Syntriax/NeuralNetworkGenetic
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Cleaning_1

Browse Source
This commit is contained in:
Asrın Doğan
2019-12-24 18:57:26 +03:00
parent 25b1839763
commit df6028922a
1 changed files with 63 additions and 79 deletions
Download Patch File Download Diff File Expand all files Collapse all files

142
Genetic.cpp
View File

@@ -3,9 +3,9 @@
#define RandomRange 1
#define InitialSynapseValue 0.0
#define MutationRate 0.15
#define CrossOverRate 0.1
#define PopCrossOverRate 0.95
#define MutationRate 0.25
#define CrossOverRate 0.25
#define PopCrossOverRate 0.7
class Synapse;
class Neuron;
@@ -23,7 +23,6 @@ float RandomFloat(int min, int max)
srand(time(0) * counter++);
value = ((rand() * counter) % ((max - min) * 100000));
result = (float)value / 100000.0 + (float)min;
// std::cout << "random is " << result << "\n";
return result;
}
@@ -48,13 +47,11 @@ float RandomFloat(int min, int max)
Synapse::Synapse()
{
// std::cout << "Created Synapse\n";
this -> value = this -> weight = this -> bias = InitialSynapseValue;
}
Synapse::~Synapse()
{
// std::cout << "Deleted Synapse\n";
}
void Synapse::SetValue(float value)
@@ -117,7 +114,6 @@ float RandomFloat(int min, int max)
Neuron::Neuron()
{
// std::cout << "Created Neuron\n";
incomings = forwards = NULL;
incomingsSize = forwardsSize = layerSize = 0;
}
@@ -125,7 +121,6 @@ float RandomFloat(int min, int max)
Neuron::~Neuron()
{
// std::cout << "Deleted Neuron\n";
}
void Neuron::Reset()
@@ -202,7 +197,6 @@ float RandomFloat(int min, int max)
Layer::Layer()
{
// std::cout << "Created Layer\n";
neuronSize = synapseSize = 0;
neurons = NULL;
synapses = NULL;
@@ -210,7 +204,6 @@ float RandomFloat(int min, int max)
Layer::Layer(int size)
{
// std::cout << "Deleted Layer\n";
neuronSize = synapseSize = 0;
synapses = NULL;
neurons = _CreateNeurons(size);
@@ -218,7 +211,6 @@ float RandomFloat(int min, int max)
Layer::~Layer()
{
// std::cout << "Deleted Layer\n";
if(neurons) delete neurons;
if(synapses) delete synapses;
}
@@ -330,7 +322,6 @@ float RandomFloat(int min, int max)
delete synapses;
synapses = NULL;
}
// synapses = (Synapse *) new char[sizeof(Synapse) * synapseCount];
synapses = _CreateSynapses(synapseCount);
if(!synapses) return false;
@@ -413,6 +404,7 @@ float RandomFloat(int min, int max)
Layer *hidden;
Output *output;
int hiddenSize;
float score;
Input *_CreateInput();
Layer *_CreateLayers(int);
Output *_CreateOutput();
@@ -424,9 +416,9 @@ float RandomFloat(int min, int max)
void FireNetwork();
void RandomizeValues();
void MutateNetwork();
friend void WriteToFile(NeuralNetwork *);
void Reset();
void CrossOverNetwork(NeuralNetwork *);
friend void WriteToFile(NeuralNetwork *);
bool SetInputNeurons(int);
bool SetHiddenNeurons(int, int);
bool SetOutputNeurons(int);
@@ -434,8 +426,10 @@ float RandomFloat(int min, int max)
bool SetLayer(int);
float GetOutput(int);
float GetError(int, float);
float GetScore(int);
float GetPrediction(int);
float GetScore();
int GetHiddenSize();
void SetScore(float);
void SetInput(float, int);
};
@@ -465,7 +459,6 @@ float RandomFloat(int min, int max)
NeuralNetwork::NeuralNetwork()
{
// std::cout << "Created NeuralNetwork\n";
hiddenSize = 0;
input = NULL;
hidden = NULL;
@@ -474,7 +467,6 @@ float RandomFloat(int min, int max)
NeuralNetwork::NeuralNetwork(int hiddenSize)
{
// std::cout << "Created NeuralNetwork\n";
this -> hiddenSize = hiddenSize;
input = _CreateInput();
hidden = _CreateLayers(hiddenSize);
@@ -483,7 +475,6 @@ float RandomFloat(int min, int max)
NeuralNetwork::~NeuralNetwork()
{
// std::cout << "Deleted NeuralNetwork\n";
if(input) delete input;
if(hidden) delete hidden;
if(output) delete output;
@@ -644,12 +635,17 @@ float RandomFloat(int min, int max)
return result < 0.0 ? -result : result;
}
float NeuralNetwork::GetScore(int index = 0)
float NeuralNetwork::GetPrediction(int index = 0)
{
float result = GetOutput(index);
return result;
}
float NeuralNetwork::GetScore()
{
return score;
}
int NeuralNetwork::GetHiddenSize()
{
return hiddenSize;
@@ -659,6 +655,11 @@ float RandomFloat(int min, int max)
{
input -> SetValue(value, index);
}
void NeuralNetwork::SetScore(float value)
{
score = value;
}
#pragma endregion
#pragma region Generation
class Generation
@@ -676,27 +677,27 @@ float RandomFloat(int min, int max)
~Generation();
void Randomize();
void Fire();
void SortByScore(int);
void SortByScoreArray(float *, int, int);
void SortByScore();
void DisplayScores(int);
void DisplayBest(int);
void SetTarget(float);
void SetInput(float, int);
void NextGeneration();
void WriteBestToFile();
void UpdateScores();
void ResetScores();
bool CreateNetworks(int, int);
bool ConnectNetworks();
bool SetInputNeurons(int);
bool SetHiddenNeurons(int, int);
bool SetOutputNeurons(int);
float GetBest(int);
float GetBestPrediction(int);
float GetError(int);
int GetStep();
};
Generation::Generation()
{
// std::cout << "Created Generation\n";
step = 0;
networks = NULL;
size = 0;
@@ -705,7 +706,6 @@ float RandomFloat(int min, int max)
Generation::Generation(int size, int hiddenSizes)
{
// std::cout << "Created Generation\n";
step = 0;
target = 0.0;
this -> size = size;
@@ -714,7 +714,6 @@ float RandomFloat(int min, int max)
Generation::~Generation()
{
// std::cout << "Deleted Generation\n";
if(networks) delete networks;
}
@@ -764,44 +763,45 @@ float RandomFloat(int min, int max)
void Generation::DisplayBest(int index = 0)
{
std::cout << "Target -> " << target << "\tBest -> " << networks -> GetScore(index) << "\n";
std::cout << "Target -> " << target << "\tBest -> " << networks -> GetPrediction(index) << "\n";
}
float Generation::GetBest(int index = 0)
void Generation::UpdateScores()
{
return networks -> GetScore(index);
float scoreToAdd;
int i;
for (i = 0; i < size; i++)
{
scoreToAdd = (networks + i) -> GetError(0, target);
(networks + i) -> SetScore((networks + i) -> GetScore() + scoreToAdd);
}
}
void Generation::ResetScores()
{
int i;
for (i = 0; i < size; i++)
(networks + i) -> SetScore(0.0);
}
float Generation::GetBestPrediction(int index = 0)
{
return networks -> GetPrediction(index);
}
float Generation::GetError(int index = 0)
{
return (networks + index) -> GetError(0, target);
}
void Generation::SortByScore(int index = 0)
void Generation::SortByScore()
{
int i;
int j;
for (i = 0; i < size - 1; i++)
for (j = i + 1; j < size; j++)
if((networks + i) -> GetError(index, target) > (networks + j) -> GetError(index, target))
if((networks + i) -> GetScore() > (networks + j) -> GetScore())
_SwapNetworks((networks + i), (networks + j));
}
void Generation::SortByScoreArray(float *array, int size, int index = 0)
{
int i;
int j;
float temp;
for (i = 0; i < size - 1; i++)
for (j = i + 1; j < size; j++)
if(*(array + i) > *(array + j))
{
temp = *(array + i);
*(array + i) = *(array + j);
*(array + j) = temp;
_SwapNetworks((networks + i), (networks + j));
}
// if((networks + i) -> GetError(index, target) > (networks + j) -> GetError(index, target))
}
void Generation::SetTarget(float target)
{
@@ -832,7 +832,6 @@ float RandomFloat(int min, int max)
NeuralNetwork *second = NULL;
Fire();
// for (; i < size; i+=2)
for (i = 2; i < crossOverCount; i+=2)
{
first = (networks + i);
@@ -849,7 +848,6 @@ float RandomFloat(int min, int max)
second -> MutateNetwork();
}
}
// SortByScore();
for (; i < size; i++)
(networks + i) -> RandomizeValues();
@@ -905,8 +903,6 @@ float RandomFloat(int min, int max)
}
#pragma endregion
int main()
{
FILE *inputFile;
@@ -917,20 +913,18 @@ int main()
int inputCounter;
int floatCounter;
int i;
int j;
int generationCounter;
int groupSize;
float trainInputs[30][5];
float scores[50];
float 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]);
fclose(inputFile);
inputFile = fopen("Data/test.data", "r");
for (inputCounter = 0; inputCounter < 150; inputCounter++)
for (inputCounter = 0; inputCounter < 120; inputCounter++)
for (floatCounter = 0; floatCounter < 5; floatCounter++)
fscanf(inputFile, "%f,", &testInputs[inputCounter][floatCounter]);
fclose(inputFile);
@@ -971,31 +965,23 @@ int main()
std::cout << trainCounter++ << "\n";
for (inputCounter = 0; inputCounter < 10; inputCounter++)
{
// for (generationCounter = 0; generationCounter < 25; generationCounter++)
// {
for (j = 0; j < 50; j++)
scores[j] = 0.0;
for (i = 0; i < 3; i++)
{
for (floatCounter = 0; floatCounter < 4; floatCounter++)
generation.SetInput(trainInputs[inputCounter * 3 + i][floatCounter], floatCounter);
generation.SetTarget(trainInputs[inputCounter * 3 + i][4]);
generation.Fire();
for (j = 0; j < 50; j++)
scores[j] += generation.GetError(j);
}
generation.SortByScoreArray(scores, 50);
generation.NextGeneration();
// generation.NextGeneration();
// }
generation.ResetScores();
for (groupSize = 0; groupSize < 3; groupSize++)
{
for (floatCounter = 0; floatCounter < 4; floatCounter++)
generation.SetInput(trainInputs[inputCounter * 3 + groupSize][floatCounter], floatCounter);
generation.SetTarget(trainInputs[inputCounter * 3 + groupSize][4]);
generation.Fire();
generation.UpdateScores();
}
generation.SortByScore();
generation.NextGeneration();
}
}
std::cout << "Best -> " << scores[0] << "\n";
std::cout << "Best -> " << generation.GetError() << "\n";
std::cout << "Train is Over!\n";
case -2:
outputFile = fopen("Data/results.data", "w");
trainCounter = 0;
for (inputCounter = 0; inputCounter < 120; inputCounter++)
{
for (floatCounter = 0; floatCounter < 4; floatCounter++)
@@ -1003,10 +989,8 @@ int main()
generation.SetTarget(testInputs[inputCounter][4]);
generation.Fire();
// generation.DisplayBest();
currentError = testInputs[inputCounter][4] - generation.GetBest() < 0 ? generation.GetBest() - testInputs[inputCounter][4] : testInputs[inputCounter][4] - generation.GetBest();
// fprintf(outputFile, "Original = %f\t->\tTrained -> %f\t Error = %f\n", testInputs[inputCounter][4], generation.GetBest(), currentError);
fprintf(outputFile, "%f,%f,%f\n", testInputs[inputCounter][4], generation.GetBest(), currentError);
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);
}
fclose(outputFile);
std::cout << "Test is Over!\n";