// Simple implementation of // Teaching perceptron math in C++ // add bias and 3rd weight for that bias // by Mustafa K. #include using namespace std; // y = (a + b) * 1.5 float const LR = 0.01; class Perceptron { float weights[2]; public: Perceptron () { weights[0] = 0.5; weights[1] = 0.5; } float guess (float inputs[2]) { return (inputs[0] * weights[0] + inputs[1] * weights[1]); } void adjust(float err, float inputs[2]) { weights[0] += err * inputs[0] * LR; weights[1] += err * inputs[1] * LR; } float getWeight(int i) { return weights[i]; } }; int main() { Perceptron p; int iteration = 10; float inputs[5][2] = {{4, 8}, {3, 7}, {2, 4}, {1, 3}, {7, 1}}; // while y = (a + b) * 1.5 float outputs[5] = {18, 15, 9, 6, 12}; cout<<"weights[0] : "<< p.getWeight(0) << endl; cout<<"weights[1] : "<< p.getWeight(1) << endl; cout<< "guess1 : "<< p.guess(inputs[0]) << endl; // Train perceptron for (int i=0; i< iteration; i++) { for (int j=0; j<5; j++) { float desired = outputs[j]; float guess = p.guess(inputs[j]); float error = desired - guess; if (guess != desired) { p.adjust (error, inputs[j]); } } } cout<<"weights[0] : "<< p.getWeight(0) << endl; cout<<"weights[1] : "<< p.getWeight(1) << endl; cout<< "guess2 : "<< p.guess(inputs[0]) << endl; float i2[] = {14, 2}; cout<< "guess {14, 2}: "<< p.guess(i2) << endl; return 0; }

Static Perceptron