i++)
+                *(sinapses + i) = Sinaps();
+        
+        return sinapses;
+    }
+    void Katman::RandomizeSinapsValues()
+    {
+        if(!forward)
+            return;
+
+        int sinapsCount = size * (forward -> GetSize());
+        for (int i = 0; i < sinapsCount; i++)
+        {
+            (layerSinapses + i) -> SetSinaps(
+                RandomFloat(-5, 5), 
+                RandomFloat(-5, 5), 
+                RandomFloat(-5, 5)
+            );
+        }
+    }
+
+    void Katman::FireLayer()
+    {
+        for (int i = 0; i < size; i++)
+            std::cout << i << ". Fire = " << (neurons + i) -> GetStatus() << "\n";
+    }
+
+    bool Katman::SetForward(Katman *forward)
+    { 
+        Sinaps *sinapses = NULL; // Pointer to store all created sinapses
+        Sinaps *newSinapses = NULL; // Temporary Pointer for creating each neurons' s1inapses
+        int forwardSize;
+        int sinapsesIndex = 0;
+        delete layerSinapses;
+
+        this -> forward = forward;
+        
+        if(!forward)
+            return true;
+
+        forwardSize = forward -> GetSize();
+
+        sinapses = (Sinaps *) new char[sizeof(Sinaps) * size * forwardSize];
+
+        if(!sinapses)
+            return false;
+
+        // Set Forwards of each neuron in the Layer
+        for (int thisCounter = 0; thisCounter < size; thisCounter++)
+        {
+            newSinapses = CreateSinapsSet(forwardSize);
+            
+            if(!newSinapses)
+                return false;
+
+            (neurons + thisCounter) -> SetForwards(newSinapses, forwardSize);
+            
+            // Add each sinaps to the array
+            for (int forwardCounter = 0; forwardCounter < forwardSize; forwardCounter++)
+                *(sinapses + (sinapsesIndex++)) = *(newSinapses + forwardCounter);
+        }
+
+        layerSinapses = sinapses;
+        // Send the sinapses to the forward layer
+        return forward -> SetIncoming(sinapses, size);
+    }
+    
+    bool Katman::SetIncoming(Sinaps *sinapsSet, int backwardsNeuronCount)
+    {
+        Sinaps *sinapses = NULL;
+
+        sinapses = (Sinaps *) new char[sizeof(Sinaps) * backwardsNeuronCount];
+        
+        if(!sinapses)
+            return false;
+        
+        for (int thisCounter = 0; thisCounter < size; thisCounter++)
+        {
+            // Add each sinaps to the array
+            for (int incomingCounter = 0; incomingCounter < backwardsNeuronCount; incomingCounter++)
+                *(sinapses + (size * thisCounter + incomingCounter)) = *(sinapsSet + incomingCounter);
+
+            (neurons + thisCounter) -> SetIncoming(sinapses, backwardsNeuronCount);
+        }
+
+        return true;
+    }
+
+    bool Katman::SetNoron(Noron *newneurons, int size)
+    {
+        neurons = (Noron *) new char[sizeof(Noron) * size];
+        
+        if(!neurons) return false;
+
+        for (int i = 0; i < size; i++)
+            *(neurons+i) = *(newneurons+i);
+        
+        this -> size = size;
+        return true;
+    }
+    
+    bool Katman::CreateNoron(int size)
+    {
+        neurons = (Noron *) new char[sizeof(Noron) * size];
+        
+        if(!neurons) return false;
+
+        for (int i = 0; i < size; i++)
+            *(neurons+i) = Noron();
+        
+        this -> size = size;
+        return true;
+    }
+
+    int Katman::GetSize() { return size; }
+#pragma endregion
+#pragma region Girdi-Cikti
+    #pragma region Girdi
+        class Girdi : public Katman
+        {
+            public:
+                Girdi();
+                Girdi(int);
+                void SetValue(int, float);
+        };
+
+        Girdi::Girdi() : Katman() {}
+        Girdi::Girdi(int size) : Katman(size) {}
+
+        void Girdi::SetValue(int index, float value)
+        {
+            Sinaps *editedSinaps = NULL;
+            int forwardNeuronCount = forward -> GetSize();
+
+            for (int i = 0; i < forwardNeuronCount; i++)
+            {
+                editedSinaps = (layerSinapses + index * forwardNeuronCount + i);
+                editedSinaps -> SetValue(value); 
+            }
+        }
+    #pragma endregion
+    #pragma region Cikti
+        class Cikti : public Katman
+        {
+            public:
+                Cikti();
+                Cikti(int);
+                float GetValue(int);
+        };
+
+        Cikti::Cikti() : Katman() {}
+        Cikti::Cikti(int size) : Katman(size) {}
+
+        float Cikti::GetValue(int index)
+        {
+            return (neurons + index) -> GetStatus();
+        }
+    #pragma endregion
+#pragma endregion
+#pragma region NeuralNetwork
+    class NeuralNetwork
+    {
+        private:
+            Girdi *input;
+            Katman *hiddenLayers;
+            Cikti *output;
+            int hiddenSize;
+        public:
+            NeuralNetwork();
+            NeuralNetwork(int);
+            ~NeuralNetwork();
+            void SetInput(int, float);
+            void RandomizeNetworkValues();
+            void FireNetwork();
+            bool SetInputNeurons(int);
+            bool SetHiddenLayerNeurons(int, int);
+            bool SetOutputNeurons(int);
+            bool ConnectLayers();
+            float GetOutputValue(int);
+    };
+    
+    NeuralNetwork::NeuralNetwork()
+    {
+        hiddenSize = 0;
+        input = NULL;
+        hiddenLayers = NULL;
+        output = NULL;
+    }
+    
+    NeuralNetwork::NeuralNetwork(int hiddenSize)
+    {
+        input = new Girdi();
+        hiddenLayers = new Katman[hiddenSize];
+        output = new Cikti();
+
+        if(!input)
+            std::cout << "Girdi Katmani Olusturulamadi!" << "\n";
+        if(!hiddenLayers)
+            std::cout << "Ara Katmanlar Olusturulamadi!" << "\n";
+        if(!output)
+            std::cout << "Cikti Katmani Olusturulamadi!" << "\n";
+        
+        if(!input || !hiddenLayers || !output)
+            return;
+
+        this -> hiddenSize = hiddenSize;
+    }
+    
+    NeuralNetwork::~NeuralNetwork()
+    {
+        delete input;
+        delete hiddenLayers;
+        delete output;
+    }
+
+    bool NeuralNetwork::SetHiddenLayerNeurons(int index, int size)
+    {
+        Noron *neurons = new Noron[size];
+        
+        if(!neurons)
+            return false;
+        
+        return (hiddenLayers + index) -> SetNoron(neurons, size);
+    }
+
+    bool NeuralNetwork::SetInputNeurons(int size)
+    {
+        Noron *neurons = new Noron[size];
+
+        if(!neurons)
+            return false;
+
+        return output -> SetNoron(neurons, size);
+    }
+
+    bool NeuralNetwork::SetOutputNeurons(int size)
+    {
+        Noron *neurons = new Noron[size];
+
+        if(!neurons)
+            return false;
+
+        return input -> SetNoron(neurons, size);
+    }
+
+    bool NeuralNetwork::ConnectLayers()
+    {
+        if(!input -> SetForward(hiddenLayers))
+            return false;
+        
+        for (int i = 0; i < hiddenSize - 1; i++)
+            if(!(hiddenLayers + i) -> SetForward(hiddenLayers + i + 1))
+                return false;
+        
+        if(!(hiddenLayers + hiddenSize - 1) -> SetForward(output))
+            return false;
+
+        return output -> SetForward(NULL);
+    }
+
+    void NeuralNetwork::FireNetwork()
+    {
+        input -> FireLayer();
+        
+        for (int i = 0; i < hiddenSize; i++)
+            (hiddenLayers + i) -> FireLayer();
+        
+        output -> FireLayer();
+    }
+
+    void NeuralNetwork::SetInput(int index, float value)
+    {
+        if(!input)
+            return;
+        
+        input -> SetValue(index, value);
+    }
+
+    void NeuralNetwork::RandomizeNetworkValues()
+    {
+        input -> RandomizeSinapsValues();
+
+        for (int i = 0; i < hiddenSize; i++)
+            (hiddenLayers + i) -> RandomizeSinapsValues();
+
+        output -> RandomizeSinapsValues();
+    }
+
+    float NeuralNetwork::GetOutputValue(int index)
+    {
+        return output -> GetValue(index);
+    }
+#pragma endregion
+int main(int argc, char const *argv[])
+{
+    NeuralNetwork network(3);
+
+    network.SetInputNeurons(1);
+    network.SetHiddenLayerNeurons(0, 2);
+    network.SetHiddenLayerNeurons(1, 3);
+    network.SetHiddenLayerNeurons(2, 2);
+    network.SetOutputNeurons(1);
+
+    network.ConnectLayers();
+    network.RandomizeNetworkValues();
+
+    network.SetInput(0, 1);
+    std::cout << "m1\n";
+
+    network.FireNetwork();
+    std::cout << "m2\n";
+    std::cout << network.GetOutputValue(0) << "\n";
+    std::cout << "m3\n";
+
+
+
+
+
+    // Katman k1(5);
+    // Katman k2(3);
+
+    // std::cout << "k1 SetForward = " << k1.SetForward(&k2) << "\n";
+    // std::cout << "k2 SetForward = " << k2.SetForward(NULL) << "\n";
+    // k1.FireLayer();
+    // k2.FireLayer();
+    return 0;
+}