Changed Genetic.cpp to hpp

.gitignore

@@ -1,3 +1,3 @@
 *.*
 !main.cpp
-!Genetic.cpp
+!Genetic.hpp

Genetic.hpp

@@ -1,15 +1,11 @@
-/*
-    Author: Asrın "Syntriax" Doğan
-    Mail: asrindogan99@gmail.com
-*/
 #include <iostream>
 #include <time.h>
 
 #define RandomRange 1
 #define InitialSynapseValue 0.0
-#define MutationRate 0.25
+#define MutationRate 0.15
 #define CrossOverRate 0.25
-#define PopCrossOverRate 0.75
+#define PopCrossOverRate 0.5
 
 class Synapse;
 class Neuron;
@@ -192,6 +188,7 @@ double RandomDouble(int min, int max)
             void Mutate();
             void RandomizeValues();
             void CrossOverSynapses(Layer *);
+            friend void LoadFromFile(NeuralNetwork *, char *);
             friend void WriteToFile(NeuralNetwork *);
             bool CreateNeurons(int);
             bool ConnectPrevious(Layer *);
@@ -283,6 +280,7 @@ double RandomDouble(int min, int max)
         double bias = 0.0;
         double weight = 0.0;
         double mutationValue = 0.0;
+        bool isMutated = false;
         int i;
 
         for (i = 0; i < synapseSize; i++)
@@ -290,12 +288,16 @@ double RandomDouble(int min, int max)
             mutationValue = RandomDouble(0, 1);
             if(mutationValue <= MutationRate)
             {
+                isMutated = true;
                 bias   = RandomDouble(-RandomRange, RandomRange);
                 weight = RandomDouble(-RandomRange, RandomRange);
                 (synapses + i) -> SetBias(bias);
                 (synapses + i) -> SetWeight(weight);
             }
         }
+
+        if(!isMutated && synapseSize != 0)
+            Mutate();
     }
 
     void Layer::CrossOverSynapses(Layer *other)
@@ -422,6 +424,7 @@ double RandomDouble(int min, int max)
             void MutateNetwork();
             void Reset();
             void CrossOverNetwork(NeuralNetwork *);
+            friend void LoadFromFile(NeuralNetwork *, char *);
             friend void WriteToFile(NeuralNetwork *);
             bool SetInputNeurons(int);
             bool SetHiddenNeurons(int, int);
@@ -548,22 +551,20 @@ double RandomDouble(int min, int max)
         int j;
         Synapse *synapsePtr = network -> input -> synapses;
         int count = network -> input -> synapseSize;
-        std::cout << count << "\n";
         FILE *file = fopen("Data/BestSynapses.txt", "w");
         for (i = 0; i < count; i++)
         {
-            fprintf(file, "%lf, %lf, ", synapsePtr -> GetWeight(), synapsePtr -> GetBias());
+            fprintf(file, "%f, %f, ", synapsePtr -> GetWeight(), synapsePtr -> GetBias());
             synapsePtr++;
         }
         
         for (j = 0; j < network -> hiddenSize; j++)
         {
             count = (network -> hidden + j) -> synapseSize;
-            std::cout << count << "\n";
             synapsePtr = (network -> hidden + j) -> synapses;
             for (i = 0; i < count; i++)
             {
-                fprintf(file, "%lf, %lf, ", synapsePtr -> GetWeight(), synapsePtr -> GetBias());
+                fprintf(file, "%f, %f, ", synapsePtr -> GetWeight(), synapsePtr -> GetBias());
                 synapsePtr++;
             }
         }
@@ -571,10 +572,9 @@ double RandomDouble(int min, int max)
 
         synapsePtr = network -> output -> synapses;
         count = network -> output -> synapseSize;
-        std::cout << count << "\n";
         for (i = 0; i < count; i++)
         {
-            fprintf(file, "%lf, %lf, ", synapsePtr -> GetWeight(), synapsePtr -> GetBias());
+            fprintf(file, "%f, %f, ", synapsePtr -> GetWeight(), synapsePtr -> GetBias());
             synapsePtr++;
         }
         fclose(file);
@@ -587,6 +587,49 @@ double RandomDouble(int min, int max)
         output = NULL;
     }
 
+    void LoadFromFile(NeuralNetwork *network, char *filePath)
+    {
+        int i;
+        int j;
+        float readWeight;
+        float readBias;
+        Synapse *synapsePtr = network -> input -> synapses;
+        int count = network -> input -> synapseSize;
+        FILE *file = fopen(filePath, "r");
+        for (i = 0; i < count; i++)
+        {
+            fscanf(file, "%f, %f, ", &readWeight, &readBias);
+            synapsePtr -> SetWeight(readWeight);
+            synapsePtr -> SetBias(readBias);
+            synapsePtr++;
+        }
+        
+        for (j = 0; j < network -> hiddenSize; j++)
+        {
+            count = (network -> hidden + j) -> synapseSize;
+            synapsePtr = (network -> hidden + j) -> synapses;
+            for (i = 0; i < count; i++)
+            {
+                fscanf(file, "%f, %f, ", &readWeight, &readBias);
+                synapsePtr -> SetWeight(readWeight);
+                synapsePtr -> SetBias(readBias);
+                synapsePtr++;
+            }
+        }
+        
+
+        synapsePtr = network -> output -> synapses;
+        count = network -> output -> synapseSize;
+        for (i = 0; i < count; i++)
+        {
+            fscanf(file, "%f, %f, ", &readWeight, &readBias);
+            synapsePtr -> SetWeight(readWeight);
+            synapsePtr -> SetBias(readBias);
+            synapsePtr++;
+        }
+        fclose(file);
+    }
+
     bool NeuralNetwork::SetInputNeurons(int size)
     {
         return input -> CreateNeurons(size);
@@ -690,6 +733,7 @@ double RandomDouble(int min, int max)
             void WriteBestToFile();
             void UpdateScores(int);
             void ResetScores();
+            void LoadBestFromFile(char *);
             bool CreateNetworks(int, int);
             bool ConnectNetworks();
             bool SetInputNeurons(int);
@@ -859,6 +903,14 @@ double RandomDouble(int min, int max)
         step++;
     }
 
+    void Generation::LoadBestFromFile(char *filePath)
+    {
+        LoadFromFile(networks, filePath);
+        LoadFromFile(networks + 1, filePath);
+        this -> NextGeneration();
+    }
+
+
     bool Generation::CreateNetworks(int size, int hiddenSizes)
     {
         if((networks = _CreateNetworks(size, hiddenSizes)))
@@ -908,108 +960,3 @@ double RandomDouble(int min, int max)
         return step;
     }
 #pragma endregion
-
-int main()
-{
-    FILE *inputFile;
-    FILE *outputFile;
-    int decision;
-    
-    int trainCounter;
-    int inputCounter;
-    int doubleCounter;
-    int groupCounter;
-
-    double trainInputs[30][5];
-    double testInputs[120][5];
-    double currentError;
-    Generation generation(50, 5);
-
-    inputFile = fopen("Data/train.data", "r");
-    for (inputCounter = 0; inputCounter < 30; inputCounter++)
-        for (doubleCounter = 0; doubleCounter < 5; doubleCounter++)
-            fscanf(inputFile, "%lf,", &trainInputs[inputCounter][doubleCounter]);
-    fclose(inputFile);
-
-    inputFile = fopen("Data/test.data", "r");
-    for (inputCounter = 0; inputCounter < 120; inputCounter++)
-        for (doubleCounter = 0; doubleCounter < 5; doubleCounter++)
-            fscanf(inputFile, "%lf,", &testInputs[inputCounter][doubleCounter]);
-    fclose(inputFile);
-
-    std::cout << "Inputs   Are Getting Set: ";
-    std::cout << (generation.SetInputNeurons(4)     ? "Successfull!" : "Failed!") << "\n";
-    std::cout << "Hidden 1 Are Getting Set: ";
-    std::cout << (generation.SetHiddenNeurons(0, 2) ? "Successfull!" : "Failed!") << "\n";
-    std::cout << "Hidden 2 Are Getting Set: ";
-    std::cout << (generation.SetHiddenNeurons(1, 2) ? "Successfull!" : "Failed!") << "\n";
-    std::cout << "Hidden 3 Are Getting Set: ";
-    std::cout << (generation.SetHiddenNeurons(2, 2) ? "Successfull!" : "Failed!") << "\n";
-    std::cout << "Hidden 4 Are Getting Set: ";
-    std::cout << (generation.SetHiddenNeurons(3, 2) ? "Successfull!" : "Failed!") << "\n";
-    std::cout << "Hidden 5 Are Getting Set: ";
-    std::cout << (generation.SetHiddenNeurons(4, 2) ? "Successfull!" : "Failed!") << "\n";
-    std::cout << "Outputs  Are Getting Set: ";
-    std::cout << (generation.SetOutputNeurons(1)    ? "Successfull!" : "Failed!") << "\n";
-    std::cout << "Networks Are Getting Connected: ";
-    std::cout << (generation.ConnectNetworks()      ? "Successfull!" : "Failed!") << "\n";
-
-    std::cout << "Networks Are Getting Randomized: ";
-    generation.Randomize();
-    std::cout << "Done!\n";
-    
-    do
-    {
-        std::cout << "\n[-1] Test\n[-2] Best to File\n[-3] Exit\nAny Positive Number for train count\nDecision: ";
-        std::cin >> decision;
-        
-        switch (decision)
-        {
-            case -3:
-                std::cout << "Exiting...\n";
-                break;
-            case -2:
-                generation.WriteBestToFile();
-                break;
-            default:
-                for (trainCounter = 0; trainCounter < decision; trainCounter++)
-                {
-                    std::cout << (trainCounter + 1) << "\n";
-                    for (inputCounter = 0; inputCounter < 10; inputCounter++)
-                    {
-                        generation.ResetScores();
-                        for (groupCounter = 0; groupCounter < 3; groupCounter++)
-                        {
-                            for (doubleCounter = 0; doubleCounter < 4; doubleCounter++)
-                                generation.SetInput(trainInputs[inputCounter * 3 + groupCounter][doubleCounter], doubleCounter);
-                            generation.SetTarget(trainInputs[inputCounter * 3 + groupCounter][4]);
-                            generation.Fire();
-                            generation.UpdateScores();
-                        }
-                        generation.SortByScore();
-                        generation.NextGeneration();
-                    }
-                }
-                std::cout << "Best Score -> " << generation.GetPredictionOfBestNetwork() << "\n";
-                std::cout << "Train is Over!\n";
-                // break; To test it after the train is done
-            case -1:
-                outputFile = fopen("Data/results.data", "w");
-                for (inputCounter = 0; inputCounter < 120; inputCounter++)
-                {
-                    for (doubleCounter = 0; doubleCounter < 4; doubleCounter++)
-                        generation.SetInput(testInputs[inputCounter][doubleCounter], doubleCounter);
-                    generation.SetTarget(testInputs[inputCounter][4]);
-                    
-                    generation.Fire();
-                    currentError = testInputs[inputCounter][4] - generation.GetPredictionOfBestNetwork() < 0 ? generation.GetPredictionOfBestNetwork() - testInputs[inputCounter][4] : testInputs[inputCounter][4] - generation.GetPredictionOfBestNetwork();
-                    fprintf(outputFile, "%lf,%lf,%lf\n", testInputs[inputCounter][4], generation.GetPredictionOfBestNetwork(), currentError);
-                }
-                fclose(outputFile);
-                std::cout << "Test is Over!\n";
-                break;
-        }
-    } while (decision != -3);
-
-    return 0;
-}

main.cpp

@@ -1,391 +1,106 @@
-#include <iostream>
+#include "Genetic.hpp"
-#include <time.h>
 
-#define InitialSynapseValue 1.0
+int main()
-
-class Synapse;
-class Neuron;
-class Layer;
-class Input;
-class Output;
-class NeuralNetwork;
-
-#pragma region Synapse
-    class Synapse
 {
-        private:
+    FILE *inputFile;
-            float weight;
+    FILE *outputFile;
-            float value;
+    int decision;
-            float bias;
-        public:
-            Synapse();
-            void SetValue(float);
-            void SetWeight(float);
-            void SetBias(float);
-            float Fire();
-    };
     
-    Synapse::Synapse()
+    int trainCounter;
+    int inputCounter;
+    int doubleCounter;
+    int groupCounter;
+
+    double trainInputs[30][5];
+    double testInputs[120][5];
+    double currentError;
+    Generation generation(50, 5);
+
+    inputFile = fopen("Data/train.data", "r");
+    for (inputCounter = 0; inputCounter < 30; inputCounter++)
+        for (doubleCounter = 0; doubleCounter < 5; doubleCounter++)
+            fscanf(inputFile, "%lf,", &trainInputs[inputCounter][doubleCounter]);
+    fclose(inputFile);
+
+    inputFile = fopen("Data/test.data", "r");
+    for (inputCounter = 0; inputCounter < 120; inputCounter++)
+        for (doubleCounter = 0; doubleCounter < 5; doubleCounter++)
+            fscanf(inputFile, "%lf,", &testInputs[inputCounter][doubleCounter]);
+    fclose(inputFile);
+
+    std::cout << "Inputs   Are Getting Set: ";
+    std::cout << (generation.SetInputNeurons(4)     ? "Successfull!" : "Failed!") << "\n";
+    std::cout << "Hidden 1 Are Getting Set: ";
+    std::cout << (generation.SetHiddenNeurons(0, 2) ? "Successfull!" : "Failed!") << "\n";
+    std::cout << "Hidden 2 Are Getting Set: ";
+    std::cout << (generation.SetHiddenNeurons(1, 2) ? "Successfull!" : "Failed!") << "\n";
+    std::cout << "Hidden 3 Are Getting Set: ";
+    std::cout << (generation.SetHiddenNeurons(2, 2) ? "Successfull!" : "Failed!") << "\n";
+    std::cout << "Hidden 4 Are Getting Set: ";
+    std::cout << (generation.SetHiddenNeurons(3, 2) ? "Successfull!" : "Failed!") << "\n";
+    std::cout << "Hidden 5 Are Getting Set: ";
+    std::cout << (generation.SetHiddenNeurons(4, 2) ? "Successfull!" : "Failed!") << "\n";
+    std::cout << "Outputs  Are Getting Set: ";
+    std::cout << (generation.SetOutputNeurons(1)    ? "Successfull!" : "Failed!") << "\n";
+    std::cout << "Networks Are Getting Connected: ";
+    std::cout << (generation.ConnectNetworks()      ? "Successfull!" : "Failed!") << "\n";
+
+    std::cout << "Networks Are Getting Randomized: ";
+    generation.Randomize();
+    std::cout << "Done!\n";
+    
+    do
     {
-        this -> value = this -> weight = this -> bias = InitialSynapseValue;
+        std::cout << "\n[-1] Test\n[-2] Best to File\n[-3] Exit\nAny Positive Number for train count\nDecision: ";
+        std::cin >> decision;
+        
+        switch (decision)
+        {
+            case -3:
+                std::cout << "Exiting...\n";
+                break;
+            case -2:
+                generation.WriteBestToFile();
+                break;
+            default:
+                for (trainCounter = 0; trainCounter < decision; trainCounter++)
+                {
+                    std::cout << (trainCounter + 1) << "\n";
+                    for (inputCounter = 0; inputCounter < 10; inputCounter++)
+                    {
+                        generation.ResetScores();
+                        for (groupCounter = 0; groupCounter < 3; groupCounter++)
+                        {
+                            for (doubleCounter = 0; doubleCounter < 4; doubleCounter++)
+                                generation.SetInput(trainInputs[inputCounter * 3 + groupCounter][doubleCounter], doubleCounter);
+                            generation.SetTarget(trainInputs[inputCounter * 3 + groupCounter][4]);
+                            generation.Fire();
+                            generation.UpdateScores();
                         }
-
+                        generation.SortByScore();
-    void Synapse::SetValue(float value)
+                        generation.NextGeneration();
-    {
-        this -> value = value;
                     }
-
-    void Synapse::SetWeight(float weight)
-    {
-        this -> weight = weight;
                 }
-
+                std::cout << "Best Score -> " << generation.GetPredictionOfBestNetwork() << "\n";
-    void Synapse::SetBias(float bias)
+                std::cout << "Train is Over!\n";
+                // break; To test it after the train is done
+            case -1:
+                outputFile = fopen("Data/results.data", "w");
+                for (inputCounter = 0; inputCounter < 120; inputCounter++)
                 {
-        this -> bias = bias;
+                    for (doubleCounter = 0; doubleCounter < 4; doubleCounter++)
+                        generation.SetInput(testInputs[inputCounter][doubleCounter], doubleCounter);
+                    generation.SetTarget(testInputs[inputCounter][4]);
+                    
+                    generation.Fire();
+                    currentError = testInputs[inputCounter][4] - generation.GetPredictionOfBestNetwork() < 0 ? generation.GetPredictionOfBestNetwork() - testInputs[inputCounter][4] : testInputs[inputCounter][4] - generation.GetPredictionOfBestNetwork();
+                    fprintf(outputFile, "%lf,%lf,%lf\n", testInputs[inputCounter][4], generation.GetPredictionOfBestNetwork(), currentError);
                 }
-
+                fclose(outputFile);
-    float Synapse::Fire()
+                std::cout << "Test is Over!\n";
-    {
+                break;
-        float result = 0.0;
-
-        result = this -> value * this -> weight + this -> bias;
-        
-        return result;
         }
-#pragma endregion
+    } while (decision != -3);
-#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();
-            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();
-    }
-
-    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();
-            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();
-    }
-
-    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
 
     return 0;
 }