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removed Cellular encoding

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Shin
2016-10-30 20:45:06 +01:00
parent 006810a49c
commit 02217393d5
13 changed files with 0 additions and 1041 deletions
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3
CMakeLists.txt
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@@ -76,9 +76,6 @@ set (LIBRARY_SOURCES
src/NeuralNetwork/Neuron.cpp src/NeuralNetwork/Neuron.cpp
src/NeuralNetwork/IMPL.cpp src/NeuralNetwork/IMPL.cpp
src/NeuralNetwork/ConstructiveAlgorithms/CellularEncoding/CellularEncoding.cpp
src/NeuralNetwork/ConstructiveAlgorithms/CellularEncoding/Cell.cpp
) )
add_library(NeuralNetwork STATIC ${LIBRARY_SOURCES}) add_library(NeuralNetwork STATIC ${LIBRARY_SOURCES})

182
include/NeuralNetwork/ConstructiveAlgorithms/CelularEncoding/Cell.h
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@@ -1,182 +0,0 @@
#pragma once
#include <EvolutionaryAlgorithms/GeneticPrograming/CodeTree.h>
#include "./Instruction/Instruction.h"
#include <cstddef>
#include <vector>
namespace NeuralNetworks {
namespace ConstructiveAlgorithms {
namespace CelularEncoding {
class CelularEncoding;
class Link {
public:
Link(bool status_, float value_, std::size_t neuron_) : status(status_), value(value_), neuron(neuron_) {
}
bool status=false;
float value=0.0;
std::size_t neuron;
};
class Cell {
friend Instruction::Instruction;
public:
Cell(const Cell&)=delete;
Cell&operator=(const Cell&)=delete;
Cell(Cell &&r): id(r.id), codePtr(r.codePtr), _isOutput(r._isOutput), _isInput(r._isInput), alive(r.alive), life(r.life), bias(r.bias), links(r.links) {
}
Cell(const std::size_t& id_, const EvolutionaryAlgorithm::GeneticPrograming::CodeTree* codePtr_): id(id_), codePtr(codePtr_) {
}
void step(CelularEncoding&);
bool isAlive() const {
return alive;
};
void die() {
alive=false;
};
std::size_t getLife() const {
return life;
}
void setLife(const std::size_t &newLife) {
life=newLife;
}
float getBias() const {
return bias;
}
void setBias(const float &newBias) {
bias=newBias;
}
std::size_t getLinkRegister() const {
return linkRegister;
}
void setLinkRegister(const std::size_t &newLinkRegister) {
linkRegister=newLinkRegister;
}
const std::vector<Link>& getLinks() const {
return links;
}
void setLinks(const std::vector<Link> &links_) {
links=links_;
}
void addLink(const Link& l) {
for(auto &link:links) {
if(link.neuron ==l.neuron) {
link.value=l.value;
link.status=l.status;
return;
}
}
links.push_back(l);
}
bool getLinkStatus(const std::size_t index) const {
if(index < links.size()) {
return links[index].status;
}
return false;
}
void setLinkStatus(const std::size_t index, const bool &status) {
if(index < links.size()) {
links[index].status=status;
}
}
float getLinkValue(const std::size_t index) const {
if(index < links.size()) {
return links[index].value;
}
return 0.0;
}
void setLinkValue(const std::size_t index, const float &value) {
if(index < links.size()) {
links[index].value = value;
}
}
void setCodePointer(const EvolutionaryAlgorithm::GeneticPrograming::CodeTree *c) {
codePtr=c;
}
const EvolutionaryAlgorithm::GeneticPrograming::CodeTree* getCodePointer() const {
return codePtr;
}
std::size_t getID() const {
return id;
}
bool isOutput() const {
return _isOutput;
}
void setOutput() {
_isOutput=true;
}
void disableOutput() {
_isOutput=false;
}
bool isInput() const {
return _isInput;
}
void setInput() {
_isInput=true;
}
void disableInput() {
_isInput=false;
}
protected:
std::size_t id;
const EvolutionaryAlgorithm::GeneticPrograming::CodeTree *codePtr;
//const Code &code;
bool _isOutput=false;
bool _isInput=false;
bool alive=true;
std::size_t life=1;
float bias = 0.0;
std::size_t linkRegister=0;
std::vector<Link> links = std::vector<Link>();
/*
std::size_t codePosition;
std::size_t linkRegister;
std::size_t sigme;
std::size_t dyn;
std::size_t link;
std::size_t simplif;
*/
// 150 of paper
private:
};
}
}
}

182
include/NeuralNetwork/ConstructiveAlgorithms/CelularEncoding/CelularEncoding.h
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@@ -1,182 +0,0 @@
#pragma once
#include "Exception.h"
#include "Cell.h"
#include <NeuralNetwork/Recurrent/Network.h>
#include <vector>
#include <algorithm>
namespace NeuralNetworks {
namespace ConstructiveAlgorithms {
namespace CelularEncoding {
class CelularEncoding {
public:
CelularEncoding(const CelularEncoding &) = delete;
CelularEncoding &operator=(const CelularEncoding &) = delete;
CelularEncoding() {
}
void setActivationFunction(const std::shared_ptr<NeuralNetwork::ActivationFunction::ActivationFunction> &fun) {
_activationFunction=fun;
}
void setMaxSteps(std::size_t steps) {
_maxSteps=steps;
}
NeuralNetwork::Recurrent::Network run() {
std::size_t cellsStep = 0;
std::size_t steps=0;
do {
cellsStep = step();
steps++;
}
while(cellsStep > 0 && steps < _maxSteps);
if(steps >= _maxSteps) {
throw Exception("Went over max steps");
}
if(cells.size() > _maxCells) {
throw Exception("Went over max cells");
}
std::size_t outputs = 0;
std::size_t inputs = 0;
std::vector<std::size_t> cells2Neurons;
cells2Neurons.resize(cells.size());
std::size_t indexOfNeuronTmp=1;
for(std::size_t i = 0; i < cells.size(); i++) {
if(cells[i]->isInput()) {
cells2Neurons[i] = indexOfNeuronTmp++;
inputs++;
}
}
for(std::size_t i = 0; i < cells.size(); i++) {
if(cells[i]->isOutput()) {
if(!cells[i]->isInput()) {
cells2Neurons[i] = indexOfNeuronTmp++;
}
outputs++;
}
}
for(std::size_t i = 0; i < cells.size(); i++) {
if(!cells[i]->isOutput() && !cells[i]->isInput()) {
cells2Neurons[i] = indexOfNeuronTmp++;
}
}
std::size_t hiddenNeurons = static_cast<int>(cells.size()) - static_cast<int>(inputs) - static_cast<int>(outputs) < 0 ? 0 : cells.size() - inputs - outputs;
NeuralNetwork::Recurrent::Network netw(inputs, outputs, hiddenNeurons);
for(std::size_t i = 0; i < cells.size(); i++) {
const auto &cell = cells[i];
std::size_t indexOfNeuron = cells2Neurons[i];
auto& neuron = netw[indexOfNeuron];
if(cells2Neurons[i] > inputs) {
neuron.setActivationFunction(*_activationFunction);
}
neuron.weight(0)=cell->getBias();
for(auto &link: cells[i]->getLinks()) {
if(link.status == true) {
neuron.weight(cells2Neurons[link.neuron]) = link.value;
} else {
neuron.weight(cells2Neurons[link.neuron]) = 0.0;
}
}
}
return netw;
}
Cell &addCell(const EvolutionaryAlgorithm::GeneticPrograming::CodeTree *c) {
cells.push_back(std::make_shared<Cell>(cells.size(), c));
return (*cells.back());
}
void setAcyclicTopology() {
cells.clear();
/*
for(std::size_t i = 0; i < inputSize; i++) {
addCell(code).die();
}
*/
Cell &cell = addCell(code);
cell.setLife(_initialLife);
_processingOrder.push_back(cell.getID());
cell.setOutput();
cell.setInput();
/*
for(std::size_t i = 0; i < inputSize; i++) {
Link l(true, 1.0, i);
cell.addLink(l);
}
*/
}
void setCyclicTopology() {
setAcyclicTopology();
// Acyclic + reccurent link
Link l(true, 1.0, cells.back()->getID());
cells.back()->addLink(l);
}
void setCode(const EvolutionaryAlgorithm::GeneticPrograming::CodeTree *code_) {
code = code_;
}
const EvolutionaryAlgorithm::GeneticPrograming::CodeTree *getCodeStart() const {
return code;
}
std::vector<std::shared_ptr<Cell>> &getCells() {
return cells;
}
void addCellToProcessingOrder(std::size_t id) {
auto position = std::find(_processingOrder.begin(),_processingOrder.end(),currentID);
if(position == _processingOrder.end()) {
_processingOrder.push_back(id);
} else {
_processingOrder.insert(position+1,id);
}
}
void setInitiaLife(std::size_t life) {
_initialLife=life;
}
protected:
std::size_t step();
private:
std::size_t _maxCells= 15;
std::size_t _maxSteps = std::numeric_limits<std::size_t>::max();
std::size_t _initialLife = 2.0;
std::shared_ptr<NeuralNetwork::ActivationFunction::ActivationFunction> _activationFunction = std::make_shared<NeuralNetwork::ActivationFunction::Sigmoid>();
std::vector<std::size_t> _processingOrder = {};
std::size_t currentID = 0;
const EvolutionaryAlgorithm::GeneticPrograming::CodeTree *code = nullptr;
std::vector<std::shared_ptr<Cell>> cells = {};
};
}
}
}

23
include/NeuralNetwork/ConstructiveAlgorithms/CelularEncoding/Exception.h
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@@ -1,23 +0,0 @@
#pragma once
#include <exception>
#include <string>
namespace NeuralNetworks {
namespace ConstructiveAlgorithms {
namespace CelularEncoding {
class Exception : public std::exception {
public:
Exception (const std::string &e) : _what(e) {
}
virtual const char* what() const noexcept override {
return _what.c_str();
}
protected:
std::string _what;
};
}
}
}

143
include/NeuralNetwork/ConstructiveAlgorithms/CelularEncoding/Instruction/Bias.h
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@@ -1,143 +0,0 @@
#pragma once
#include "./Instruction.h"
#include "../Cell.h"
#include "../CelularEncoding.h"
namespace NeuralNetworks {
namespace ConstructiveAlgorithms {
namespace CelularEncoding {
namespace Instruction{
class IncBias : public Instruction{
public:
virtual void run(Cell &cell, CelularEncoding &, const std::vector<double> &) override {
cell.setBias(cell.getBias()+1.0);
cell.setCodePointer(cell.getCodePointer()->c1);
}
virtual std::size_t numberOfNodes() const override {
return 1;
}
virtual std::string toString() const override {
return "IncBias";
}
};
class DecBias : public Instruction{
public:
virtual void run(Cell &cell, CelularEncoding &, const std::vector<double> &) override {
cell.setBias(cell.getBias()-1.0);
cell.setCodePointer(cell.getCodePointer()->c1);
}
virtual std::size_t numberOfNodes() const override {
return 1;
}
virtual std::string toString() const override {
return "DecBias";
}
};
class MulBias : public Instruction{
public:
virtual void run(Cell &cell, CelularEncoding &, const std::vector<double> &) override {
cell.setBias(cell.getBias()*2.0);
cell.setCodePointer(cell.getCodePointer()->c1);
}
virtual std::size_t numberOfNodes() const override {
return 1;
}
virtual std::string toString() const override {
return "MulBias";
}
};
class DivBias : public Instruction{
public:
virtual void run(Cell &cell, CelularEncoding &, const std::vector<double> &) override {
cell.setBias(cell.getBias()*0.5);
cell.setCodePointer(cell.getCodePointer()->c1);
}
virtual std::size_t numberOfNodes() const override {
return 1;
}
virtual std::string toString() const override {
return "DivBias";
}
};
class SetBiasZero : public Instruction{
public:
SetBiasZero(bool terminal = true) : _nodes(terminal ? 0 : 1) {
}
virtual void run(Cell &cell, CelularEncoding &, const std::vector<double> &) override {
cell.setBias(0.0);
cell.setCodePointer(cell.getCodePointer()->c1);
}
virtual std::size_t numberOfNodes() const override {
return _nodes;
}
virtual std::string toString() const override {
return "SetBiasZero";
}
protected:
std::size_t _nodes;
};
class SetBiasOne : public Instruction{
public:
SetBiasOne(bool terminal = true) : _nodes(terminal ? 0 : 1) {
}
virtual void run(Cell &cell, CelularEncoding &, const std::vector<double> &) override {
cell.setBias(1.0);
cell.setCodePointer(cell.getCodePointer()->c1);
}
virtual std::size_t numberOfNodes() const override {
return _nodes;
}
virtual std::string toString() const override {
return "SetBiasZero";
}
protected:
std::size_t _nodes;
};
class SetBiasMinusOne : public Instruction{
public:
SetBiasMinusOne(bool terminal = true) : _nodes(terminal ? 0 : 1) {
}
virtual void run(Cell &cell, CelularEncoding &, const std::vector<double> &) override {
cell.setBias(-1.0);
cell.setCodePointer(cell.getCodePointer()->c1);
}
virtual std::size_t numberOfNodes() const override {
return _nodes;
}
virtual std::string toString() const override {
return "SetBiasZero";
}
protected:
std::size_t _nodes;
};
}
}
}
}

101
include/NeuralNetwork/ConstructiveAlgorithms/CelularEncoding/Instruction/Division.h
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@@ -1,101 +0,0 @@
#pragma once
#include "./Instruction.h"
#include "../Cell.h"
#include "../CelularEncoding.h"
namespace NeuralNetworks {
namespace ConstructiveAlgorithms {
namespace CelularEncoding {
namespace Instruction{
class Par : public Instruction{
public:
virtual void run(Cell &cell, CelularEncoding &encoding, const std::vector<double> &) override {
Cell &newCell = encoding.addCell(cell.getCodePointer()->c2);
if(cell.isOutput()) {
newCell.setOutput();
}
if(cell.isInput()) {
newCell.setInput();
}
newCell.setLife(cell.getLife());
newCell.setBias(cell.getBias());
newCell.setLinks(cell.getLinks());
for(auto &cellI:encoding.getCells()) {
for( const Link &link: cellI->getLinks()) {
if(link.neuron == cell.getID()) {
Link newLink (link);
newLink.neuron=newCell.getID();
cellI->addLink(newLink);
break;
}
}
}
encoding.addCellToProcessingOrder(newCell.getID());
cell.setCodePointer(cell.getCodePointer()->c1);
}
virtual std::size_t numberOfNodes() const override {
return 2;
}
virtual std::string toString() const override {
return "Par";
}
protected:
private:
};
class Seq : public Instruction{
public:
virtual void run(Cell &cell, CelularEncoding &encoding, const std::vector<double> &) override {
Cell &newCell = encoding.addCell(cell.getCodePointer()->c2);
Link l(true, 1.0, cell.getID());
if(cell.isOutput()) {
newCell.setOutput();
cell.disableOutput();
}
newCell.addLink(l);
for(auto& cellIter:encoding.getCells()) {
if(cellIter->getID() != newCell.getID()) {
auto links = cellIter->getLinks();
for(auto& link: links) {
if(link.neuron==cell.getID()) {
link.neuron=newCell.getID();
}
}
cellIter->setLinks(links);
}
}
newCell.setLife(cell.getLife());
encoding.addCellToProcessingOrder(newCell.getID());
cell.setCodePointer(cell.getCodePointer()->c1);
//TODO: copy registers
}
virtual std::size_t numberOfNodes() const override {
return 2;
}
virtual std::string toString() const override {
return "Seq";
}
protected:
private:
};
}
}
}
}

58
include/NeuralNetwork/ConstructiveAlgorithms/CelularEncoding/Instruction/Finish.h
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@@ -1,58 +0,0 @@
#pragma once
#include "./Instruction.h"
#include "../Cell.h"
#include "../CelularEncoding.h"
namespace NeuralNetworks {
namespace ConstructiveAlgorithms {
namespace CelularEncoding {
namespace Instruction{
class End : public Instruction{
public:
virtual void run(Cell &cell, CelularEncoding &, const std::vector<double> &) override {
cell.die();
}
virtual std::size_t numberOfNodes() const override {
return 0;
}
virtual std::string toString() const override {
return "End";
}
protected:
private:
};
class Rec : public Instruction{
public:
Rec(bool terminal = true) : _nodes(terminal ? 0 : 1) {
}
virtual void run(Cell &cell, CelularEncoding &encoding, const std::vector<double> &) override {
if(cell.getLife()<=1) {
cell.setCodePointer(cell.getCodePointer()->c1);
cell.die();
}else {
cell.setLife(cell.getLife()-1);
cell.setCodePointer(encoding.getCodeStart());
}
}
virtual std::size_t numberOfNodes() const override {
return _nodes;
}
virtual std::string toString() const override {
return "Rec";
}
protected:
std::size_t _nodes;
private:
};
}
}
}
}

32
include/NeuralNetwork/ConstructiveAlgorithms/CelularEncoding/Instruction/Instruction.h
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@@ -1,32 +0,0 @@
#pragma once
#include <EvolutionaryAlgorithms/GeneticPrograming/Instruction.h>
#include <vector>
namespace NeuralNetworks {
namespace ConstructiveAlgorithms {
namespace CelularEncoding {
class Cell;
class CelularEncoding;
namespace Instruction {
class Instruction : public EvolutionaryAlgorithm::GeneticPrograming::Instruction {
public:
virtual ~Instruction() {
}
// todo what implement??
void operator()() {}
virtual void run(Cell &, CelularEncoding &, const std::vector<double> &) = 0;
protected:
private:
};
}
}
}
}

245
include/NeuralNetwork/ConstructiveAlgorithms/CelularEncoding/Instruction/Link.h
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@@ -1,245 +0,0 @@
#pragma once
#include "./Instruction.h"
#include "../Cell.h"
#include "../CelularEncoding.h"
namespace NeuralNetworks {
namespace ConstructiveAlgorithms {
namespace CelularEncoding {
namespace Instruction {
class Inclr : public Instruction {
public:
virtual void run(Cell &cell, CelularEncoding &, const std::vector<double> &) override {
if(cell.getLinkRegister() == cell.getLinks().size() - 1) {
cell.setLinkRegister(0);
} else {
cell.setLinkRegister(cell.getLinkRegister() + 1);
}
cell.setCodePointer(cell.getCodePointer()->c1);
}
virtual std::size_t numberOfNodes() const override {
return 1;
}
virtual std::string toString() const override {
return "IncLr";
}
protected:
private:
};
class Declr : public Instruction {
public:
virtual void run(Cell &cell, CelularEncoding &, const std::vector<double> &) override {
if(cell.getLinkRegister() == 0) {
if(cell.getLinks().size() > 1) {
cell.setLinkRegister(cell.getLinks().size() - 1);
}
} else {
cell.setLinkRegister(cell.getLinkRegister() - 1);
}
cell.setCodePointer(cell.getCodePointer()->c1);
}
virtual std::size_t numberOfNodes() const override {
return 1;
}
virtual std::string toString() const override {
return "Declr";
}
protected:
private:
};
class ValPlus : public Instruction {
public:
virtual void run(Cell &cell, CelularEncoding &, const std::vector<double> &) override {
cell.setLinkValue(cell.getLinkRegister(), 1.0);
cell.setCodePointer(cell.getCodePointer()->c1);
}
virtual std::size_t numberOfNodes() const override {
return 1;
}
virtual std::string toString() const override {
return "Val+";
}
protected:
private:
};
class ValMinus : public Instruction {
public:
virtual void run(Cell &cell, CelularEncoding &, const std::vector<double> &) override {
cell.setLinkValue(cell.getLinkRegister(), -1.0);
cell.setCodePointer(cell.getCodePointer()->c1);
}
virtual std::size_t numberOfNodes() const override {
return 1;
}
virtual std::string toString() const override {
return "Val-";
}
protected:
private:
};
class Inc : public Instruction {
public:
virtual void run(Cell &cell, CelularEncoding &, const std::vector<double> &) override {
cell.setLinkValue(cell.getLinkRegister(), cell.getLinkValue(cell.getLinkRegister()) + 1.0);
cell.setCodePointer(cell.getCodePointer()->c1);
}
virtual std::size_t numberOfNodes() const override {
return 1;
}
virtual std::string toString() const override {
return "Inc";
}
protected:
private:
};
class Dec : public Instruction {
public:
virtual void run(Cell &cell, CelularEncoding &, const std::vector<double> &) override {
cell.setLinkValue(cell.getLinkRegister(), cell.getLinkValue(cell.getLinkRegister()) - 1.0);
cell.setCodePointer(cell.getCodePointer()->c1);
}
virtual std::size_t numberOfNodes() const override {
return 1;
}
virtual std::string toString() const override {
return "Dec";
}
protected:
private:
};
class Mult : public Instruction {
public:
virtual void run(Cell &cell, CelularEncoding &, const std::vector<double> &) override {
cell.setLinkValue(cell.getLinkRegister(), cell.getLinkValue(cell.getLinkRegister()) * 2.0);
cell.setCodePointer(cell.getCodePointer()->c1);
}
virtual std::size_t numberOfNodes() const override {
return 1;
}
virtual std::string toString() const override {
return "Mult";
}
protected:
private:
};
class Div : public Instruction {
public:
virtual void run(Cell &cell, CelularEncoding &, const std::vector<double> &) override {
cell.setLinkValue(cell.getLinkRegister(), cell.getLinkValue(cell.getLinkRegister()) * 0.5);
cell.setCodePointer(cell.getCodePointer()->c1);
}
virtual std::size_t numberOfNodes() const override {
return 1;
}
virtual std::string toString() const override {
return "Div";
}
protected:
private:
};
class On : public Instruction {
public:
virtual void run(Cell &cell, CelularEncoding &, const std::vector<double> &) override {
cell.setLinkStatus(cell.getLinkRegister(), true);
cell.setCodePointer(cell.getCodePointer()->c1);
}
virtual std::size_t numberOfNodes() const override {
return 1;
}
virtual std::string toString() const override {
return "On";
}
protected:
private:
};
class Cyc : public Instruction {
public:
virtual void run(Cell &cell, CelularEncoding &, const std::vector<double> &) override {
bool exists = false;
for(std::size_t i = 0; i < cell.getLinks().size(); i++) {
auto &link = cell.getLinks()[i];
if(link.neuron == cell.getID()) {
exists = true;
cell.setLinkStatus(i,true);
}
}
if(!exists) {
cell.addLink(Link(true, 1.0, cell.getID()));
}
cell.setCodePointer(cell.getCodePointer()->c1);
}
virtual std::size_t numberOfNodes() const override {
return 1;
}
virtual std::string toString() const override {
return "Cyc";
}
protected:
private:
};
class Off : public Instruction {
public:
virtual void run(Cell &cell, CelularEncoding &, const std::vector<double> &) override {
cell.setLinkStatus(cell.getLinkRegister(), false);
cell.setCodePointer(cell.getCodePointer()->c1);
}
virtual std::size_t numberOfNodes() const override {
return 1;
}
virtual std::string toString() const override {
return "Off";
}
protected:
private:
};
}
}
}
}

34
include/NeuralNetwork/ConstructiveAlgorithms/CelularEncoding/Instruction/Other.h
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@@ -1,34 +0,0 @@
#pragma once
#include "./Instruction.h"
#include "../Cell.h"
#include "../CelularEncoding.h"
namespace NeuralNetworks {
namespace ConstructiveAlgorithms {
namespace CelularEncoding {
namespace Instruction{
class Wait : public Instruction{
public:
Wait(bool terminal = false) : _numberOfNodes(terminal? 0 : 1) {
}
virtual void run(Cell &cell, CelularEncoding &, const std::vector<double> &) override {
cell.setCodePointer(cell.getCodePointer()->c1);
}
virtual std::size_t numberOfNodes() const override {
return _numberOfNodes;
}
virtual std::string toString() const override {
return "Wait";
}
protected:
std::size_t _numberOfNodes;
};
}
}
}
}

12
src/NeuralNetwork/ConstructiveAlgorithms/CellularEncoding/Cell.cpp
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@@ -1,12 +0,0 @@
#include <NeuralNetwork/ConstructiveAlgorithms/CelularEncoding/Cell.h>
#include <NeuralNetwork/ConstructiveAlgorithms/CelularEncoding/CelularEncoding.h>
void NeuralNetworks::ConstructiveAlgorithms::CelularEncoding::Cell::step(CelularEncoding &c) {
if(isAlive()) {
reinterpret_cast<Instruction::Instruction *>(codePtr->instruction.lock().get())->run(*this, c, codePtr->parameters);
}
if(codePtr == nullptr) {
die();
}
}

20
src/NeuralNetwork/ConstructiveAlgorithms/CellularEncoding/CellularEncoding.cpp
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@@ -1,20 +0,0 @@
#include <NeuralNetwork/ConstructiveAlgorithms/CelularEncoding/CelularEncoding.h>
std::size_t NeuralNetworks::ConstructiveAlgorithms::CelularEncoding::CelularEncoding::step() {
std::size_t activeCellCount = 0;
std::vector<std::size_t> processingOrder(_processingOrder);
for(std::size_t i = 0; i < processingOrder.size(); i++) {
std::size_t cell = processingOrder[i];
if(cells[cell]->isAlive()) {
currentID = cells[cell]->getID();
cells[cell]->step(*this);
activeCellCount++;
} else {
auto iter = std::find(_processingOrder.begin(), _processingOrder.end(), cell);
if(iter != _processingOrder.end()) {
_processingOrder.erase(iter);
}
}
}
return activeCellCount;
}

6
tests/CMakeLists.txt
View File

@@ -28,12 +28,6 @@ target_link_libraries(recurrent NeuralNetwork gtest gtest_main)
add_executable(quickpropagation quickpropagation.cpp) add_executable(quickpropagation quickpropagation.cpp)
target_link_libraries(quickpropagation NeuralNetwork gtest gtest_main) target_link_libraries(quickpropagation NeuralNetwork gtest gtest_main)
add_executable(celular_code celular_code.cpp)
target_link_libraries(celular_code NeuralNetwork gtest gtest_main)
add_executable(genetic_programing genetic_programing.cpp)
target_link_libraries(genetic_programing NeuralNetwork gtest gtest_main)
# PERF # PERF
add_executable(backpropagation_function_cmp backpropagation_function_cmp.cpp) add_executable(backpropagation_function_cmp backpropagation_function_cmp.cpp)