It finally supports multiple outputs for the network

examples/adder.xml

@@ -10,22 +10,25 @@
 -->
 
 <NETWORK NAME="adder">
-	<TRAINING ID="1">
+	<TRAINING ID="0">
-		<INPUT ID="0">2</INPUT>
 		<INPUT ID="1">3</INPUT>
-		<OUTPUT ID="0">5</OUTPUT>
+		<INPUT ID="2">2</INPUT>
+		<OUTPUT ID="3">5</OUTPUT>
+		<OUTPUT ID="4">1</OUTPUT>
 	</TRAINING>
 
-	<TRAINING ID="1">
+	<TRAINING ID="5">
-		<INPUT ID="0">3</INPUT>
+		<INPUT ID="6">5</INPUT>
-		<INPUT ID="1">4</INPUT>
+		<INPUT ID="7">3</INPUT>
-		<OUTPUT ID="0">7</OUTPUT>
+		<OUTPUT ID="8">8</OUTPUT>
+		<OUTPUT ID="9">2</OUTPUT>
 	</TRAINING>
 
-	<TRAINING ID="2">
+	<TRAINING ID="10">
-		<INPUT ID="0">10</INPUT>
+		<INPUT ID="11">6</INPUT>
-		<INPUT ID="1">10</INPUT>
+		<INPUT ID="12">3</INPUT>
-		<OUTPUT ID="0">20</OUTPUT>
+		<OUTPUT ID="13">9</OUTPUT>
+		<OUTPUT ID="14">3</OUTPUT>
 	</TRAINING>
 </NETWORK>
 

examples/adderFromScratch.cpp

@@ -12,17 +12,18 @@ using namespace std;
 using namespace neuralpp;
 
 int main()  {
-	NeuralNet net(3, 3, 1, 0.005, 1000);
+	NeuralNet net(2, 2, 2, 0.005, 1000);
 	string xml;
 	double tmp;
 
 	// XML initialization. Then, I say XML that 2+3=5, 3+3=6, 5+4=9
 	// Strings' format is "input1,input2,...,inputn;output1,output2,...,outputm
 	NeuralNet::initXML(xml);
-	xml += NeuralNet::XMLFromSet(0, "2,3,4;9");
+	xml += NeuralNet::XMLFromSet(0, "3,2;5,1");
-	xml += NeuralNet::XMLFromSet(1, "3,3,1;7");
+	xml += NeuralNet::XMLFromSet(1, "4,2;6,2");
-	xml += NeuralNet::XMLFromSet(2, "5,4,2;11");
+	xml += NeuralNet::XMLFromSet(2, "6,3;9,3");
 	NeuralNet::closeXML(xml);
+	cout << xml << endl;
 	
 	net.train(xml, NeuralNet::str);
 	vector<double> v;
@@ -36,13 +37,9 @@ int main()  {
 	cin >> tmp;
 	v.push_back(tmp);
 
-	cout << "Third number to add: ";
-	cin >> tmp;
-	v.push_back(tmp);
-
 	net.setInput(v);
 	net.propagate();
-	cout << "Output: " << net.getOutput() << endl;
+	cout << "Output: " << net.getOutputs()[0] << "; " << net.getOutputs()[1] << endl;
 	return 0;
 }
 

examples/doAdd.cpp

@@ -38,7 +38,7 @@ int main()  {
 
 	net.setInput(v);
 	net.propagate();
-	cout << "Neural net output: " << net.getOutput() << endl;
+	cout << "Neural net output: " << net.getOutputs()[0] << "; " << net.getOutputs()[1] << endl;
 
 	return 0;
 }

examples/learnAdd.cpp

@@ -13,7 +13,7 @@ using namespace std;
 using namespace neuralpp;
 
 int main()  {
-	NeuralNet net(2, 2, 1, 0.005, 1000);
+	NeuralNet net(2, 2, 2, 0.005, 1000);
 
 	cout << "Training in progress - This may take a while...\n";
 	net.train("adder.xml", NeuralNet::file);

include/neural++.hpp

@@ -289,12 +289,12 @@ namespace neuralpp  {
 		/**
 		 * @return Reference to input neuron of the synapsis
 		 */
-		Neuron* getIn();
+		Neuron* getIn() const;
 		
 		/**
 		 * @return Reference to output neuron of the synapsis
 		 */
-		Neuron* getOut();
+		Neuron* getOut() const;
 
 		/**
 		 * @brief Set the weight of the synapsis
@@ -313,19 +313,19 @@ namespace neuralpp  {
 		 * @brief Return the weight of the synapsis
 		 * @return Weight of the synapsis
 		 */
-		double getWeight();
+		double getWeight() const;
 		
 		/**
 		 * @brief Return the delta of the synapsis
 		 * @return Delta of the synapsis
 		 */
-		double getDelta();
+		double getDelta() const;
 		
 		/**
 		 * @brief Get the delta of the synapsis at the previous iteration
 		 * @return The previous delta
 		 */
-		double getPrevDelta();
+		double getPrevDelta() const;
 
 		/**
 		 * @brief Get the inertial momentum of a synapsis. This value is inversely proportional
@@ -337,7 +337,7 @@ namespace neuralpp  {
 		 * @param x The number of iterations already taken
 		 * @return The inertial momentum of the synapsis
 		 */
-		double momentum (int N, int x);
+		double momentum (int N, int x) const;
 	};
 
 	/**

src/neuralnet.cpp

@@ -11,7 +11,6 @@
  * this program. If not, see <http://www.gnu.org/licenses/>.                                      *
  **************************************************************************************************/
 
-#include <iostream>
 #include <fstream>
 #include <sstream>
 
@@ -87,7 +86,7 @@ namespace neuralpp {
 		output->propagate();
 	}
 
-	void NeuralNet::setInput(vector <double> v) {
+	void NeuralNet::setInput(vector<double> v) {
 		input->setInput(v);
 	}
 
@@ -473,8 +472,8 @@ namespace neuralpp {
 				xml.OutOfElem();
 
 				setInput(input);
-				propagate();
 				setExpected(output);
+				propagate();
 				update();
 			}
 		}

src/synapsis.cpp

@@ -47,23 +47,23 @@ namespace neuralpp {
 		actv_f = a;
 	}
 
-	Neuron *Synapsis::getIn() {
+	Neuron *Synapsis::getIn() const  {
 		return in;
 	}
 
-	Neuron *Synapsis::getOut() {
+	Neuron *Synapsis::getOut() const  {
 		return out;
 	}
 
-	double Synapsis::getWeight() {
+	double Synapsis::getWeight() const  {
 		return weight;
 	}
 
-	double Synapsis::getDelta() {
+	double Synapsis::getDelta() const  {
 		return delta;
 	}
 
-	double Synapsis::getPrevDelta() {
+	double Synapsis::getPrevDelta() const  {
 		return prev_delta;
 	}
 
@@ -82,7 +82,7 @@ namespace neuralpp {
 		delta = d;
 	}
 
-	double Synapsis::momentum(int N, int x) {
+	double Synapsis::momentum(int N, int x) const  {
 		return (BETA0 * N) / (20 * x + N);
 	}
 }