113 lines
3.1 KiB
C++
113 lines
3.1 KiB
C++
#pragma once
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#include "../Network.h"
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#include <vector>
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#include <sstream>
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#include <iomanip>
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#include <limits>
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namespace NeuralNetwork {
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namespace Recurrent {
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/**
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* @author Tomas Cernik (Tom.Cernik@gmail.com)
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* @brief Reccurent model of Artifical neural network
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*/
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class Network: public NeuralNetwork::Network {
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public:
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/**
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* @brief Constructor for Network
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* @param _inputSize is number of inputs to network
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* @param _outputSize is size of output from network
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* @param hiddenUnits is number of hiddenUnits to be created
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*/
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inline Network(size_t _inputSize, size_t _outputSize,size_t hiddenUnits=0):NeuralNetwork::Network(),inputSize(_inputSize),outputSize(_outputSize), neurons(0),outputs(0) {
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neurons.push_back(new NeuralNetwork::BiasNeuron());
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for(size_t i=0;i<_inputSize;i++) {
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neurons.push_back(new NeuralNetwork::InputNeuron(neurons.size()));
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}
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for(size_t i=0;i<_outputSize;i++) {
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addNeuron();
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}
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for(size_t i=0;i<hiddenUnits;i++) {
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addNeuron();
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}
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};
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Network(const Network &r) :inputSize(r.inputSize), outputSize(r.outputSize), neurons(0), outputs(r.outputs) {
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neurons.push_back(new NeuralNetwork::BiasNeuron());
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for(std::size_t i=1;i<r.neurons.size();i++) {
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neurons.push_back(r.neurons[i]->clone());
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}
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}
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Network& operator=(const Network&r);
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/**
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* @brief Virtual destructor for Network
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*/
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virtual ~Network() {
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for(auto& a:neurons) {
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delete a;
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}
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};
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/**
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* @brief This is a function to compute one iterations of network
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* @param input is input of network
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* @returns output of network
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*/
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inline virtual std::vector<float> computeOutput(const std::vector<float>& input) override {
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return computeOutput(input,1);
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}
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/**
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* @brief This is a function to compute iterations of network
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* @param input is input of network
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* @param iterations is number of iterations
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* @returns output of network
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*/
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std::vector<float> computeOutput(const std::vector<float>& input, unsigned int iterations);
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std::vector<NeuronInterface*>& getNeurons () {
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return neurons;
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}
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virtual SimpleJSON::Type::Object serialize() const override;
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NeuronInterface& addNeuron() {
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neurons.push_back(new Neuron(neurons.size()));
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NeuronInterface *newNeuron=neurons.back();
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for(std::size_t i=0;i<neurons.size();i++) {
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neurons[i]->setInputSize(newNeuron->id+1);
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}
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return *newNeuron;
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}
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/**
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* @brief creates new network from joining two
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* @param r is network that is connected to outputs of this network
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* @returns network of constructed from two networks
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*/
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NeuralNetwork::Recurrent::Network connectWith(const NeuralNetwork::Recurrent::Network &r) const;
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static std::unique_ptr<Network> deserialize(const SimpleJSON::Type::Object&);
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typedef SimpleJSON::Factory<Network> Factory;
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protected:
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size_t inputSize=0;
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size_t outputSize=0;
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std::vector<NeuronInterface*> neurons;
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std::vector<float> outputs;
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SIMPLEJSON_REGISTER(NeuralNetwork::Recurrent::Network::Factory,NeuralNetwork::Recurrent::Network, deserialize)
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};
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}
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} |