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/**************************************************************************************************
* LibNeural++ v.0.2 - All-purpose library for managing neural networks *
* Copyright (C) 2009, BlackLight *
* *
* This program is free software: you can redistribute it and/or modify it under the terms of the *
* GNU General Public License as published by the Free Software Foundation, either version 3 of *
* the License, or (at your option) any later version. This program is distributed in the hope *
* that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of *
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for *
* more details. You should have received a copy of the GNU General Public License along with *
* this program. If not, see <http://www.gnu.org/licenses/>. *
**************************************************************************************************/
#include "neural++.h"
using namespace neuralpp;
/**
* @brief Constructor
* @param sz Size of the layer
* @param a Activation function
* @param d Its derivate
*/
Layer::Layer (size_t sz, float(*a)(float), float(*d)(float)) {
for (size_t i=0; i<sz; i++) {
Neuron n(a,d);
elements.push_back(n);
}
actv_f=a;
deriv=d;
}
/**
* @brief Alternative constructor. It directly gets a vector of neurons to build
* the layer
*/
Layer::Layer (vector< Neuron > &el, float (*a)(float), float(*d)(float)) {
elements=el;
actv_f=a;
deriv=d;
}
/**
* @return Number of neurons in the layer
*/
size_t Layer::size() { return elements.size(); }
/**
* @brief Redefinition for operator []. It gets the neuron at <i>i</i>
*/
Neuron& Layer::operator[] (size_t i) { return elements[i]; }
/**
* @brief It links a layer to another
* @param l Layer to connect to the current as input layer
*/
void Layer::link (Layer& l) {
srand ((unsigned) time(NULL));
for (size_t i=0; i<l.size(); i++) {
Neuron *n1 = &(l.elements[i]);
for (size_t j=0; j<size(); j++) {
Neuron *n2 = &(elements[j]);
Synapsis s(n1,n2,RAND,actv_f,deriv);
n1->push_out(s);
n2->push_in(s);
}
}
}
/**
* @brief It sets a vector of propagation values to all its neurons
* @param v Vector of values to write as propagation values
*/
void Layer::setProp (vector<float> &v) {
for (size_t i=0; i<size(); i++)
elements[i].setProp(v[i]);
}
/**
* @brief It sets a vector of activation values to all its neurons
* @param v Vector of values to write as activation values
*/
void Layer::setActv (vector<float> &v) {
for (size_t i=0; i<size(); i++)
elements[i].setActv(v[i]);
}
/**
* @brief It propagates its activation values to the output layers
*/
void Layer::propagate() {
for (size_t i=0; i<size(); i++) {
Neuron *n = &(elements[i]);
n->setProp(n->propagate());
n->setActv( actv_f(n->getProp()) );
}
}

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/**************************************************************************************************
* LibNeural++ v.0.2 - All-purpose library for managing neural networks *
* Copyright (C) 2009, BlackLight *
* *
* This program is free software: you can redistribute it and/or modify it under the terms of the *
* GNU General Public License as published by the Free Software Foundation, either version 3 of *
* the License, or (at your option) any later version. This program is distributed in the hope *
* that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of *
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for *
* more details. You should have received a copy of the GNU General Public License along with *
* this program. If not, see <http://www.gnu.org/licenses/>. *
**************************************************************************************************/
#ifndef __NEURALPP
#define __NEURALPP
#include <vector>
#include <cmath>
#include <ctime>
using namespace std;
namespace neuralpp {
//! Default rand value: |sin(rand)|, always >= 0 and <= 1
#define RAND ( (float) abs( sinf((float) rand()) ) )
class Synapsis;
class Neuron;
class Layer;
class NeuralNet;
class NetworkFileNotFoundException;
class InvalidXMLException;
/**
* @class NetworkFileNotFoundException
* @brief Exception thrown when doing an attempt to load a network from an invalid file
*/
class NetworkFileNotFoundException : public exception {
public:
NetworkFileNotFoundException() {}
const char* what() const throw() { return strdup("Attempt to load a neural network from an invalid network file\n"); }
};
/**
* @class InvalidXMLException
* @brief Exception thrown when trying parsing an invalid XML
*/
class InvalidXMLException : public exception {
public:
InvalidXMLException() {}
const char* what() const throw() { return strdup("Attempt to load an invalid XML file\n"); }
};
/**
* @class NeuralNet
* @brief Main project's class. Use *ONLY* this class, unless you know what you're doing
*/
class NeuralNet {
int epochs;
float l_rate;
float ex;
Layer* input;
Layer* hidden;
Layer* output;
void updateWeights();
void commitChanges (Layer *l);
float error(float);
float (*actv_f)(float);
float (*deriv)(float);
public:
/**
* @brief Enum to choose the eventual training source for our network (XML from a file or from a string)
*/
typedef enum { file, str } source;
NeuralNet (size_t, size_t, size_t, float, int);
NeuralNet (size_t, size_t, size_t, float(*)(float), float(*)(float), float, int);
NeuralNet (const char*) throw();
float getOutput();
float expected();
vector<float> getVectorOutput();
void setExpected(float);
void update();
void propagate();
void setInput (vector<float>&);
void link();
bool save (const char*);
void train(string, source) throw();
static vector<float> split (char, string);
static void initXML (string&);
static string XMLFromSet (int, string);
static void closeXML(string&);
};
/**
* @class Synapsis
* @brief Class for managing synapsis. Don't use this class directly unless you know what
* you're doing, use NeuralNet instead
*/
class Synapsis {
float delta;
float weight;
Neuron *in;
Neuron *out;
float (*actv_f)(float);
float (*deriv)(float);
public:
Synapsis (Neuron* i, Neuron* o, float(*)(float), float(*)(float));
Synapsis (Neuron* i, Neuron* o, float w, float(*)(float), float(*)(float));
Neuron* getIn();
Neuron* getOut();
void setWeight(float);
void setDelta(float);
float getWeight();
float getDelta();
};
/**
* @class Neuron
* @brief Class for managing neurons. Don't use this class directly unless you know what
* you're doing, use NeuralNet instead
*/
class Neuron {
float actv_val;
float prop_val;
vector< Synapsis > in;
vector< Synapsis > out;
float (*actv_f)(float);
float (*deriv)(float);
public:
Neuron (float (*)(float), float(*)(float));
Neuron (vector< Synapsis >, vector< Synapsis >, float (*)(float), float(*)(float));
Synapsis& synIn (size_t i);
Synapsis& synOut (size_t i);
void push_in (Synapsis&);
void push_out (Synapsis&);
void setActv (float);
void setProp (float);
float getActv();
float getProp();
float propagate();
size_t nIn();
size_t nOut();
};
/**
* @class Layer
* @brief Class for managing layers of neurons. Don't use this class directly unless you know what
* you're doing, use NeuralNet instead
*/
class Layer {
vector< Neuron > elements;
void (*update_weights)();
float (*actv_f)(float);
float (*deriv)(float);
public:
Layer (size_t sz, float (*)(float), float(*)(float));
Layer (vector< Neuron >&, float(*)(float), float(*)(float));
Neuron& operator[] (size_t);
void link (Layer&);
void setProp (vector<float>&);
void setActv (vector<float>&);
void propagate();
size_t size();
};
}
#endif

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/**************************************************************************************************
* LibNeural++ v.0.2 - All-purpose library for managing neural networks *
* Copyright (C) 2009, BlackLight *
* *
* This program is free software: you can redistribute it and/or modify it under the terms of the *
* GNU General Public License as published by the Free Software Foundation, either version 3 of *
* the License, or (at your option) any later version. This program is distributed in the hope *
* that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of *
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for *
* more details. You should have received a copy of the GNU General Public License along with *
* this program. If not, see <http://www.gnu.org/licenses/>. *
**************************************************************************************************/
#include "neural++.h"
#include "Markup.h"
#include <iostream>
using namespace neuralpp;
/**
* @brief Built-in function. The default activation function: f(x)=x
*/
float __actv(float prop) { return prop; }
/**
* @brief Default derivate for default activation function: f'(x)=1
*/
float __deriv(float prop) { return 1; }
/**
* @brief Constructor
* @param in_size Size of the input layer
* @param hidden_size Size of the hidden layer
* @param out_size Size of the output layer
* @param l learn rate (get it after doing some experiments, but generally try to
* keep its value quite low to be more accurate)
* @param e Epochs (cycles) to execute (the most you execute, the most the network
* can be accurate for its purpose)
*/
NeuralNet::NeuralNet (size_t in_size, size_t hidden_size, size_t out_size, float l, int e) {
epochs=e;
ref_epochs=epochs;
l_rate=l;
actv_f=__actv;
deriv=__deriv;
input = new Layer(in_size, __actv, __deriv);
hidden = new Layer(hidden_size, __actv, __deriv);
output = new Layer(out_size, __actv, __deriv);
link();
}
/**
* @brief Constructor
* @param in_size Size of the input layer
* @param hidden_size Size of the hidden layer
* @param out_size Size of the output layer
* @param actv Activation function to use (default: f(x)=x)
* @param deriv Derivate for the activation function to use (default: f'(x)=1)
* @param l learn rate (get it after doing some experiments, but generally try to
* keep its value quite low to be more accurate)
* @param e Epochs (cycles) to execute (the most you execute, the most the network
* can be accurate for its purpose)
*/
NeuralNet::NeuralNet (size_t in_size, size_t hidden_size, size_t out_size,
float(*a)(float), float(*d)(float), float l, int e) {
epochs=e;
ref_epochs=epochs;
l_rate=l;
actv_f=a;
deriv=d;
input = new Layer(in_size,a,d);
hidden = new Layer(hidden_size,a,d);
output = new Layer(out_size,a,d);
link();
}
/**
* @brief It gets the output of the network (note: the layer output should contain
* an only neuron)
*/
float NeuralNet::getOutput() { return (*output)[0].getActv(); }
/**
* @brief It gets the output of the network in case the output layer contains more neurons
*/
vector<float> NeuralNet::getVectorOutput() {
vector<float> v;
for (size_t i=0; i<output->size(); i++)
v.push_back( (*output)[i].getActv() );
return v;
}
/**
* @brief It get the error made on the expected result as |v-v'|/v
* @param Expected value
* @return Mean error
*/
float NeuralNet::error(float expected) {
return abs( (getOutput() - expected*
deriv(getOutput())) / (abs(expected)) );
}
/**
* @brief It propagates values through the network. Use this when you want to give
* an already trained network some new values the get to the output
*/
void NeuralNet::propagate() {
hidden->propagate();
output->propagate();
}
/**
* @brief It sets the input for the network
* @param v Vector of floats, containing the values to give to your network
*/
void NeuralNet::setInput(vector<float>& v) {
input->setProp(v);
input->setActv(v);
}
/**
* @brief It links the layers of the network (input, hidden, output). Don't use unless
* you exactly know what you're doing, it is already called by the constructor
*/
void NeuralNet::link() {
hidden->link(*input);
output->link(*hidden);
}
/**
* @brief It sets the value you expect from your network
*/
void NeuralNet::setExpected(float e) { ex=e; }
/**
* @brief It gets the value expected. Of course you should specify this when you
* build your network by using setExpected.
*/
float NeuralNet::expected() { return ex; }
/**
* @brief It updates the weights of the net's synapsis through back-propagation.
* In-class use only
*/
void NeuralNet::updateWeights() {
float out_delta;
for (size_t i=0; i<output->size(); i++) {
Neuron *n = &(*output)[i];
for (size_t j=0; j<n->nIn(); j++) {
Synapsis *s = &(n->synIn(j));
out_delta = s->getIn()->getActv() * error(ex) * l_rate;
s->setDelta(out_delta);
}
}
for (size_t i=0; i<hidden->size(); i++) {
Neuron *n = &(*hidden)[i];
float d = deriv(n->getProp()) * n->synOut(0).getWeight() * out_delta;
for (size_t j=0; j<n->nIn(); j++) {
Synapsis *s = &(n->synIn(j));
s->setDelta(l_rate * d * s->getIn()->getActv());
}
}
}
/**
* @brief It commits the changes made by updateWeights() to the layer l.
* In-class use only
* @param l Layer to commit the changes
*/
void NeuralNet::commitChanges (Layer *l) {
for (size_t i=0; i<l->size(); i++) {
Neuron *n = &(*l)[i];
for (size_t j=0; j<n->nIn(); j++) {
Synapsis *s = &(n->synIn(j));
s->setWeight(s->getWeight() + s->getDelta());
s->setDelta(0);
}
}
}
/**
* @brief It updates through back-propagation the weights of the synapsis and
* computes again the output value for <i>epochs</i> times, calling back
* updateWeights and commitChanges functions
*/
void NeuralNet::update() {
while ((epochs--)>0) {
updateWeights();
commitChanges(output);
commitChanges(hidden);
propagate();
}
}
/**
* @brief Save an already trained neural network to a binary file
* @param fname Name of the file to write
* @return true in case of success, false otherwise
*/
bool NeuralNet::save(const char *fname) {
FILE *fp;
struct netrecord record;
if (!(fp=fopen(fname,"wb")))
return false;
record.input_size = input->size();
record.hidden_size = hidden->size();
record.output_size = output->size();
record.epochs = ref_epochs;
record.l_rate = l_rate;
record.ex = ex;
if (fwrite (&record, sizeof(struct netrecord), 1, fp)<=0)
return false;
// Saving neurons' state
for (unsigned int i=0; i < input->size(); i++) {
struct neuronrecord r;
r.prop = (*input)[i].getProp();
r.actv = (*input)[i].getActv();
fwrite (&r, sizeof(struct neuronrecord), 1, fp);
}
for (unsigned int i=0; i < hidden->size(); i++) {
struct neuronrecord r;
r.prop = (*hidden)[i].getProp();
r.actv = (*hidden)[i].getActv();
fwrite (&r, sizeof(struct neuronrecord), 1, fp);
}
for (unsigned int i=0; i < output->size(); i++) {
struct neuronrecord r;
r.prop = (*output)[i].getProp();
r.actv = (*output)[i].getActv();
fwrite (&r, sizeof(struct neuronrecord), 1, fp);
}
// Saving synapsis' state
for (unsigned int i=0; i < input->size(); i++) {
int nout = (*input)[i].nOut();
fwrite (&nout, sizeof(int), 1, fp);
for (int j=0; j < nout; j++) {
struct synrecord r;
r.w = (*input)[i].synOut(j).getWeight();
r.d = (*input)[i].synOut(j).getDelta();
fwrite (&r, sizeof(struct synrecord), 1, fp);
}
}
for (unsigned int i=0; i < output->size(); i++) {
int nin = (*output)[i].nIn();
fwrite (&nin, sizeof(int), 1, fp);
for (int j=0; j < nin; j++) {
struct synrecord r;
r.w = (*output)[i].synIn(j).getWeight();
r.d = (*output)[i].synIn(j).getDelta();
fwrite (&r, sizeof(struct synrecord), 1, fp);
}
}
for (unsigned int i=0; i < hidden->size(); i++) {
int nin = (*hidden)[i].nIn();
fwrite (&nin, sizeof(int), 1, fp);
for (int j=0; j < nin; j++) {
struct synrecord r;
r.w = (*hidden)[i].synIn(j).getWeight();
r.d = (*hidden)[i].synIn(j).getDelta();
fwrite (&r, sizeof(struct synrecord), 1, fp);
}
}
for (unsigned int i=0; i < hidden->size(); i++) {
int nout = (*hidden)[i].nOut();
fwrite (&nout, sizeof(int), 1, fp);
for (int j=0; j < nout; j++) {
struct synrecord r;
r.w = (*hidden)[i].synOut(j).getWeight();
r.d = (*hidden)[i].synOut(j).getDelta();
fwrite (&r, sizeof(struct synrecord), 1, fp);
}
}
fclose(fp);
return true;
}
/**
* @brief Constructs a neural network from a previously saved file
* (saved using 'save()' method)
* @param fname File name to load the network from
* @throw NetworkFileNotFoundException
*/
NeuralNet::NeuralNet (const char *fname) throw() {
struct netrecord record;
FILE *fp;
if (!(fp=fopen(fname,"rb")))
throw NetworkFileNotFoundException();
if (fread(&record, sizeof(struct netrecord), 1, fp)<=0)
throw NetworkFileNotFoundException();
*this = NeuralNet(record.input_size, record.hidden_size, record.output_size, record.l_rate, record.epochs);
// Restore neurons
for (unsigned int i=0; i < input->size(); i++) {
struct neuronrecord r;
fread (&r, sizeof(struct neuronrecord), 1, fp);
(*input)[i].setProp(r.prop);
(*input)[i].setActv(r.actv);
(*input)[i].synClear();
}
for (unsigned int i=0; i < hidden->size(); i++) {
struct neuronrecord r;
fread (&r, sizeof(struct neuronrecord), 1, fp);
(*hidden)[i].setProp(r.prop);
(*hidden)[i].setActv(r.actv);
(*hidden)[i].synClear();
}
for (unsigned int i=0; i < output->size(); i++) {
struct neuronrecord r;
fread (&r, sizeof(struct neuronrecord), 1, fp);
(*output)[i].setProp(r.prop);
(*output)[i].setActv(r.actv);
(*output)[i].synClear();
}
for (unsigned int i=0; i < input->size(); i++)
(*input)[i].synClear();
for (unsigned int i=0; i < hidden->size(); i++)
(*hidden)[i].synClear();
for (unsigned int i=0; i < output->size(); i++)
(*output)[i].synClear();
hidden->link(*input);
output->link(*hidden);
// Restore synapsis
for (unsigned int i=0; i < input->size(); i++) {
int nout;
fread (&nout, sizeof(int), 1, fp);
for (int j=0; j < nout; j++) {
struct synrecord r;
fread (&r, sizeof(struct synrecord), 1, fp);
(*input)[i].synOut(j).setWeight(r.w);
(*input)[i].synOut(j).setDelta(r.d);
}
}
for (unsigned int i=0; i < output->size(); i++) {
int nin;
fread (&nin, sizeof(int), 1, fp);
for (int j=0; j < nin; j++) {
struct synrecord r;
fread (&r, sizeof(struct synrecord), 1, fp);
(*output)[i].synIn(j).setWeight(r.w);
(*output)[i].synIn(j).setDelta(r.d);
}
}
for (unsigned int i=0; i < hidden->size(); i++) {
int nin;
fread (&nin, sizeof(int), 1, fp);
for (int j=0; j < nin; j++) {
struct synrecord r;
fread (&r, sizeof(struct synrecord), 1, fp);
(*hidden)[i].synIn(j).setWeight(r.w);
(*hidden)[i].synIn(j).setDelta(r.d);
}
}
for (unsigned int i=0; i < hidden->size(); i++) {
int nout;
fread (&nout, sizeof(int), 1, fp);
for (int j=0; j < nout; j++) {
struct synrecord r;
fread (&r, sizeof(struct synrecord), 1, fp);
(*hidden)[i].synOut(j).setWeight(r.w);
(*hidden)[i].synOut(j).setDelta(r.d);
}
}
fclose(fp);
}
/**
* @brief Train a network using a training set loaded from an XML file. A sample XML file
* is available in examples/adder.xml
* @param xml XML file containing our training set
* @param src Source type from which the XML will be loaded (from a file [default] or from a string)
* @throw InvalidXMLException
*/
void NeuralNet::train (string xmlsrc, NeuralNet::source src = file) throw() {
float out;
CMarkup xml;
if (src == file)
xml.Load(xmlsrc.c_str());
else
xml.SetDoc(xmlsrc.c_str());
if (!xml.IsWellFormed()) {
throw InvalidXMLException();
return;
}
if (xml.FindElem("NETWORK")) {
while (xml.FindChildElem("TRAINING")) {
vector<float> input;
float output;
bool valid = false;
xml.IntoElem();
while (xml.FindChildElem("INPUT")) {
xml.IntoElem();
input.push_back(atof(xml.GetData().c_str()));
xml.OutOfElem();
}
if (xml.FindChildElem("OUTPUT")) {
xml.IntoElem();
output = atof(xml.GetData().c_str());
xml.OutOfElem();
}
xml.OutOfElem();
while (!valid) {
char str[BUFSIZ];
setInput(input);
propagate();
setExpected(output);
update();
out = getOutput();
memset (str, 0x0, sizeof(str));
snprintf (str, sizeof(str), "%f", out);
if (!strstr(str, "inf"))
valid=true;
}
}
}
return;
}
/**
* @brief Initialize the training XML for the neural network
* @param xml String that will contain the XML
*/
void NeuralNet::initXML (string& xml) {
xml.append("<?xml version=\"1.0\" encoding=\"iso-8859-1\"?>\n"
"<!DOCTYPE NETWORK SYSTEM \"http://blacklight.gotdns.org/prog/neuralpp/trainer.dtd\">\n"
"<!-- Automatically generated by Neural++ library - by BlackLight -->\n\n"
"<NETWORK>\n"
);
}
/**
* @brief Splits a string into a vector of floats, given a delimitator
* @param delim Delimitator
* @param str String to be splitted
* @return Vector of floats containing splitted values
*/
vector<float> NeuralNet::split (char delim, string str) {
char tmp[1024];
vector<float> v;
memset (tmp, 0x0, sizeof(tmp));
for (unsigned int i=0, j=0; i <= str.length(); i++) {
if (str[i] == delim || i == str.length()) {
v.push_back(atof(tmp));
memset (tmp, 0x0, sizeof(tmp));
j=0;
} else
tmp[j++] = str[i];
}
return v;
}
/**
* @brief Get a training set from a string and copies it to an XML
* For example, these strings could be training sets for making sums:
* "2,3;5" - "5,6;11" - "2,2;4" - "4,5:9"
* This method called on the first string will return an XML such this:
* '&lt;training id="0"&gt;&lt;input id="0"&gt;2&lt;/input&gt;&lt;input id="1"&gt;3&lt;/input&gt;&lt;output id="0"&gt;5&lt;/output&gt;
* &lt;/training&gt;'
*
* @param id ID for the given training set (0,1,..,n)
* @param set String containing input values and expected outputs
* @return XML string
*/
string NeuralNet::XMLFromSet (int id, string set) {
string xml;
vector<float> in, out;
unsigned int delimPos = -1;
char delim=';';
char tmp[1024];
for (delimPos=0; delimPos < set.length() && set[delimPos] != delim; delimPos++);
if (delimPos == set.length())
return xml;
string inStr = set.substr(0,delimPos);
string outStr = set.substr(delimPos+1, set.length());
in = split(',', inStr);
out = split(',', outStr);
snprintf (tmp, sizeof(tmp), "%d", id);
xml += "\t<TRAINING ID=\"" + string(tmp) + "\">\n";
for (unsigned int i=0; i < in.size(); i++) {
memset (tmp, 0x0, sizeof(tmp));
snprintf (tmp, sizeof(tmp), "%d", i);
xml += "\t\t<INPUT ID=\"" + string(tmp) + "\">";
memset (tmp, 0x0, sizeof(tmp));
snprintf (tmp, sizeof(tmp), "%f", in[i]);
xml += string(tmp) + "</INPUT>\n";
}
for (unsigned int i=0; i < out.size(); i++) {
memset (tmp, 0x0, sizeof(tmp));
snprintf (tmp, sizeof(tmp), "%d", i);
xml += "\t\t<OUTPUT ID=\"" + string(tmp) + "\">";
memset (tmp, 0x0, sizeof(tmp));
snprintf (tmp, sizeof(tmp), "%f", out[i]);
xml += string(tmp) + "</OUTPUT>\n";
}
xml += "\t</TRAINING>\n\n";
return xml;
}
/**
* @brief Closes an open XML document generated by "initXML" and "XMLFromSet"
* @param XML string to close
*/
void NeuralNet::closeXML(string &xml) {
xml.append("</NETWORK>\n\n");
}

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/**************************************************************************************************
* LibNeural++ v.0.2 - All-purpose library for managing neural networks *
* Copyright (C) 2009, BlackLight *
* *
* This program is free software: you can redistribute it and/or modify it under the terms of the *
* GNU General Public License as published by the Free Software Foundation, either version 3 of *
* the License, or (at your option) any later version. This program is distributed in the hope *
* that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of *
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for *
* more details. You should have received a copy of the GNU General Public License along with *
* this program. If not, see <http://www.gnu.org/licenses/>. *
**************************************************************************************************/
#include "neural++.h"
using namespace neuralpp;
/**
* @brief Constructor
* @param a Activation function
* @param d Its derivate
*/
Neuron::Neuron (float (*a)(float), float (*d)(float)) {
actv_f=a;
deriv=d;
}
/**
* @brief Alternative constructor, that gets also the synapsis linked to the neuron
*/
Neuron::Neuron (vector< Synapsis > i, vector< Synapsis > o, float (*a)(float), float(*d)(float)) {
in=i;
out=o;
actv_f=a;
deriv=d;
}
/**
* @brief Gets the i-th synapsis connected on the input of the neuron
*/
Synapsis& Neuron::synIn (size_t i) { return in[i]; }
/**
* @brief Gets the i-th synapsis connected on the output of the neuron
*/
Synapsis& Neuron::synOut (size_t i) { return out[i]; }
/**
* @brief It pushes a new input synapsis
*/
void Neuron::push_in (Synapsis& s) { in.push_back(s); }
/**
* @brief It pushes a new output synapsis
*/
void Neuron::push_out (Synapsis& s) { out.push_back(s); }
/**
* @brief Change the propagation value of the neuron
*/
void Neuron::setProp (float val) { prop_val=val; }
/**
* @brief Change the activation value of the neuron
*/
void Neuron::setActv (float val) { actv_val=actv_f(val); }
/**
* @return Number of input synapsis
*/
size_t Neuron::nIn() { return in.size(); }
/**
* @return Number of output synapsis
*/
size_t Neuron::nOut() { return out.size(); }
/**
* @brief It gets the propagation value of the neuron
*/
float Neuron::getProp() { return prop_val; }
/**
* @brief It gets the activation value of the neuron
*/
float Neuron::getActv() { return actv_val; }
/**
* @brief Propagate a neuron's activation value to the connected neurons
*/
float Neuron::propagate() {
float aux=0;
for (size_t i=0; i<nIn(); i++)
aux += (in[i].getWeight() * in[i].getIn()->actv_val);
return aux;
}

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/**************************************************************************************************
* LibNeural++ v.0.2 - All-purpose library for managing neural networks *
* Copyright (C) 2009, BlackLight *
* *
* This program is free software: you can redistribute it and/or modify it under the terms of the *
* GNU General Public License as published by the Free Software Foundation, either version 3 of *
* the License, or (at your option) any later version. This program is distributed in the hope *
* that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of *
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for *
* more details. You should have received a copy of the GNU General Public License along with *
* this program. If not, see <http://www.gnu.org/licenses/>. *
**************************************************************************************************/
#include "neural++.h"
using namespace neuralpp;
/**
* @brief Constructor
* @param i Input neuron
* @param o Output neuron
* @param a Activation function
* @param d Derivate for activation function
*/
Synapsis::Synapsis (Neuron* i, Neuron* o, float(*a)(float), float(*d)(float)) {
srand((unsigned) time(NULL));
delta=0;
weight=RAND;
in=i;
out=o;
actv_f=a;
deriv=d;
}
/**
* @brief Constructor
* @param i Input neuron
* @param o Output neuron
* @param w Weight for the synapsis (default: random)
* @param a Activation function
* @param d Derivate for activation function
*/
Synapsis::Synapsis (Neuron* i, Neuron* o, float w, float(*a)(float), float(*d)(float)) {
delta=0;
weight=w;
in=i;
out=o;
actv_f=a;
deriv=d;
}
/**
* @return Reference to input neuron of the synapsis
*/
Neuron* Synapsis::getIn() { return in; }
/**
* @return Reference to output neuron of the synapsis
*/
Neuron* Synapsis::getOut() { return out; }
/**
* @return Weight of the synapsis
*/
float Synapsis::getWeight() { return weight; }
/**
* @return Delta of the synapsis
*/
float Synapsis::getDelta() { return delta; }
/**
* @brief It sets the weight of the synapsis
*/
void Synapsis::setWeight(float w) { weight=w; }
/**
* @brief It sets the delta (how much to change the weight after an update)
* of the synapsis
*/
void Synapsis::setDelta(float d) { delta=d; }