entropy size change added
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@@ -42,9 +42,7 @@ void Shin::NeuronNetwork::Learning::BackPropagation::propagate(const Shin::Neuro
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else
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max=network[i-1]->size();
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size_t j=1;
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int size=network[i]->size();
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for(j=1;j<size;j++)
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for(size_t j=1;j<network[i]->size();j++)
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{
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network[i]->operator[](j)->setWeight(0,network[i]->operator[](j)->getWeight(0)+deltas[i][j]*learningCoeficient);
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for(size_t k=1;k<max;k++)
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@@ -73,7 +71,7 @@ double Shin::NeuronNetwork::Learning::BackPropagation::teach(const Shin::NeuronN
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{
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for(size_t i=0;i<solution.size();i++)
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{
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s.push_back(solution[i]*((double)(990+(rand()%21))/1000.0));
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s.push_back(solution[i]*((double)((100000-entropySize)+(rand()%(entropySize*2+1)))/100000.0));
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}
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propagate(s);
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}else
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@@ -9,8 +9,14 @@
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#include "Supervised"
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/*
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* http://sydney.edu.au/engineering/it/~comp4302/ann4-3s.pdf
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* http://www.cs.cmu.edu/afs/cs/academic/class/15883-f13/slides/backprop.pdf
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* http://airccse.org/journal/jcsit/0211ijcsit08.pdf
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* http://www.cedar.buffalo.edu/~srihari/CSE574/Chap5/Chap5.3-BackProp.pdf
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* http://stackoverflow.com/questions/13095938/can-somebody-please-explain-the-backpropagation-algorithm-to-me
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* http://ufldl.stanford.edu/wiki/index.php/Backpropagation_Algorithm
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*
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*
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* http://www.cleveralgorithms.com/nature-inspired/neural/backpropagation.html
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*
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*/
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@@ -29,9 +35,11 @@ namespace Learning
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void setLearningCoeficient (double);
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void allowEntropy() {entropy=1;}
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void setEntropySize(int milipercents) { entropySize=milipercents; }
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protected:
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double learningCoeficient=0.4;
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bool entropy=1;
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bool entropy=0;
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int entropySize=500;
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};
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}
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}
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@@ -2,7 +2,7 @@
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Shin::NeuronNetwork::Learning::OpticalBackPropagation::OpticalBackPropagation(FeedForwardNetworkQuick &n): BackPropagation(n)
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{
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setEntropySize(100);
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}
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void Shin::NeuronNetwork::Learning::OpticalBackPropagation::propagate(const Shin::NeuronNetwork::Solution& expectation)
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@@ -11,8 +11,23 @@
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#include "functional"
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/*
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* http://www2.econ.iastate.edu/tesfatsi/RLUsersGuide.ICAC2005.pdf
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* http://www.autonlab.org/tutorials/rl06.pdf
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* http://www.nbu.bg/cogs/events/2000/Readings/Petrov/rltutorial.pdf
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*
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* http://www.applied-mathematics.net/qlearning/qlearning.html
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* http://nn.cs.utexas.edu/downloads/papers/stanley.gecco02_1.pdf
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*
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* http://stackoverflow.com/questions/740389/good-implementations-of-reinforced-learning
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*
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* http://stackoverflow.com/questions/10722064/training-a-neural-network-with-reinforcement-learning
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*
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* http://remi.coulom.free.fr/Thesis/
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* http://remi.coulom.free.fr/Publications/Thesis.pdf
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*
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* http://link.springer.com/article/10.1007/BF00992696
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*
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* http://scholar.google.cz/scholar?start=10&q=reinforcement+learning+feedforward&hl=en&as_sdt=0,5&as_vis=1
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*
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*/
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