This commit is contained in:
Asrın Doğan 2019-12-15 15:57:34 +03:00
parent 8a8d56cc02
commit e9defa3b86
1 changed files with 241 additions and 570 deletions

799
main.cpp
View File

@ -1,7 +1,12 @@
#include <iostream> #include <iostream>
#include <cstdlib>
#include <time.h> #include <time.h>
class Synapse;
class Neuron;
class Layer;
class Input;
class Output;
class NeuralNetwork;
float RandomFloat(int min, int max) float RandomFloat(int min, int max)
{ {
@ -11,473 +16,273 @@ float RandomFloat(int min, int max)
srand(time(0) + counter++ * 50); srand(time(0) + counter++ * 50);
value = (rand() % ((max - min) * 100)); value = (rand() % ((max - min) * 100));
result = (float)value / 100.0 + (float)min; result = (float)value / 100.0 + (float)min;
std::clog << "Function RandomFloat: Between " << min << " and " << max << " returned value is " << result << "\n"; return result;
return 1.0;
} }
#pragma region Sinaps #pragma region Synapse
class Sinaps class Synapse
{ {
private: private:
float weight; // Ağırlık float weight;
float value; // Değer float value;
float bias; // Öteleme float bias;
public: public:
Sinaps(); Synapse();
~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 SetValue(float);
void SetWeight(float);
void SetBias(float); void SetBias(float);
float Fire(); float Fire();
}; };
Sinaps::Sinaps() Synapse::Synapse()
{ {
// weight = value = bias = 0.0; this -> value = this -> weight = this -> bias = 1.0;
this -> weight = RandomFloat(-1, 1);
this -> value = RandomFloat(-1, 1);
this -> bias = RandomFloat(-1, 1);
std::clog << "Create Sinaps: Weight = " << weight
<< " Value = " << value
<< " Bias = " << bias << "\n" << "\n";
} }
Sinaps::~Sinaps() void Synapse::SetValue(float value)
{ {
std::clog << "Delete Sinaps: Weight = " << weight this -> value = value;
<< " Value = " << value
<< " Bias = " << bias << "\n";
} }
Sinaps::Sinaps(float weight, float value, float bias)
void Synapse::SetWeight(float weight)
{ {
this -> weight = weight; this -> weight = weight;
this -> value = value;
this -> bias = bias;
std::clog << "Create Sinaps: Weight = " << weight
<< " Value = " << value
<< " Bias = " << bias << "\n";
} }
void Sinaps::SetSinaps(float weight, float value, float bias) void Synapse::SetBias(float bias)
{ {
this -> weight = weight;
this -> value = value;
this -> bias = bias; this -> bias = bias;
std::clog << "\n" << "Set Sinaps: Weight = " << weight }
<< " Value = " << value
<< " Bias = " << bias << "\n";
}
void Sinaps::SetWeight(float weight) { std::clog << "Set Sinaps Weight: " << weight << "\n"; this -> weight = weight; }
void Sinaps::SetValue(float value) { std::clog << "Set Sinaps Value: " << value << "\n"; this -> value = value; }
void Sinaps::SetBias(float bias) { std::clog << "Set Sinaps Bias: " << bias << "\n"; this -> bias = bias; }
float Sinaps::Fire() float Synapse::Fire()
{ {
float result = weight * value + bias; float result = 0.0;
std::clog << "Return Sinaps Fire: " << weight << " * " <<
value << " + " << result = this -> value * this -> weight + this -> bias;
bias << " = " <<
result << "\n";
return result; return result;
} }
#pragma endregion #pragma endregion
#pragma region Noron #pragma region Neuron
class Noron class Neuron
{ {
private: private:
Sinaps *forwards; Synapse *incomings;
Sinaps *incoming; Synapse *forwards;
int forwardsCount; int incomingsSize;
int incomingCount; int forwardsSize;
int layerSize;
float value;
public: public:
Noron(); Neuron();
~Noron(); void ConnectIncomings(Synapse *, int);
bool SetForwards(Sinaps *, int); void ConnectForwards(Synapse *, int, int);
bool SetIncoming(Sinaps *, int); void SetValue(float);
float GetStatus(); float GetValue();
}; };
Noron::Noron() Neuron::Neuron()
{ {
forwards = incoming = NULL; incomings = forwards = NULL;
forwardsCount = incomingCount = 0; incomingsSize = forwardsSize = layerSize = 0;
std::clog << "Create Noron: NULL" << "\n"; value = 0.0;
} }
Noron::~Noron() void Neuron::SetValue(float value)
{ {
delete forwards; this -> value = value;
delete incoming;
} }
bool Noron::SetForwards(Sinaps *newForwards, int size) void Neuron::ConnectIncomings(Synapse *incomings, int incomingsSize)
{ {
std::clog << "\n" << "SetForwards: Allocating Memory of Size " << size << " Sinapses" << "\n"; this -> incomings = incomings;
if(forwards) this -> incomingsSize = incomingsSize;
{
delete forwards;
forwards = NULL;
std::clog << "SetForwards: Old Forwards Has Been Deleted!" << "\n";
}
forwards = (Sinaps *)new char[sizeof(Sinaps) * size];
if(!forwards)
{
std::clog << "SetForwards: Memory Couldn't Allocated!" << "\n";
return false;
}
std::clog << "SetForwards: Memory Allocated!" << "\n";
for (int i = 0; i < size; i++)
*(forwards+i) = *(newForwards+i);
forwardsCount = size;
std::clog << "SetForwards: Successfull!" << "\n";
return true;
} }
bool Noron::SetIncoming(Sinaps *newIncoming, int size) void Neuron::ConnectForwards(Synapse *forwards, int forwardsSize, int layerSize)
{ {
std::clog << "\n" << "SetIncoming: Allocating Memory of Size " << size << " Sinapses" << "\n"; this -> forwards = forwards;
this -> forwardsSize = forwardsSize;
if(incoming) this -> layerSize = layerSize;
{
delete incoming;
incoming = NULL;
std::clog << "SetIncoming: Old Incoming Has Been Deleted!" << "\n";
}
incoming = (Sinaps *)new char[sizeof(Sinaps) * size];
if(!incoming)
{
std::clog << "SetIncoming: Memory Couldn't Allocated!" << "\n";
return false;
}
std::clog << "SetIncoming: Memory Allocated!" << "\n";
for (int i = 0; i < size; i++)
*(incoming+i) = *(newIncoming+i);
incomingCount = size;
std::clog << "SetIncoming: Successfull!" << "\n";
return true;
} }
float Noron::GetStatus() float Neuron::GetValue()
{ {
std::clog << "\n" << "GetStatus: Called!" << "\n"; float result = 0.0;
float toplam = 0.0;
std::clog << "GetStatus: Firing All Sinapses!" << "\n" << "\n"; if(!incomings) return (value = result);
for (int i = 0; i < incomingCount; i++)
{
std::clog << "GetStatus: Firing Sinaps " << i << "\n";
toplam += (incoming + i) -> Fire();
}
for (int i = 0; i < forwardsCount; i++) for (int i = 0; i < incomingsSize; i++)
{ result += (incomings + i) -> Fire();
std::clog << "GetStatus: Setting Value of Sinaps " << i << " to " << toplam << "\n";
(forwards + i) -> SetValue(toplam);
}
std::clog << "Get Noron Status: Sum = " << toplam << "\n";
return toplam; if(!forwards) return (value = result);
for (int i = 0; i < forwardsSize; i++)
// currentSynapse = (forwards -> synapses + (forwardNeuron * this -> neuronSize));
// (forwards + i) -> SetValue(result);
//BAK BURAYA
(forwards + i * layerSize) -> SetValue(result);
value = result;
return result;
} }
#pragma endregion #pragma endregion
#pragma region Katman #pragma region Layer
class Katman class Layer
{ {
protected: protected:
Noron *neurons; Neuron *neurons;
Katman *forward; Synapse *synapses;
Sinaps *layerSinapses; int neuronSize;
int size; int synapseSize;
Sinaps *CreateSinapsSet(int size); Neuron *_CreateNeurons(int);
public: public:
Katman(); Layer();
Katman(int); Layer(int);
~Katman(); ~Layer();
void FireLayer(); void FireLayer();
void RandomizeSinapsValues(); bool CreateNeurons(int);
bool SetForward(Katman *); bool ConnectPrevious(Layer *);
bool SetIncoming(Sinaps *sinapsSet, int backwardsNeuronCount); bool ConnectForwards(Layer *);
bool SetNoron(Noron *, int);
bool CreateNoron(int);
int GetSize(); int GetSize();
}; };
Katman::Katman() Layer::Layer()
{ {
neuronSize = synapseSize = 0;
neurons = NULL; neurons = NULL;
forward = NULL; synapses = NULL;
layerSinapses = NULL;
this -> size = 0;
std::clog << "Create Layer: NULL" << "\n";
}
Katman::Katman(int size)
{
neurons = NULL;
forward = NULL;
layerSinapses = NULL;
this -> size = 0;
if(!CreateNoron(size))
{
std::clog << "Error Create Layer: Neurons Couldn't Created!" << "\n";
std::cout << "Katman Oluşturulamadı!" << "\n";
return;
}
std::clog << "Create Layer: " << size << " Neurons Has Been Created!" << "\n";
this -> size = size;
}
Katman::~Katman() { delete neurons; }
Sinaps *Katman::CreateSinapsSet(int size)
{
std::clog << "\n" << "CreateSinapsSet: Allocating Memory Size of " << size << " Sinapses" << "\n";
Sinaps* sinapses = new Sinaps[size];
if(sinapses)
std::clog << "Create Sinaps Set: Memory Size of " << size << " Sinapses Allocated!" << "\n";
else
std::clog << "Error Create Sinaps Set!" << "\n";
return sinapses;
}
void Katman::RandomizeSinapsValues()
{
if(!forward)
return;
float weight = 0;
float value = 0;
float bias = 0;
int sinapsCount = size * (forward -> GetSize());
for (int i = 0; i < sinapsCount; i++)
{
weight = RandomFloat(-1, 1);
value = RandomFloat(-1, 1);
bias = RandomFloat(-1, 1);
(layerSinapses + i) -> SetSinaps(weight, value, bias);
std::clog << "RandomizeSinapsValues: SetSinaps Called With Values of SetSinaps(" << weight << ", " << value << ", " << bias << ")" << "\n";
}
} }
void Katman::FireLayer() Layer::Layer(int size)
{ {
std::clog << "\n" << "FireLayer: Number of " << size << " Neurons' GetStatus is Being Called!" << "\n"; neuronSize = synapseSize = 0;
for (int i = 0; i < size; i++) synapses = NULL;
std::clog << i << ". Neuron Status: " << (neurons + i) -> GetStatus() << "\n"; neurons = _CreateNeurons(size);
} }
bool Katman::SetForward(Katman *forward) Layer::~Layer()
{ {
std::clog << "\n" << "SetForward: Called!" << "\n"; if(neurons) delete neurons;
Sinaps *sinapses = NULL; // Pointer to store all created sinapses if(synapses) delete synapses;
Sinaps *newSinapses = NULL; // Temporary Pointer for creating each neurons' s1inapses }
int forwardSize = 0;
if(layerSinapses) 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;
// Synapse *connectSynapses = NULL;
if(synapses) delete synapses;
synapses = (Synapse *) new char[sizeof(Synapse) * synapseCount];
if(!synapses) return false;
// connectSynapses = (Synapse *) new char[sizeof(Synapse) * previousSize];
for (int thisNeuron = 0; thisNeuron < this -> neuronSize; thisNeuron++)
{ {
delete layerSinapses; for (int prevNeuron = 0; prevNeuron < previousSize; prevNeuron++)
layerSinapses = NULL;
std::clog << "SetForward: Old layerSinapses Has Been Deleted!" << "\n";
}
this -> forward = forward;
if(!forward)
{
std::clog << "SetForward: Forward is NULL" << "\n";
return true;
}
forwardSize = forward -> GetSize();
std::clog << "SetForward: Creating Sinaps Set with Number of " << (size * forwardSize) << "\n";
if(sinapses)
{
delete sinapses;
sinapses = NULL;
std::clog << "SetForward: Old Sinapses Has Been Deleted!" << "\n";
}
sinapses = (Sinaps *)new char[sizeof(Sinaps) * size * forwardSize];
if(!sinapses)
{
std::clog << "Error SetForward: Couldn't Allocate Memory for Sinapses!" << "\n";
return false;
}
std::clog << "SetForward: SinapsSet Created!" << "\n";
// Set Forwards of each neuron in the Layer
for (int thisCounter = 0; thisCounter < size; thisCounter++)
{
newSinapses = CreateSinapsSet(forwardSize);
if(!newSinapses)
{ {
std::clog << "SetForward -> CreateSinapsSet: Couldn't Allocate Memory for Sinapses!" << "\n"; currentIndex = thisNeuron * previousSize + prevNeuron;
return false; currentSynapse = (synapses + currentIndex);
currentNeuron = (previous -> neurons) + prevNeuron;
*currentSynapse = Synapse();
// currentSynapse = (Synapse *) new char[sizeof(Synapse)];
// currentSynapse -> SetWeight(1);
// currentSynapse -> SetValue(2);
// currentSynapse -> SetBias(3);
// currentSynapse -> SetRoot(currentNeuron);
} }
std::clog << "SetForward -> CreateSinapsSet: SinapsSet Created!" << "\n";
std::clog << "SetForward -> SetForwards: " << currentNeuron = (neurons + thisNeuron);
(neurons + thisCounter) -> SetForwards(newSinapses, forwardSize) currentNeuron -> ConnectIncomings((synapses + thisNeuron * previousSize), previousSize);
<< "\n";;
// Add each sinaps to the array
for (int forwardCounter = 0; forwardCounter < forwardSize; forwardCounter++)
*(sinapses + forwardCounter) = *(newSinapses + forwardCounter);
std::clog << "SetForward: Sinapses Are Added to the Array!" << "\n";
} }
layerSinapses = sinapses; return previous -> ConnectForwards(this);
std::clog << "SetForward: Sinaps Array Has Been Set to the Class' Pointer!" << "\n";
// Send the sinapses to the forward layer
return forward -> SetIncoming(sinapses, size);
} }
bool Katman::SetIncoming(Sinaps *sinapsSet, int backwardsNeuronCount) bool Layer::ConnectForwards(Layer *forwards)
{ {
Sinaps *sinapses = NULL; int forwardsSize = forwards -> neuronSize;
int sinapcCounter = 0; Neuron *currentNeuron = NULL;
std::clog << "\n" << "SetIncoming: Creating Sinaps Set with Number of " << backwardsNeuronCount << "\n"; for (int thisNeuron = 0; thisNeuron < this -> neuronSize; thisNeuron++)
if(sinapses)
{ {
delete sinapses; currentNeuron = (neurons + thisNeuron);
sinapses = NULL; for (int forwardNeuron = 0; forwardNeuron < forwardsSize; forwardNeuron++)
std::clog << "SetIncoming: Old Sinapses Has Been Deleted!" << "\n"; currentNeuron -> ConnectForwards(forwards -> synapses + thisNeuron, forwardsSize, this -> neuronSize);
// currentSynapse = (forwards -> synapses + (thisNeuron + forwardNeuron * this -> neuronSize));
} }
sinapses = (Sinaps *)new char[sizeof(Sinaps) * backwardsNeuronCount];
if(!sinapses)
{
std::clog << "Error SetIncoming: Couldn't Allocate Memory for Sinapses!" << "\n";
return false;
}
std::clog << "SetIncoming: SinapsSet Created!" << "\n";
for (int thisCounter = 0; thisCounter < size; thisCounter++)
{
std::clog << "SetIncoming: Sinapses Are Being Added to the Array!" << "\n";
// Add each sinaps to the array
for (int incomingCounter = 0; incomingCounter < backwardsNeuronCount; incomingCounter++)
*(sinapses + incomingCounter) = *(sinapsSet + sinapcCounter++);
std::clog << "SetIncoming -> Neuron SetIncoming: " <<
(neurons + thisCounter) -> SetIncoming(sinapses, backwardsNeuronCount)
<< "\n";
}
return true; return true;
} }
bool Katman::SetNoron(Noron *newNeurons, int size) int Layer::GetSize()
{ {
std::clog << "\n" << "SetNoron: Creating Neurons with Number of " << size << "\n"; return neuronSize;
if(neurons)
{
delete neurons;
neurons = NULL;
std::clog << "SetNoron: Old Neurons Has Been Deleted!" << "\n";
}
neurons = (Noron *) new char[sizeof(Noron) * size];
if(!neurons)
{
std::clog << "Error SetNoron: Creating Neurons Failed!" << "\n";
return false;
}
std::clog << "SetNoron: Neurons Created Successfully!" << "\n";
std::clog << "SetNoron: Setting Neurons to the Class' Neurons!" << "\n";
for (int i = 0; i < size; i++)
*(neurons+i) = *(newNeurons+i);
std::clog << "SetNoron: Neurons are Set Successfully!" << "\n";
this -> size = size;
return true;
} }
#pragma region Input-Output
bool Katman::CreateNoron(int size) class Input : public Layer
{
std::clog << "\n" << "CreateNoron: Creating Neurons with Number of " << size << "\n";
if(neurons)
{
delete neurons;
neurons = NULL;
std::clog << "CreateNoron: Old Neurons Has Been Deleted!" << "\n";
}
neurons = new Noron[size];
if(!neurons)
{
std::clog << "Error CreateNoron: Creating Neurons Failed!" << "\n";
return false;
}
std::clog << "CreateNoron: Neurons Created Successfully!" << "\n";
std::clog << "CreateNoron: Neurons are Set Successfully!" << "\n";
this -> size = size;
return true;
}
int Katman::GetSize() { return size; }
#pragma endregion
#pragma region Girdi-Cikti
#pragma region Girdi
class Girdi : public Katman
{ {
public: public:
Girdi(); Input();
Girdi(int);
void SetValue(int, float); void SetValue(int, float);
}; };
Girdi::Girdi() : Katman(1) { std::clog << "\n" << "Create Input Layer: Called!" << "\n"; } Input::Input() : Layer() {}
Girdi::Girdi(int size) : Katman(size) {} void Input::SetValue(int index, float value)
void Girdi::SetValue(int index, float value)
{ {
Sinaps *editedSinaps = NULL; if(index >= this -> neuronSize || index < 0)
int forwardNeuronCount = forward -> GetSize(); return;
std::clog << "\n" << "SetValue: Index of " << index << " Neuron's Sinapses Values are Getting Set to Value of " << value << "\n";
for (int i = 0; i < forwardNeuronCount; i++) (neurons + index) -> SetValue(value);
{
std::clog << "SetValue -> Sinaps SetValue: Index of " << index << " Neuron's " << i << " Sinaps Value is Getting Set to Value of " << value << "\n";
editedSinaps = (layerSinapses + index * forwardNeuronCount + i);
editedSinaps -> SetValue(value);
std::clog << "SetValue -> Sinaps SetValue: Successfull" << "\n";
}
std::clog << "SetValue: Successfull" << "\n";
} }
#pragma endregion
#pragma region Cikti class Output : public Layer
class Cikti : public Katman
{ {
public: public:
Cikti(); Output();
Cikti(int);
float GetValue(int); float GetValue(int);
}; };
Cikti::Cikti() : Katman(1) { std::clog << "\n" << "Create Output Layer: Called!" << "\n"; } Output::Output() : Layer() {}
Cikti::Cikti(int size) : Katman(size) {} float Output::GetValue(int index)
float Cikti::GetValue(int index)
{ {
std::clog << "\n" << "Call GetValue!" << "\n"; float result = 0.0;
float result = (neurons + index) -> GetStatus();
std::clog << "GetValue: " << result << "\n"; if(index >= this -> neuronSize || index < 0)
return result;
result = (neurons + index) -> GetValue();
return result; return result;
} }
#pragma endregion #pragma endregion
@ -486,244 +291,110 @@ float RandomFloat(int min, int max)
class NeuralNetwork class NeuralNetwork
{ {
private: private:
Girdi *input; Input *input;
Katman *hiddenLayers; Layer *hidden;
Cikti *output; Output *output;
int hiddenSize; int hiddenSize;
public: public:
NeuralNetwork(); NeuralNetwork();
NeuralNetwork(int); NeuralNetwork(int);
~NeuralNetwork(); ~NeuralNetwork();
void SetInput(int, float);
void RandomizeNetworkValues();
void FireNetwork(); void FireNetwork();
bool SetInputNeurons(int); bool SetInputNeurons(int);
bool SetHiddenLayerNeurons(int, int); bool SetHiddenNeurons(int, int);
bool SetOutputNeurons(int); bool SetOutputNeurons(int);
bool ConnectLayers(); bool ConnectLayers();
float GetOutputValue(int); float GetOutput(int);
void SetInput(int, float);
}; };
NeuralNetwork::NeuralNetwork() NeuralNetwork::NeuralNetwork()
{ {
std::clog << "\n" << "Create NeuralNetwork: NULL" << "\n";
hiddenSize = 0; hiddenSize = 0;
input = NULL; input = NULL;
hiddenLayers = NULL; hidden = NULL;
output = NULL; output = NULL;
} }
NeuralNetwork::NeuralNetwork(int hiddenSize) NeuralNetwork::NeuralNetwork(int hiddenSize)
{ {
std::clog << "\n" << "Create NeuralNetwork: Called" << "\n";
input = new Girdi();
hiddenLayers = new Katman[hiddenSize];
output = new Cikti();
std::clog << "\n" << "Create NeuralNetwork: New Neural Network Created with " << hiddenSize << " Layers!" << "\n";
if(!input)
{
std::clog << "Error Create NeuralNetwork: Memory Couldn't Allocated for Input Layer!" << "\n";
std::cout << "Girdi Katmani Olusturulamadi!" << "\n";
}
if(!hiddenLayers)
{
std::clog << "Error Create NeuralNetwork: Memory Couldn't Allocated for Hidden Layers!" << "\n";
std::cout << "Ara Katmanlar Olusturulamadi!" << "\n";
}
if(!output)
{
std::clog << "Error Create NeuralNetwork: Memory Couldn't Allocated for Output Layer!" << "\n";
std::cout << "Cikti Katmani Olusturulamadi!" << "\n";
}
if(!input || !hiddenLayers || !output)
return;
std::clog << "Create NeuralNetwork: Succesfull!" << "\n";
this -> hiddenSize = hiddenSize; this -> hiddenSize = hiddenSize;
input = new Input();
hidden = new Layer(hiddenSize);
output = new Output();
} }
NeuralNetwork::~NeuralNetwork() NeuralNetwork::~NeuralNetwork()
{ {
delete input; if(input) delete input;
delete hiddenLayers; if(hidden) delete hidden;
delete output; if(output) delete output;
}
bool NeuralNetwork::SetHiddenLayerNeurons(int index, int size)
{
bool result;
std::clog << "\n" << "SetHiddenLayerNeurons: Size of " << size << " at Index of " << index << "\n";
std::clog << "SetHiddenLayerNeurons: Allocating Memory Size of " << size << " Neurons!" << "\n";
Noron *neurons = (Noron *) new char[sizeof(Noron) * size];
if(!neurons)
{
std::clog << "Error SetHiddenLayerNeurons: Couldn't Allocate Memory for Neurons!" << "\n";
return false;
}
std::clog << "SetHiddenLayerNeurons: Memory Allocated!" << "\n";
std::clog << "SetHiddenLayerNeurons -> CreateNoron: Called!" << "\n";
result = (hiddenLayers + index) -> CreateNoron(size);
std::clog << "SetHiddenLayerNeurons -> CreateNoron: " << result << "\n";
// if(result)
// return (hiddenLayers + index) -> SetNoron(neurons, size);
return result;
}
bool NeuralNetwork::SetInputNeurons(int size)
{
bool result;
std::clog << "\n" << "SetInputNeurons: Size of " << size << "\n";
std::clog << "SetInputNeurons: Allocating Memory Size of " << size << " Neurons!" << "\n";
Noron *neurons = (Noron *) new char[sizeof(Noron) * size];
if(!neurons)
{
std::clog << "Error SetInputNeurons: Couldn't Allocate Memory for Neurons!" << "\n";
return false;
}
std::clog << "SetInputNeurons: Memory Allocated!" << "\n";
std::clog << "SetInputNeurons -> CreateNoron: Called!" << "\n";
result = input -> CreateNoron(size);
std::clog << "SetInputNeurons -> CreateNoron: " << result << "\n";
// return output -> SetNoron(neurons, size);
return result;
}
bool NeuralNetwork::SetOutputNeurons(int size)
{
bool result;
std::clog << "\n" << "SetOutputNeurons: Size of " << size << "\n";
std::clog << "SetOutputNeurons: Allocating Memory Size of " << size << " Neurons!" << "\n";
Noron *neurons = (Noron *) new char[sizeof(Noron) * size];
if(!neurons)
{
std::clog << "Error SetOutputNeurons: Couldn't Allocate Memory for Neurons!" << "\n";
return false;
}
std::clog << "SetOutputNeurons: Memory Allocated!" << "\n";
std::clog << "SetOutputNeurons -> CreateNoron: Called!" << "\n";
result = input -> CreateNoron(size);
std::clog << "SetOutputNeurons -> CreateNoron: " << result << "\n";
// return input -> SetNoron(neurons, size);
return result;
}
bool NeuralNetwork::ConnectLayers()
{
if(!input -> SetForward(hiddenLayers))
{
std::clog << "\n" << "Error ConnectLayers: Input Couldn't Set to Forward!" << "\n";
return false;
}
std::clog << "\n" << "ConnectLayers: Input is Set to Forward Successfully!" << "\n";
for (int i = 0; i < hiddenSize - 1; i++)
if(!(hiddenLayers + i) -> SetForward(hiddenLayers + i + 1))
{
std::clog << "Error ConnectLayers: Hidden Layer " << i << " Couldn't Set to Forward!" << "\n";
return false;
}
else
std::clog << "ConnectLayers: Hidden Layer " << i << " is Set to Forward Successfully!" << "\n";
std::clog << "ConnectLayers: Hidden Layers are Set to Forward Successfully!" << "\n";
if(!(hiddenLayers + hiddenSize - 1) -> SetForward(output))
{
std::clog << "Error ConnectLayers: Output Couldn't Set to Forward!" << "\n";
return false;
}
std::clog << "ConnectLayers: Output is Set to Forward Successfully!" << "\n";
return output -> SetForward(NULL);
} }
void NeuralNetwork::FireNetwork() void NeuralNetwork::FireNetwork()
{ {
// input -> FireLayer();
// std::clog << "\n" << "FireNetwork: Input Fired!" << "\n";
for (int i = 0; i < hiddenSize; i++) for (int i = 0; i < hiddenSize; i++)
{ (hidden + i) -> FireLayer();
(hiddenLayers + i) -> FireLayer();
std::clog << "FireNetwork: Hidden Layer " << i << " Fired!" << "\n";
}
output -> FireLayer(); output -> FireLayer();
std::clog << "FireNetwork: Output Fired!" << "\n"; }
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) void NeuralNetwork::SetInput(int index, float value)
{ {
if(!input)
{
std::clog << "\n" << "SetInput: There's no Input Layer Set!" << "\n";
return;
}
std::clog << "\n" << "SetInput -> Input SetValue: SetValue(" << index << ", " << value << ")!" << "\n";
input -> SetValue(index, value); input -> SetValue(index, value);
} }
void NeuralNetwork::RandomizeNetworkValues()
{
std::clog << "\n" << "RandomizeNetworkValues: Input Sinapses Are Getting Randomized!" << "\n";
input -> RandomizeSinapsValues();
for (int i = 0; i < hiddenSize; i++)
{
std::clog << "RandomizeNetworkValues: Hidden Layer " << i << " Sinapses Are Getting Randomized!" << "\n";
(hiddenLayers + i) -> RandomizeSinapsValues();
}
std::clog << "RandomizeNetworkValues: Output Sinapses Are Getting Randomized!" << "\n";
output -> RandomizeSinapsValues();
}
float NeuralNetwork::GetOutputValue(int index)
{
float result = output -> GetValue(index);
std::clog << "\n" << "GetOutputValue: " << result << "\n";
return result;
}
#pragma endregion #pragma endregion
int main(int argc, char const *argv[]) int main(int argc, char const *argv[])
{ {
float result;
FILE *file;
NeuralNetwork network(3); NeuralNetwork network(3);
network.SetInputNeurons(1); network.SetInputNeurons(1);
network.SetHiddenLayerNeurons(0, 2); network.SetHiddenNeurons(0, 2);
network.SetHiddenLayerNeurons(1, 3); network.SetHiddenNeurons(1, 3);
network.SetHiddenLayerNeurons(2, 2); network.SetHiddenNeurons(2, 2);
network.SetOutputNeurons(1); network.SetOutputNeurons(1);
network.ConnectLayers(); network.ConnectLayers();
std::cout << "m1\n"; network.SetInput(0, 2);
network.RandomizeNetworkValues();
std::cout << "m2\n";
network.SetInput(0, 1);
std::cout << "m3\n";
network.FireNetwork(); network.FireNetwork();
result = network.GetOutputValue(0); std::cout << "Result = " << network.GetOutput(0) << "\n";
std::cout << "Output is " << result << "\n";
file = fopen("result.txt", "w");
fprintf(file, "%f", result);
fclose(file);
// std::cout << "Sinaps = " << sizeof(Sinaps) * 30 << "\n";
// std::cout << "Noron = " << sizeof(Noron) * 9 << "\n";
// std::cout << "Katman = " << sizeof(Katman) * 3 << "\n";
// std::cout << "Girdi = " << sizeof(Girdi) * 1 << "\n";
// std::cout << "Cikti = " << sizeof(Cikti) * 1 << "\n";
// std::cout << "NeuralNetwork = " << sizeof(NeuralNetwork) * 1 << "\n";
return 0; return 0;
} }