Making everything cooler

BUGS

@@ -0,0 +1,12 @@
+Sometimes the training phase of the network breaks in the middle. It happens
+because the synaptical weights are initialized with random values, and
+sometimes updating them causes those values to become >= 1. This makes the
+output values of the network diverge instead of converging to the desired,
+expected values. The library recognizes this behaviour, and when a weight
+become >= 1 throws an InvalidSynapticalWeightException. So far there's no
+way to prevent this odd, random behaviour. The network implements the usage
+of an inertial momentum coefficient to avoid strong oscillations in the
+training phase, in order to make this phenomenon rarer, but also using this
+mechanism there's a possibility ~ 10% of getting a diverging network, and
+so a training phase broken by an InvalidSynapticalWeightException.
+

INSTALL

@@ -0,0 +1,3 @@
+$ make
+% make install
+

Makefile

@@ -17,10 +17,20 @@ all:
 install:
 	mkdir -p ${PREFIX}/lib
 	mkdir -p ${PREFIX}/${INCLUDEDIR}
+	mkdir -p ${PREFIX}/share
+	mkdir -p ${PREFIX}/share/${LIB}
+	install -m 0644 README ${PREFIX}/share/${LIB}/README
+	install -m 0644 INSTALL ${PREFIX}/share/${LIB}/INSTALL
+	install -m 0644 BUGS ${PREFIX}/share/${LIB}/BUGS
+	install -m 0644 VERSION ${PREFIX}/share/${LIB}/VERSION
+	install -m 0644 ChangeLog ${PREFIX}/share/${LIB}/ChangeLog
+	cp -r examples ${PREFIX}/share/${LIB}
+	cp -r doc ${PREFIX}/share/${LIB}
 	install -m 0755 lib${LIB}.so.0.0.0 ${PREFIX}/lib/lib${LIB}.so.0.0.0
 	install -m 0644 lib${LIB}.a ${PREFIX}/lib/lib${LIB}.a
 	install -m 0644 ${INCLUDEDIR}/${LIB}.hpp ${PREFIX}/${INCLUDEDIR}
 	install -m 0644 ${INCLUDEDIR}/${LIB}_exception.hpp ${PREFIX}/${INCLUDEDIR}
+	install -m 0644 ${PREFIX}/share/${LIB}/README
 	ln -sf ${PREFIX}/lib/lib${LIB}.so.0.0.0 ${PREFIX}/lib/lib${LIB}.so.0
 
 uninstall:
@@ -29,8 +39,17 @@ uninstall:
 	rm ${PREFIX}/${INCLUDEDIR}/${LIB}_exception.hpp
 	rm ${PREFIX}/lib/lib${LIB}.so.0.0.0
 	rm ${PREFIX}/lib/lib${LIB}.so.0
+	rm ${PREFIX}/share/${LIB}/README
+	rm ${PREFIX}/share/${LIB}/INSTALL
+	rm ${PREFIX}/share/${LIB}/BUGS
+	rm ${PREFIX}/share/${LIB}/VERSION
+	rm ${PREFIX}/share/${LIB}/ChangeLog
+	rm -r ${PREFIX}/share/${LIB}/doc
+	rm -r ${PREFIX}/share/${LIB}/examples
+	rmdir ${PREFIX}/share/${LIB}
 
 clean:
 	rm *.o
 	rm lib${LIB}.so.0.0.0
 	rm lib${LIB}.a
+

examples/Makefile

@@ -1,5 +1,6 @@
 all:
 	g++ -Wall -o learnAdd learnAdd.cpp -lneural++
+	g++ -Wall -o doAnd doAnd.cpp -lneural++
 	g++ -Wall -o doAdd doAdd.cpp -lneural++
 	g++ -Wall -o adderFromScratch adderFromScratch.cpp -lneural++
 

examples/learnAdd.cpp

@@ -15,14 +15,14 @@ using namespace std;
 using namespace neuralpp;
 
 double f (double x)  {
-	return (1.0/(1.0 + pow(M_E,x)));
+	return (x <= 0) ? 1 : 0;
 }
 
 int main()  {
 	int id = 0;
 	string xml;
 	time_t t1, t2;
-	NeuralNet net(2, 2, 1, 0.005, 100);
+	NeuralNet net(2, 2, 1, 0.005, 1000, 0.1, f);
 
 	NeuralNet::initXML(xml);
 	xml += NeuralNet::XMLFromSet(id, "2,3;5");

include/neural++.hpp

@@ -28,7 +28,7 @@
 #define 	RAND 	(double) ( (rand() / (RAND_MAX/2)) - 1)
 
 //! Initial value for the inertial momentum of the synapses
-#define 	BETA0 	0.8
+#define 	BETA0 	1.0
 
 /**
  * @namespace neuralpp
@@ -108,7 +108,8 @@ namespace neuralpp  {
 		 *   'sensitive' on variations of the input values
 		 * @param a Activation function to use (default: f(x)=x)
 		 */
-		NeuralNet (size_t in_size, size_t hidden_size, size_t out_size, double l, int e, double th = 0.0, double (*a)(double) = __actv);
+		NeuralNet (size_t in_size, size_t hidden_size, size_t out_size, double l,
+				int e, double th = 0.0, double (*a)(double) = __actv);
 
 		/**
 		 * @brief Constructor
@@ -117,23 +118,6 @@ namespace neuralpp  {
 		 */
 		NeuralNet (const std::string file) throw(NetworkFileNotFoundException);
 		
-		
-		/**
-		 * @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 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)
-		 * @param th Threshold, value in [0,1] that establishes how much a neuron must be
-		 *   'sensitive' on variations of the input values
-		 */
-		//NeuralNet (size_t in_size, size_t hidden_size, size_t out_size,
-		//		double(*actv)(double), double l, int e, double th);
-
 		/**
 		 * @brief It gets the output of the network (note: the layer output should contain
 		 * an only neuron)

src/neuron.cpp

@@ -77,6 +77,7 @@ namespace neuralpp {
 		for (size_t i = 0; i < nIn(); i++)
 			aux += (in[i].getWeight() * in[i].getIn()->actv_val);
 
+		aux -= threshold;
 		setProp(aux);
 		setActv( actv_f(aux) );
 	}