cascade

include/NeuralNetwork/Cascade/Network.h

@@ -1,6 +1,7 @@
 #pragma once
 
 #include "../Network.h"
+#include <random>
 
 namespace NeuralNetwork {
 	namespace Cascade {
@@ -10,7 +11,7 @@ namespace NeuralNetwork {
 				 * @brief Constructor for Network
 				 * @param _inputSize is number of inputs to network
 				 */
-				Network(std::size_t inputSize, std::size_t outputSize) : NeuralNetwork::Network(inputSize,outputSize) {
+				Network(std::size_t inputSize, std::size_t outputSize, const ActivationFunction::ActivationFunction &activationFunction=ActivationFunction::Sigmoid(-4.9)) : NeuralNetwork::Network(inputSize,outputSize) {
 					_neurons.push_back(std::make_shared<BiasNeuron>());
 
 					for(std::size_t i = 0; i < inputSize; i++) {
@@ -18,7 +19,7 @@ namespace NeuralNetwork {
 					}
 
 					for(std::size_t i = 0; i < outputSize; i++) {
-						_neurons.push_back(std::make_shared<Neuron>(_neurons.size()));
+						_neurons.push_back(std::make_shared<Neuron>(_neurons.size(),activationFunction));
 						_neurons.back()->setInputSize(inputSize + 1); // +1 is bias
 					}
 				}
@@ -29,8 +30,8 @@ namespace NeuralNetwork {
 
 					compute[0] = 1.0;
 
-					for(std::size_t i = 1; i <= _inputs; i++) {
-						compute[i] = input[i - 1];
+					for(std::size_t i = 0; i < _inputs; i++) {
+						compute[i+1] = input[i];
 					}
 
 					// 0 is bias, 1-_inputSize is input
@@ -45,14 +46,22 @@ namespace NeuralNetwork {
 					return _neurons.size();
 				}
 
+				const std::vector<std::shared_ptr<NeuronInterface>>& getNeurons() {
+					return _neurons;
+				}
+
 				std::shared_ptr<NeuronInterface> getNeuron(std::size_t id) {
 					return _neurons[id];
 				}
 
+				std::vector<std::shared_ptr<NeuronInterface>> getOutputNeurons() {
+					return std::vector<std::shared_ptr<NeuronInterface>>(_neurons.end()-_outputs,_neurons.end());
+				}
+
 				std::shared_ptr<NeuronInterface> addNeuron() {
 					_neurons.push_back(std::make_shared<Neuron>());
 					auto neuron = _neurons.back();
-					neuron->setInputSize(_neurons.size() - _outputs);
+					neuron->setInputSize(_neurons.size() - _outputs-1);
 					// 0 is bias, 1-_inputSize is input
 					std::size_t maxIndexOfNeuron = _neurons.size() - 1;
 					// move output to right position
@@ -93,12 +102,13 @@ namespace NeuralNetwork {
 					return std::unique_ptr<Network>(net);
 				}
 
-
 				//I I H H O O 6
 				void randomizeWeights() {
-					for(std::size_t neuron = _neurons.size() - _outputs; neuron < _neurons.size(); neuron++) {
-						for(std::size_t weight = 0; weight < _neurons.size() - _outputs; weight++) {
-							_neurons[neuron]->weight(weight) = 1.0 - static_cast<float>(rand() % 2001) / 1000.0;
+					std::mt19937 _generator(rand());
+					std::uniform_real_distribution<> _distribution(-0.3,0.3);
+					for(auto& neuron :getOutputNeurons()) {
+						for(std::size_t weight = 0; weight < neuron->getWeights().size(); weight++) {
+							neuron->weight(weight) = _distribution(_generator);
 						}
 					}
 				}