Syntriax/NeuralNetworkGenetic
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Floats to Double and Signature

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Asrın Doğan
2019-12-24 21:36:20 +03:00
parent 4475fd3c7f
commit 29da090c99
1 changed files with 99 additions and 91 deletions
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190
Genetic.cpp
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@@ -1,3 +1,7 @@
/*
Author: Asrın "Syntriax" Doğan
Mail: asrindogan99@gmail.com
*/
#include <iostream>
#include <time.h>
@@ -5,7 +9,7 @@
#define InitialSynapseValue 0.0
#define MutationRate 0.25
#define CrossOverRate 0.25
#define PopCrossOverRate 0.7
#define PopCrossOverRate 0.75
class Synapse;
class Neuron;
@@ -15,14 +19,14 @@ class Output;
class NeuralNetwork;
class Generation;
float RandomFloat(int min, int max)
double RandomDouble(int min, int max)
{
float result;
int value;
double result;
long 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;
value = ((rand() * counter) % ((max - min) * 100000000));
result = (double)value / 100000000.0 + (double)min;
return result;
}
@@ -30,19 +34,19 @@ float RandomFloat(int min, int max)
class Synapse
{
private:
float weight;
float value;
float bias;
double weight;
double value;
double bias;
public:
Synapse();
~Synapse();
void SetValue(float);
void SetWeight(float);
void SetBias(float);
float GetWeight();
float GetValue();
float GetBias();
float Fire();
void SetValue(double);
void SetWeight(double);
void SetBias(double);
double GetWeight();
double GetValue();
double GetBias();
double Fire();
};
Synapse::Synapse()
@@ -54,39 +58,39 @@ float RandomFloat(int min, int max)
{
}
void Synapse::SetValue(float value)
void Synapse::SetValue(double value)
{
this -> value = value;
}
void Synapse::SetWeight(float weight)
void Synapse::SetWeight(double weight)
{
this -> weight = weight;
}
void Synapse::SetBias(float bias)
void Synapse::SetBias(double bias)
{
this -> bias = bias;
}
float Synapse::GetWeight()
double Synapse::GetWeight()
{
return weight;
}
float Synapse::GetValue()
double Synapse::GetValue()
{
return value;
}
float Synapse::GetBias()
double Synapse::GetBias()
{
return bias;
}
float Synapse::Fire()
double Synapse::Fire()
{
float result = 0.0;
double result = 0.0;
result = this -> value * this -> weight + this -> bias;
@@ -107,9 +111,9 @@ float RandomFloat(int min, int max)
~Neuron();
void ConnectIncomings(Synapse *, int);
void ConnectForwards(Synapse *, int, int);
void SetValue(float);
void SetValue(double);
void Reset();
float GetValue();
double GetValue();
};
Neuron::Neuron()
@@ -129,7 +133,7 @@ float RandomFloat(int min, int max)
incomingsSize = forwardsSize = layerSize = 0;
}
void Neuron::SetValue(float value)
void Neuron::SetValue(double value)
{
int i;
for (i = 0; i < forwardsSize; i++)
@@ -149,10 +153,10 @@ float RandomFloat(int min, int max)
this -> layerSize = layerSize;
}
float Neuron::GetValue()
double Neuron::GetValue()
{
int i;
float result = 0.0;
double result = 0.0;
if(!incomings) return result;
@@ -261,14 +265,14 @@ float RandomFloat(int min, int max)
void Layer::RandomizeValues()
{
float bias;
float weight;
double bias;
double weight;
int i;
for (i = 0; i < synapseSize; i++)
{
bias = RandomFloat(-RandomRange, RandomRange);
weight = RandomFloat(-RandomRange, RandomRange);
bias = RandomDouble(-RandomRange, RandomRange);
weight = RandomDouble(-RandomRange, RandomRange);
(synapses + i) -> SetBias(bias);
(synapses + i) -> SetWeight(weight);
}
@@ -276,18 +280,18 @@ float RandomFloat(int min, int max)
void Layer::Mutate()
{
float bias = 0.0;
float weight = 0.0;
float mutationValue = 0.0;
double bias = 0.0;
double weight = 0.0;
double mutationValue = 0.0;
int i;
for (i = 0; i < synapseSize; i++)
{
mutationValue = RandomFloat(0, 1);
mutationValue = RandomDouble(0, 1);
if(mutationValue <= MutationRate)
{
bias = RandomFloat(-RandomRange, RandomRange);
weight = RandomFloat(-RandomRange, RandomRange);
bias = RandomDouble(-RandomRange, RandomRange);
weight = RandomDouble(-RandomRange, RandomRange);
(synapses + i) -> SetBias(bias);
(synapses + i) -> SetWeight(weight);
}
@@ -298,7 +302,7 @@ float RandomFloat(int min, int max)
{
int thisCounter;
for (thisCounter = 0; thisCounter < synapseSize; thisCounter++)
if(RandomFloat(0, 1) < CrossOverRate)
if(RandomDouble(0, 1) < CrossOverRate)
_SwapSynapses((synapses + thisCounter), (other -> synapses + thisCounter));
}
@@ -364,11 +368,11 @@ float RandomFloat(int min, int max)
{
public:
Input();
void SetValue(float, int);
void SetValue(double, int);
};
Input::Input() : Layer() {}
void Input::SetValue(float value, int index = 0)
void Input::SetValue(double value, int index = 0)
{
if(index >= this -> neuronSize || index < 0)
return;
@@ -380,13 +384,13 @@ float RandomFloat(int min, int max)
{
public:
Output();
float GetValue(int);
double GetValue(int);
};
Output::Output() : Layer() {}
float Output::GetValue(int index = 0)
double Output::GetValue(int index = 0)
{
float result = 0.0;
double result = 0.0;
if(index >= this -> neuronSize || index < 0)
return result;
@@ -404,7 +408,7 @@ float RandomFloat(int min, int max)
Layer *hidden;
Output *output;
int hiddenSize;
float score;
double score;
Input *_CreateInput();
Layer *_CreateLayers(int);
Output *_CreateOutput();
@@ -424,13 +428,13 @@ float RandomFloat(int min, int max)
bool SetOutputNeurons(int);
bool ConnectLayers();
bool SetLayer(int);
float GetOutput(int);
float GetError(int, float);
float GetPrediction(int);
float GetScore();
double GetOutput(int);
double GetError(int, double);
double GetPrediction(int);
double GetScore();
int GetHiddenSize();
void SetScore(float);
void SetInput(float, int);
void SetScore(double);
void SetInput(double, int);
};
Input *NeuralNetwork::_CreateInput()
@@ -548,7 +552,7 @@ float RandomFloat(int min, int max)
FILE *file = fopen("Data/BestSynapses.txt", "w");
for (i = 0; i < count; i++)
{
fprintf(file, "%f, %f, ", synapsePtr -> GetWeight(), synapsePtr -> GetBias());
fprintf(file, "%lf, %lf, ", synapsePtr -> GetWeight(), synapsePtr -> GetBias());
synapsePtr++;
}
@@ -559,7 +563,7 @@ float RandomFloat(int min, int max)
synapsePtr = (network -> hidden + j) -> synapses;
for (i = 0; i < count; i++)
{
fprintf(file, "%f, %f, ", synapsePtr -> GetWeight(), synapsePtr -> GetBias());
fprintf(file, "%lf, %lf, ", synapsePtr -> GetWeight(), synapsePtr -> GetBias());
synapsePtr++;
}
}
@@ -570,7 +574,7 @@ float RandomFloat(int min, int max)
std::cout << count << "\n";
for (i = 0; i < count; i++)
{
fprintf(file, "%f, %f, ", synapsePtr -> GetWeight(), synapsePtr -> GetBias());
fprintf(file, "%lf, %lf, ", synapsePtr -> GetWeight(), synapsePtr -> GetBias());
synapsePtr++;
}
fclose(file);
@@ -624,24 +628,24 @@ float RandomFloat(int min, int max)
return input && hidden && output;
}
float NeuralNetwork::GetOutput(int index = 0)
double NeuralNetwork::GetOutput(int index = 0)
{
return output -> GetValue(index);
}
float NeuralNetwork::GetError(int index = 0, float target = 0.0)
double NeuralNetwork::GetError(int index = 0, double target = 0.0)
{
float result = GetOutput(index) - target;
double result = GetOutput(index) - target;
return result < 0.0 ? -result : result;
}
float NeuralNetwork::GetPrediction(int index = 0)
double NeuralNetwork::GetPrediction(int index = 0)
{
float result = GetOutput(index);
double result = GetOutput(index);
return result;
}
float NeuralNetwork::GetScore()
double NeuralNetwork::GetScore()
{
return score;
}
@@ -651,12 +655,12 @@ float RandomFloat(int min, int max)
return hiddenSize;
}
void NeuralNetwork::SetInput(float value, int index = 0)
void NeuralNetwork::SetInput(double value, int index = 0)
{
input -> SetValue(value, index);
}
void NeuralNetwork::SetScore(float value)
void NeuralNetwork::SetScore(double value)
{
score = value;
}
@@ -668,7 +672,7 @@ float RandomFloat(int min, int max)
NeuralNetwork *networks;
int size;
int step;
float target;
double target;
void _SwapNetworks(NeuralNetwork *, NeuralNetwork *);
NeuralNetwork *_CreateNetworks(int, int);
public:
@@ -680,8 +684,8 @@ float RandomFloat(int min, int max)
void SortByScore();
void DisplayScores(int);
void DisplayBest(int);
void SetTarget(float);
void SetInput(float, int);
void SetTarget(double);
void SetInput(double, int);
void NextGeneration();
void WriteBestToFile();
void UpdateScores();
@@ -691,8 +695,8 @@ float RandomFloat(int min, int max)
bool SetInputNeurons(int);
bool SetHiddenNeurons(int, int);
bool SetOutputNeurons(int);
float GetBestPrediction(int);
float GetError(int);
double GetBestPrediction(int);
double GetError(int);
int GetStep();
};
@@ -768,7 +772,7 @@ float RandomFloat(int min, int max)
void Generation::UpdateScores()
{
float scoreToAdd;
double scoreToAdd;
int i;
for (i = 0; i < size; i++)
{
@@ -784,15 +788,16 @@ float RandomFloat(int min, int max)
(networks + i) -> SetScore(0.0);
}
float Generation::GetBestPrediction(int index = 0)
double Generation::GetBestPrediction(int index = 0)
{
return networks -> GetPrediction(index);
}
float Generation::GetError(int index = 0)
double Generation::GetError(int index = 0)
{
return (networks + index) -> GetError(0, target);
}
void Generation::SortByScore()
{
int i;
@@ -803,12 +808,12 @@ float RandomFloat(int min, int max)
_SwapNetworks((networks + i), (networks + j));
}
void Generation::SetTarget(float target)
void Generation::SetTarget(double target)
{
this -> target = target;
}
void Generation::SetInput(float value, int index = 0)
void Generation::SetInput(double value, int index = 0)
{
int i;
for (i = 0; i < this -> size; i++)
@@ -840,7 +845,7 @@ float RandomFloat(int min, int max)
first -> Copy(*(networks + 0));
second -> Copy(*(networks + 1));
if(RandomFloat(0, 1) < 0.5)
if(RandomDouble(0, 1) < 0.5)
first -> CrossOverNetwork(second);
else
{
@@ -897,6 +902,7 @@ float RandomFloat(int min, int max)
return false;
return true;
}
int Generation::GetStep()
{
return step;
@@ -908,30 +914,30 @@ int main()
FILE *inputFile;
FILE *outputFile;
int decision;
float currentError;
double currentError;
int trainCounter;
int inputCounter;
int floatCounter;
int doubleCounter;
int groupSize;
float trainInputs[30][5];
float testInputs[120][5];
double trainInputs[30][5];
double testInputs[120][5];
Generation generation(50, 5);
inputFile = fopen("Data/train.data", "r");
for (inputCounter = 0; inputCounter < 30; inputCounter++)
for (floatCounter = 0; floatCounter < 5; floatCounter++)
fscanf(inputFile, "%f,", &trainInputs[inputCounter][floatCounter]);
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 (floatCounter = 0; floatCounter < 5; floatCounter++)
fscanf(inputFile, "%f,", &testInputs[inputCounter][floatCounter]);
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 << (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: ";
@@ -943,15 +949,17 @@ int main()
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 << (generation.SetOutputNeurons(1) ? "Successfull!" : "Failed!") << "\n";
std::cout << "Networks Are Getting Connected: ";
std::cout << (generation.ConnectNetworks() ? "Successfull!" : "Failed!") << "\n";
std::cout << (generation.ConnectNetworks() ? "Successfull!" : "Failed!") << "\n";
std::cout << "Networks Are Getting Randomized: ";
generation.Randomize();
std::cout << "Done!\n";
do
{
std::cout << "[-1] Test\n[-2] Best to File\n[-3] Exit\nAny Positive Number for train count\nDecision: ";
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)
@@ -971,8 +979,8 @@ int main()
generation.ResetScores();
for (groupSize = 0; groupSize < 3; groupSize++)
{
for (floatCounter = 0; floatCounter < 4; floatCounter++)
generation.SetInput(trainInputs[inputCounter * 3 + groupSize][floatCounter], floatCounter);
for (doubleCounter = 0; doubleCounter < 4; doubleCounter++)
generation.SetInput(trainInputs[inputCounter * 3 + groupSize][doubleCounter], doubleCounter);
generation.SetTarget(trainInputs[inputCounter * 3 + groupSize][4]);
generation.Fire();
generation.UpdateScores();
@@ -988,13 +996,13 @@ int main()
outputFile = fopen("Data/results.data", "w");
for (inputCounter = 0; inputCounter < 120; inputCounter++)
{
for (floatCounter = 0; floatCounter < 4; floatCounter++)
generation.SetInput(testInputs[inputCounter][floatCounter], floatCounter);
for (doubleCounter = 0; doubleCounter < 4; doubleCounter++)
generation.SetInput(testInputs[inputCounter][doubleCounter], doubleCounter);
generation.SetTarget(testInputs[inputCounter][4]);
generation.Fire();
currentError = testInputs[inputCounter][4] - generation.GetBestPrediction() < 0 ? generation.GetBestPrediction() - testInputs[inputCounter][4] : testInputs[inputCounter][4] - generation.GetBestPrediction();
fprintf(outputFile, "%f,%f,%f\n", testInputs[inputCounter][4], generation.GetBestPrediction(), currentError);
fprintf(outputFile, "%lf,%lf,%lf\n", testInputs[inputCounter][4], generation.GetBestPrediction(), currentError);
}
fclose(outputFile);
std::cout << "Test is Over!\n";