// The original code has a bug preventing solution if // weights are different. works for 2a+2b but not for 2a+b //Fixed by bell and Aaron Eberhardt // Warm thanks to SIGSEGV, original code is a very nice and compact implementation in c++ of // the python lesson, s/he deserves all the credit, this is just a bug correction // Created by SIGSEGV /** * Implementing "Teaching math using neural networks" * lesson in C++ * * Formula (a+b)*2 **/ #include #include #include using namespace std; class neural_network{ private: vector weights; vector dot ( const vector> *in1, const vector *in2 ){ vector outputs; for(const auto i : *in1) { double product = 0; vector::const_iterator it = in2->begin(); for(const auto j : i) { product += j * *it++; //cout << j << " " << *it << " = " << product<< endl; } outputs.emplace_back(product); } return outputs; } public: neural_network(size_t weight_count) { weights.resize(weight_count); for(auto& i : weights) i = 1; } void train( const vector>& inputs, const vector& outputs, size_t num ){ while(num--){ vector::const_iterator it = outputs.begin(); auto output = think(&inputs); int c=0,count=0; for(const auto i : output){ double error = *it++ - i; for(auto& j : weights) { c=count/2; ptrdiff_t pos = find(weights.begin(), weights.end(), j) - weights.begin(); j += 0.01*error*inputs[c][pos]; count++; //cout << j << " "<< inputs[c][pos] << " "<< c<<" "< think(const vector > *inputs){ return dot(inputs, &weights); } void solve(const vector>& inputs){ cout << "Input:\n"; for(const auto i : inputs) { for(const auto n : i) cout << n << " "; cout.put('\n'); } cout << "Output:\n"; for(const auto i : think(&inputs)) cout << i << '\n'; cout << "===================\n"; } }; int main(){ neural_network *A = new neural_network(2); A->train({{2,3},{1,1},{5,2},{3,12}} , {7,3,12,18} , 1000); A->solve({{2,3},{1,1},{5,2},{12,3}}); A->solve({{3,3},{450,550},{-70,130},{15,2}}); //custom input delete A; }

neural_bell