Fixin' namespace neuralpp

ChangeLog

@@ -6,7 +6,8 @@
 	* neural++.hpp: Changed header name, added BETA0 macro
 	* synapsis.cpp: Added momentum() method to compute the inertial momentum
 	of a synapsis
-	* everything: Data type changed from float to double for everything
+	* everything: Data type changed from float to double for everything,
+	fixing neuralpp namespace
 
 --- Release 0.2.2 ---
 

include/neural++.hpp

@@ -28,13 +28,13 @@
 #include "neural++_exception.hpp"
 using namespace std;
 
-namespace neuralpp  {
 //! Default rand value: |sin(rand)|, always >= 0 and <= 1
-	#define	RAND		( (double) abs( sinf((double) rand()) ) )
+#define	RAND		( abs( sin(rand()) ) )
 
 //! Initial value for the inertial momentum of the synapses
 #define 	BETA0 	0.7
 	
+namespace neuralpp  {
 	class Synapsis;
 	class Neuron;
 	class Layer;

src/layer.cpp

@@ -15,12 +15,7 @@
 #include "neural++.hpp"
 using namespace neuralpp;
 
-/**
+namespace neuralpp  {
- * @brief Constructor
- * @param sz Size of the layer
- * @param a Activation function
- * @param d Its derivate
- */
 Layer::Layer (size_t sz, double(*a)(double), double(*d)(double))  {
 	for (size_t i=0; i<sz; i++)  {
 		Neuron n(a,d);
@@ -31,30 +26,16 @@ Layer::Layer (size_t sz, double(*a)(double), double(*d)(double))  {
 	deriv=d;
 }
 
-/**
- * @brief Alternative constructor. It directly gets a vector of neurons to build
- *  the layer
- */
 Layer::Layer (vector< Neuron > &el, double (*a)(double), double(*d)(double))  {
 	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));
 
@@ -71,27 +52,16 @@ void Layer::link (Layer& l)  {
 	}
 }
 
-/**
- * @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<double> &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<double> &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]);
@@ -100,4 +70,5 @@ void Layer::propagate()  {
 		n->setActv( actv_f(n->getProp()) );
 	}
 }
+}
 

src/neuralnet.cpp

@@ -14,28 +14,13 @@
 #include "neural++.hpp"
 #include "Markup.h"
 #include <iostream>
-using namespace neuralpp;
 
-/**
+namespace neuralpp  {
- * @brief Built-in function. The default activation function: f(x)=x
+
- */
 double __actv(double prop)  { return prop; }
 
-/**
- * @brief Default derivate for default activation function: f'(x)=1
- */
 double __deriv(double 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, double l, int e)  {
 	epochs=e;
 	ref_epochs=epochs;
@@ -49,18 +34,6 @@ NeuralNet::NeuralNet (size_t in_size, size_t hidden_size, size_t out_size, doubl
 	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,
 		double(*a)(double), double(*d)(double), double l, int e)  {
 	epochs=e;
@@ -76,15 +49,8 @@ NeuralNet::NeuralNet (size_t in_size, size_t hidden_size, size_t out_size,
 	link();
 }
 
-/**
- * @brief It gets the output of the network (note: the layer output should contain
- *   an only neuron)
- */
 double NeuralNet::getOutput()  { return (*output)[0].getActv(); }
 
-/**
- * @brief It gets the output of the network in case the output layer contains more neurons
- */
 vector<double> NeuralNet::getVectorOutput()  {
 	vector<double> v;
 
@@ -93,58 +59,30 @@ vector<double> NeuralNet::getVectorOutput()  {
 	return v;
 }
 
-/**
- * @brief It get the error made on the expected result as |v-v'|/v
- * @param Expected value
- * @return Mean error
- */
 double NeuralNet::error(double 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 doubles, containing the values to give to your network
- */
 void NeuralNet::setInput(vector<double>& 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(double e)  { ex=e; }
 
-/**
- * @brief It gets the value expected. Of course you should specify this when you
- *   build your network by using setExpected.
- */
 double NeuralNet::expected()  { return ex; }
 
-/**
- * @brief It updates the weights of the net's synapsis through back-propagation.
- *   In-class use only
- */
 void NeuralNet::updateWeights()  {
 	double out_delta;
 
@@ -180,11 +118,6 @@ void NeuralNet::updateWeights()  {
 	}
 }
 
-/**
- * @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];
@@ -197,11 +130,6 @@ void NeuralNet::commitChanges (Layer *l)  {
 	}
 }
 
-/**
- * @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();
@@ -211,11 +139,6 @@ void NeuralNet::update()  {
 	}
 }
 
-/**
- * @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;
@@ -309,12 +232,6 @@ bool NeuralNet::save(const char *fname)  {
 	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;
@@ -423,13 +340,6 @@ NeuralNet::NeuralNet (const char *fname) throw() {
 	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()  {
 	double out;
 	CMarkup xml;
@@ -487,10 +397,6 @@ void NeuralNet::train (string xmlsrc, NeuralNet::source src = file) throw()  {
 	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"
@@ -499,12 +405,6 @@ void NeuralNet::initXML (string& xml)  {
 	);
 }
 
-/**
- * @brief Splits a string into a vector of doubles, given a delimitator
- * @param delim Delimitator
- * @param str String to be splitted
- * @return Vector of doubles containing splitted values
- */
 vector<double> NeuralNet::split (char delim, string str)  {
 	char tmp[1024];
 	vector<double> v;
@@ -522,18 +422,6 @@ vector<double> NeuralNet::split (char delim, string str)  {
 	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<double> in, out;
@@ -579,11 +467,8 @@ string NeuralNet::XMLFromSet (int id, string set)  {
 	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");
 }
+}
 

src/neuron.cpp

@@ -12,21 +12,14 @@
  **************************************************************************************************/
 
 #include "neural++.hpp"
-using namespace neuralpp;
 
-/**
+namespace neuralpp  {
- * @brief Constructor
+
- * @param a Activation function
- * @param d Its derivate
- */
 Neuron::Neuron (double (*a)(double), double (*d)(double))  {
 	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, double (*a)(double), double(*d)(double))  {
 	in=i;
 	out=o;
@@ -35,59 +28,26 @@ Neuron::Neuron (vector< Synapsis > i, vector< Synapsis > o, double (*a)(double),
 	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 (double val)  { prop_val=val; }
 
-/**
- * @brief Change the activation value of the neuron
- */
 void Neuron::setActv (double 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
- */
 double Neuron::getProp()  { return prop_val; }
 
-/**
- * @brief It gets the activation value of the neuron
- */
 double Neuron::getActv()  { return actv_val; }
 
-/**
- * @brief Propagate a neuron's activation value to the connected neurons
- */
 double Neuron::propagate()  {
 	double aux=0;
 
@@ -95,4 +55,5 @@ double Neuron::propagate()  {
 		aux += (in[i].getWeight() * in[i].getIn()->actv_val);
 	return aux;
 }
+}
 

src/synapsis.cpp

@@ -15,6 +15,8 @@
 #include "neural++.hpp"
 using namespace neuralpp;
 
+namespace neuralpp  {
+
 Synapsis::Synapsis(Neuron* i, Neuron* o, double w, double d)  {
 	in=i; out=o;
 	weight=w;
@@ -66,4 +68,5 @@ void Synapsis::setDelta(double d)  {
 double Synapsis::momentum(int N, int x)  {
 	return (BETA0*N)/(20*x + N);
 }
+}