Cleaning_1
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Asrın Doğan
parent
25b1839763
commit
df6028922a
142
Genetic.cpp
142
Genetic.cpp
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@ -3,9 +3,9 @@
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#define RandomRange 1
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#define InitialSynapseValue 0.0
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#define MutationRate 0.15
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#define CrossOverRate 0.1
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#define PopCrossOverRate 0.95
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#define MutationRate 0.25
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#define CrossOverRate 0.25
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#define PopCrossOverRate 0.7
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class Synapse;
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class Neuron;
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@ -23,7 +23,6 @@ float RandomFloat(int min, int max)
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srand(time(0) * counter++);
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value = ((rand() * counter) % ((max - min) * 100000));
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result = (float)value / 100000.0 + (float)min;
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// std::cout << "random is " << result << "\n";
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return result;
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}
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@ -48,13 +47,11 @@ float RandomFloat(int min, int max)
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Synapse::Synapse()
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{
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// std::cout << "Created Synapse\n";
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this -> value = this -> weight = this -> bias = InitialSynapseValue;
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}
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Synapse::~Synapse()
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{
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// std::cout << "Deleted Synapse\n";
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}
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void Synapse::SetValue(float value)
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@ -117,7 +114,6 @@ float RandomFloat(int min, int max)
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Neuron::Neuron()
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{
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// std::cout << "Created Neuron\n";
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incomings = forwards = NULL;
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incomingsSize = forwardsSize = layerSize = 0;
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}
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@ -125,7 +121,6 @@ float RandomFloat(int min, int max)
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Neuron::~Neuron()
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{
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// std::cout << "Deleted Neuron\n";
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}
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void Neuron::Reset()
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@ -202,7 +197,6 @@ float RandomFloat(int min, int max)
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Layer::Layer()
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{
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// std::cout << "Created Layer\n";
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neuronSize = synapseSize = 0;
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neurons = NULL;
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synapses = NULL;
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@ -210,7 +204,6 @@ float RandomFloat(int min, int max)
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Layer::Layer(int size)
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{
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// std::cout << "Deleted Layer\n";
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neuronSize = synapseSize = 0;
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synapses = NULL;
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neurons = _CreateNeurons(size);
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@ -218,7 +211,6 @@ float RandomFloat(int min, int max)
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Layer::~Layer()
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{
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// std::cout << "Deleted Layer\n";
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if(neurons) delete neurons;
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if(synapses) delete synapses;
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}
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@ -330,7 +322,6 @@ float RandomFloat(int min, int max)
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delete synapses;
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synapses = NULL;
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}
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// synapses = (Synapse *) new char[sizeof(Synapse) * synapseCount];
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synapses = _CreateSynapses(synapseCount);
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if(!synapses) return false;
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@ -413,6 +404,7 @@ float RandomFloat(int min, int max)
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Layer *hidden;
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Output *output;
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int hiddenSize;
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float score;
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Input *_CreateInput();
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Layer *_CreateLayers(int);
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Output *_CreateOutput();
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@ -424,9 +416,9 @@ float RandomFloat(int min, int max)
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void FireNetwork();
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void RandomizeValues();
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void MutateNetwork();
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friend void WriteToFile(NeuralNetwork *);
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void Reset();
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void CrossOverNetwork(NeuralNetwork *);
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friend void WriteToFile(NeuralNetwork *);
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bool SetInputNeurons(int);
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bool SetHiddenNeurons(int, int);
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bool SetOutputNeurons(int);
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@ -434,8 +426,10 @@ float RandomFloat(int min, int max)
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bool SetLayer(int);
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float GetOutput(int);
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float GetError(int, float);
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float GetScore(int);
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float GetPrediction(int);
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float GetScore();
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int GetHiddenSize();
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void SetScore(float);
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void SetInput(float, int);
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};
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@ -465,7 +459,6 @@ float RandomFloat(int min, int max)
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NeuralNetwork::NeuralNetwork()
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{
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// std::cout << "Created NeuralNetwork\n";
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hiddenSize = 0;
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input = NULL;
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hidden = NULL;
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@ -474,7 +467,6 @@ float RandomFloat(int min, int max)
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NeuralNetwork::NeuralNetwork(int hiddenSize)
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{
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// std::cout << "Created NeuralNetwork\n";
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this -> hiddenSize = hiddenSize;
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input = _CreateInput();
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hidden = _CreateLayers(hiddenSize);
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@ -483,7 +475,6 @@ float RandomFloat(int min, int max)
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NeuralNetwork::~NeuralNetwork()
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{
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// std::cout << "Deleted NeuralNetwork\n";
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if(input) delete input;
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if(hidden) delete hidden;
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if(output) delete output;
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@ -644,12 +635,17 @@ float RandomFloat(int min, int max)
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return result < 0.0 ? -result : result;
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}
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float NeuralNetwork::GetScore(int index = 0)
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float NeuralNetwork::GetPrediction(int index = 0)
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{
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float result = GetOutput(index);
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return result;
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}
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float NeuralNetwork::GetScore()
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{
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return score;
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}
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int NeuralNetwork::GetHiddenSize()
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{
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return hiddenSize;
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@ -659,6 +655,11 @@ float RandomFloat(int min, int max)
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{
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input -> SetValue(value, index);
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}
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void NeuralNetwork::SetScore(float value)
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{
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score = value;
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}
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#pragma endregion
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#pragma region Generation
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class Generation
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@ -676,27 +677,27 @@ float RandomFloat(int min, int max)
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~Generation();
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void Randomize();
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void Fire();
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void SortByScore(int);
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void SortByScoreArray(float *, int, int);
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void SortByScore();
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void DisplayScores(int);
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void DisplayBest(int);
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void SetTarget(float);
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void SetInput(float, int);
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void NextGeneration();
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void WriteBestToFile();
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void UpdateScores();
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void ResetScores();
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bool CreateNetworks(int, int);
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bool ConnectNetworks();
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bool SetInputNeurons(int);
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bool SetHiddenNeurons(int, int);
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bool SetOutputNeurons(int);
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float GetBest(int);
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float GetBestPrediction(int);
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float GetError(int);
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int GetStep();
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};
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Generation::Generation()
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{
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// std::cout << "Created Generation\n";
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step = 0;
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networks = NULL;
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size = 0;
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@ -705,7 +706,6 @@ float RandomFloat(int min, int max)
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Generation::Generation(int size, int hiddenSizes)
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{
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// std::cout << "Created Generation\n";
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step = 0;
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target = 0.0;
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this -> size = size;
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@ -714,7 +714,6 @@ float RandomFloat(int min, int max)
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Generation::~Generation()
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{
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// std::cout << "Deleted Generation\n";
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if(networks) delete networks;
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}
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@ -764,44 +763,45 @@ float RandomFloat(int min, int max)
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void Generation::DisplayBest(int index = 0)
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{
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std::cout << "Target -> " << target << "\tBest -> " << networks -> GetScore(index) << "\n";
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std::cout << "Target -> " << target << "\tBest -> " << networks -> GetPrediction(index) << "\n";
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}
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float Generation::GetBest(int index = 0)
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void Generation::UpdateScores()
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{
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return networks -> GetScore(index);
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float scoreToAdd;
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int i;
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for (i = 0; i < size; i++)
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{
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scoreToAdd = (networks + i) -> GetError(0, target);
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(networks + i) -> SetScore((networks + i) -> GetScore() + scoreToAdd);
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}
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}
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void Generation::ResetScores()
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{
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int i;
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for (i = 0; i < size; i++)
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(networks + i) -> SetScore(0.0);
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}
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float Generation::GetBestPrediction(int index = 0)
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{
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return networks -> GetPrediction(index);
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}
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float Generation::GetError(int index = 0)
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{
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return (networks + index) -> GetError(0, target);
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}
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void Generation::SortByScore(int index = 0)
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void Generation::SortByScore()
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{
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int i;
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int j;
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for (i = 0; i < size - 1; i++)
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for (j = i + 1; j < size; j++)
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if((networks + i) -> GetError(index, target) > (networks + j) -> GetError(index, target))
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if((networks + i) -> GetScore() > (networks + j) -> GetScore())
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_SwapNetworks((networks + i), (networks + j));
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}
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void Generation::SortByScoreArray(float *array, int size, int index = 0)
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{
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int i;
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int j;
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float temp;
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for (i = 0; i < size - 1; i++)
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for (j = i + 1; j < size; j++)
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if(*(array + i) > *(array + j))
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{
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temp = *(array + i);
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*(array + i) = *(array + j);
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*(array + j) = temp;
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_SwapNetworks((networks + i), (networks + j));
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}
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// if((networks + i) -> GetError(index, target) > (networks + j) -> GetError(index, target))
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}
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void Generation::SetTarget(float target)
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{
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@ -832,7 +832,6 @@ float RandomFloat(int min, int max)
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NeuralNetwork *second = NULL;
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Fire();
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// for (; i < size; i+=2)
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for (i = 2; i < crossOverCount; i+=2)
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{
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first = (networks + i);
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@ -849,7 +848,6 @@ float RandomFloat(int min, int max)
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second -> MutateNetwork();
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}
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}
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// SortByScore();
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for (; i < size; i++)
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(networks + i) -> RandomizeValues();
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@ -905,8 +903,6 @@ float RandomFloat(int min, int max)
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}
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#pragma endregion
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int main()
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{
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FILE *inputFile;
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@ -917,20 +913,18 @@ int main()
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int inputCounter;
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int floatCounter;
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int i;
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int j;
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int generationCounter;
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int groupSize;
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float trainInputs[30][5];
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float scores[50];
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float testInputs[120][5];
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Generation generation(50, 5);
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inputFile = fopen("Data/train.data", "r");
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for (inputCounter = 0; inputCounter < 30; inputCounter++)
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for (floatCounter = 0; floatCounter < 5; floatCounter++)
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fscanf(inputFile, "%f,", &trainInputs[inputCounter][floatCounter]);
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fclose(inputFile);
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inputFile = fopen("Data/test.data", "r");
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for (inputCounter = 0; inputCounter < 150; inputCounter++)
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for (inputCounter = 0; inputCounter < 120; inputCounter++)
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for (floatCounter = 0; floatCounter < 5; floatCounter++)
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fscanf(inputFile, "%f,", &testInputs[inputCounter][floatCounter]);
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fclose(inputFile);
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@ -971,31 +965,23 @@ int main()
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std::cout << trainCounter++ << "\n";
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for (inputCounter = 0; inputCounter < 10; inputCounter++)
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{
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// for (generationCounter = 0; generationCounter < 25; generationCounter++)
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// {
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for (j = 0; j < 50; j++)
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scores[j] = 0.0;
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for (i = 0; i < 3; i++)
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{
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for (floatCounter = 0; floatCounter < 4; floatCounter++)
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generation.SetInput(trainInputs[inputCounter * 3 + i][floatCounter], floatCounter);
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generation.SetTarget(trainInputs[inputCounter * 3 + i][4]);
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generation.Fire();
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for (j = 0; j < 50; j++)
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scores[j] += generation.GetError(j);
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}
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generation.SortByScoreArray(scores, 50);
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generation.NextGeneration();
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// generation.NextGeneration();
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// }
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generation.ResetScores();
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for (groupSize = 0; groupSize < 3; groupSize++)
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{
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for (floatCounter = 0; floatCounter < 4; floatCounter++)
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generation.SetInput(trainInputs[inputCounter * 3 + groupSize][floatCounter], floatCounter);
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generation.SetTarget(trainInputs[inputCounter * 3 + groupSize][4]);
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generation.Fire();
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generation.UpdateScores();
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}
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generation.SortByScore();
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generation.NextGeneration();
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}
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}
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std::cout << "Best -> " << scores[0] << "\n";
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std::cout << "Best -> " << generation.GetError() << "\n";
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std::cout << "Train is Over!\n";
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case -2:
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outputFile = fopen("Data/results.data", "w");
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trainCounter = 0;
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for (inputCounter = 0; inputCounter < 120; inputCounter++)
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{
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for (floatCounter = 0; floatCounter < 4; floatCounter++)
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@ -1003,10 +989,8 @@ int main()
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generation.SetTarget(testInputs[inputCounter][4]);
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generation.Fire();
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// generation.DisplayBest();
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currentError = testInputs[inputCounter][4] - generation.GetBest() < 0 ? generation.GetBest() - testInputs[inputCounter][4] : testInputs[inputCounter][4] - generation.GetBest();
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// fprintf(outputFile, "Original = %f\t->\tTrained -> %f\t Error = %f\n", testInputs[inputCounter][4], generation.GetBest(), currentError);
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fprintf(outputFile, "%f,%f,%f\n", testInputs[inputCounter][4], generation.GetBest(), currentError);
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currentError = testInputs[inputCounter][4] - generation.GetBestPrediction() < 0 ? generation.GetBestPrediction() - testInputs[inputCounter][4] : testInputs[inputCounter][4] - generation.GetBestPrediction();
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fprintf(outputFile, "%f,%f,%f\n", testInputs[inputCounter][4], generation.GetBestPrediction(), currentError);
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}
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fclose(outputFile);
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std::cout << "Test is Over!\n";
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