Trying to fix, again...

include/neural++.hpp

@@ -26,10 +26,10 @@
 using namespace std;
 
 //! Default rand value: |sin(rand)|, always >= 0 and <= 1
-#define	RAND		( abs( sin(rand()) ) )
+#define 	RAND 	(double) ( (rand() / (RAND_MAX/2)) - 1)
 
 //! Initial value for the inertial momentum of the synapses
-#define 	BETA0 	0.7
+#define 	BETA0 	0.8
 
 /**
  * @namespace neuralpp
@@ -40,8 +40,6 @@ namespace neuralpp  {
 	class Neuron;
 	class Layer;
 	class NeuralNet;
-	class NetworkFileNotFoundException;
-	class InvalidXMLException;
 
 	/**
 	 * @class NeuralNet
@@ -67,11 +65,11 @@ namespace neuralpp  {
 		void commitChanges (Layer *l);
 
 		/**
-		 * @brief It get the error made on the expected result as |v-v'|/v
+		 * @brief Get the error made on the expected result as |v-v'|/v
 		 * @param ex Expected value
 		 * @return Mean error
 		 */
-		double error(double ex);
+		double error(double ex) const;
 		
 		/**
 		 * @brief Private pointer to function, containing the function to
@@ -117,7 +115,7 @@ namespace neuralpp  {
 		 * @param file Binary file containing a neural network previously saved by save() method
 		 * @throw NetworkFileNotFoundException
 		 */
-		NeuralNet (const char* file) throw(NetworkFileNotFoundException);
+		NeuralNet (const string file) throw(NetworkFileNotFoundException);
 		
 		
 		/**
@@ -140,7 +138,7 @@ namespace neuralpp  {
 		 * an only neuron)
 		 * @return The output value of the network
 		 */
-		double getOutput();
+		double getOutput() const;
 
 		/**
 		 * @brief It gets the output of the network in case the output layer contains more neurons
@@ -153,7 +151,7 @@ namespace neuralpp  {
 		 * build your network by using setExpected.
 		 * @return The expected output value for a certain training phase
 		 */
-		double expected();
+		double expected() const;
 
 		/**
 		 * @brief It sets the value you expect from your network
@@ -189,8 +187,10 @@ namespace neuralpp  {
 		/**
 		 * @brief Save a trained neural network to a binary file
 		 * @param fname Binary file where you're going to save your network
+		 * @throws NetworkFileWriteException When you get an error writing the network's information to
+		 * a file
 		 */
-		bool save(const char* fname);
+		void save(const char* fname) throw(NetworkFileWriteException);
 
 		/**
 		 * @brief Train a network using a training set loaded from an XML file. A sample XML file
@@ -296,14 +296,14 @@ namespace neuralpp  {
 		 * @brief Set the weight of the synapsis
 		 * @param w Weight to be set
 		 */
-		void setWeight(double w);
+		void setWeight(double w) throw(InvalidSynapticalWeightException);
 		
 		/**
 		 * @brief It sets the delta (how much to change the weight after an update) 
 		 * of the synapsis
 		 * @param d Delta to be set
 		 */
-		void setDelta(double d);
+		void setDelta(double d) throw(InvalidSynapticalWeightException);
 
 		/**
 		 * @brief Return the weight of the synapsis
@@ -476,7 +476,7 @@ namespace neuralpp  {
 		 * @param i Index of the neuron to get in the layer
 		 * @return Reference to the i-th neuron
 		 */
-		Neuron& operator[] (size_t i);
+		Neuron& operator[] (size_t i) throw(NetworkIndexOutOfBoundsException);
 
 		/**
 		 * @brief It links a layer to another
@@ -504,7 +504,7 @@ namespace neuralpp  {
 		/**
 		 * @return Number of neurons in the layer
 		 */
-		size_t size();
+		size_t size() const;
 	};
 
 	struct netrecord  {

include/neural++_exception.hpp

@@ -31,6 +31,17 @@ namespace neuralpp  {
 		const char* what() const throw() { return "Attempt to load a neural network from an invalid network file"; }
 	};
 
+	/**
+	 * @class NetworkFileWriteException
+	 * @brief Exception thrown when trying to write the network's information to a file that cannot
+	 * be written
+	 */
+	class NetworkFileWriteException : public std::exception  {
+	public:
+		NetworkFileWriteException()  {}
+		const char* what() const throw()  { return "There was an error while writing the network file"; }
+	};
+
 	/**
 	 * @class InvalidXMLException
 	 * @brief Exception thrown when trying parsing an invalid XML
@@ -40,6 +51,28 @@ namespace neuralpp  {
 		InvalidXMLException()  {}
 		const char* what() const throw() { return "Attempt to load an invalid XML file"; }
 	};
+
+	/**
+	 * @class NetworkIndexOutOfBoundsException
+	 * @brief Exception raised when trying to access a neuron whose index is larger than the number
+	 * of neurons in the network
+	 */
+	class NetworkIndexOutOfBoundsException : public std::exception  {
+	public:
+		NetworkIndexOutOfBoundsException()  {}
+		const char* what() const throw()  { return "Attempt to access a non-existing neuron"; }
+	};
+
+	/**
+	 * @class InvalidSynapticalWeightException
+	 * @brief Exception raised when, while trying the network or directly, the weight of a synapsis is
+	 * set to a value |w| > 1
+	 */
+	class InvalidSynapticalWeightException : public std::exception  {
+	public:
+		InvalidSynapticalWeightException()  {}
+		const char* what() const throw()  { return "Attempt to set an invalid weight for the synapsis"; }
+	};
 }
 
 #endif

src/layer.cpp

@@ -19,7 +19,9 @@ namespace neuralpp {
 		for (size_t i = 0; i < sz; i++) {
 			Neuron n(a, d);
 			 elements.push_back(n);
-		} actv_f = a;
+		}
+		
+		actv_f = a;
 		deriv = d;
 	}
 
@@ -30,11 +32,14 @@ namespace neuralpp {
 		deriv = d;
 	}
 
-	size_t Layer::size() {
+	size_t Layer::size() const  {
 		return elements.size();
 	}
 
-	Neuron & Layer::operator[](size_t i) {
+	Neuron & Layer::operator[](size_t i) throw(NetworkIndexOutOfBoundsException)  {
+		if (i > size())
+			throw NetworkIndexOutOfBoundsException();
+
 		return elements[i];
 	}
 

src/neuralnet.cpp

@@ -11,7 +11,9 @@
  * this program. If not, see <http://www.gnu.org/licenses/>.                                      *
  **************************************************************************************************/
 
+#include <fstream>
 #include <sstream>
+
 #include "neural++.hpp"
 #include "Markup.h"
 
@@ -56,7 +58,7 @@ namespace neuralpp {
 		link();
 	}
 
-	double NeuralNet::getOutput() {
+	double NeuralNet::getOutput() const  {
 		return (*output)[0].getActv();
 	}
 
@@ -68,7 +70,7 @@ namespace neuralpp {
 		return v;
 	}
 
-	double NeuralNet::error(double expected) {
+	double NeuralNet::error(double expected) const  {
 		return abs((getOutput() - expected *
 			    deriv(getOutput())) / (abs(expected)));
 	}
@@ -92,7 +94,7 @@ namespace neuralpp {
 		ex = e;
 	}
 
-	double NeuralNet::expected() {
+	double NeuralNet::expected() const  {
 		return ex;
 	}
 
@@ -168,12 +170,12 @@ namespace neuralpp {
 		}
 	}
 
-	bool NeuralNet::save(const char *fname) {
-		FILE *fp;
+	void NeuralNet::save (const char *fname) throw(NetworkFileWriteException)  {
 		struct netrecord record;
+		ofstream out(fname);
 
-		if (!(fp = fopen(fname, "wb")))
-			return false;
+		if (!out)
+			throw NetworkFileWriteException();
 
 		record.input_size = input->size();
 		record.hidden_size = hidden->size();
@@ -183,96 +185,113 @@ namespace neuralpp {
 		record.l_rate = l_rate;
 		record.ex = ex;
 
-		if (fwrite(&record, sizeof(struct netrecord), 1, fp) <= 0)
-			return false;
+		if (out.write((char*) &record, sizeof(struct netrecord)) <= 0)
+			throw NetworkFileWriteException();
 
 		// 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);
+
+			if (out.write((char*) &r, sizeof(struct neuronrecord)) <= 0)
+				throw NetworkFileWriteException();
 		}
 
 		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);
+			
+			if (out.write((char*) &r, sizeof(struct neuronrecord)) <= 0)
+				throw NetworkFileWriteException();
 		}
 
 		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);
+
+			if (out.write((char*) &r, sizeof(struct neuronrecord)) <= 0)
+				throw NetworkFileWriteException();
 		}
 
 		// Saving synapsis' state
 		for (unsigned int i = 0; i < input->size(); i++) {
 			int nout = (*input)[i].nOut();
-			fwrite(&nout, sizeof(int), 1, fp);
+
+			if (out.write((char*) &nout, sizeof(int)) <= 0)
+				throw NetworkFileWriteException();
 
 			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);
+
+				if (out.write((char*) &r, sizeof(struct synrecord)) <= 0)
+					throw NetworkFileWriteException();
 			}
 		}
 
 		for (unsigned int i = 0; i < output->size(); i++) {
 			int nin = (*output)[i].nIn();
-			fwrite(&nin, sizeof(int), 1, fp);
 
+			if (out.write((char*) &nin, sizeof(int)) <= 0)
+				throw NetworkFileWriteException();
+			
 			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);
+
+				if (out.write((char*) &r, sizeof(struct synrecord)) <= 0)
+					throw NetworkFileWriteException();
 			}
 		}
 
 		for (unsigned int i = 0; i < hidden->size(); i++) {
 			int nin = (*hidden)[i].nIn();
-			fwrite(&nin, sizeof(int), 1, fp);
+
+			if (out.write((char*) &nin, sizeof(int)) <= 0)
+				throw NetworkFileWriteException();
 
 			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);
+
+				if (out.write((char*) &r, sizeof(struct synrecord)) <= 0)
+					throw NetworkFileWriteException();
 			}
 		}
 
 		for (unsigned int i = 0; i < hidden->size(); i++) {
 			int nout = (*hidden)[i].nOut();
-			fwrite(&nout, sizeof(int), 1, fp);
+
+			if (out.write((char*) &nout, sizeof(int)) <= 0)
+				throw NetworkFileWriteException();
 
 			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);
+
+				if (out.write((char*) &r, sizeof(struct synrecord)) <= 0)
+					throw NetworkFileWriteException();
 			}
 		}
 
-		fclose(fp);
-		return true;
+		out.close();
 	}
 
-	NeuralNet::NeuralNet(const char *fname) throw(NetworkFileNotFoundException) {
+	NeuralNet::NeuralNet(const string fname) throw(NetworkFileNotFoundException) {
 		struct netrecord record;
-		FILE *fp;
+		ifstream in(fname.c_str());
 
-		if (!(fp = fopen(fname, "rb")))
+		if (!in)
 			throw NetworkFileNotFoundException();
 
-		if (fread(&record, sizeof(struct netrecord), 1, fp) <= 0)
+		if (in.read((char*) &record, sizeof(struct netrecord)) <= 0)
 			throw NetworkFileNotFoundException();
 
 		*this =
@@ -283,7 +302,9 @@ namespace neuralpp {
 		// Restore neurons
 		for (unsigned int i = 0; i < input->size(); i++) {
 			struct neuronrecord r;
-			fread(&r, sizeof(struct neuronrecord), 1, fp);
+
+			if (in.read((char*) &r, sizeof(struct neuronrecord)) <= 0)
+				throw NetworkFileNotFoundException();
 
 			(*input)[i].setProp(r.prop);
 			(*input)[i].setActv(r.actv);
@@ -292,7 +313,9 @@ namespace neuralpp {
 
 		for (unsigned int i = 0; i < hidden->size(); i++) {
 			struct neuronrecord r;
-			fread(&r, sizeof(struct neuronrecord), 1, fp);
+			
+			if (in.read((char*) &r, sizeof(struct neuronrecord)) <= 0)
+				throw NetworkFileNotFoundException();
 
 			(*hidden)[i].setProp(r.prop);
 			(*hidden)[i].setActv(r.actv);
@@ -301,7 +324,9 @@ namespace neuralpp {
 
 		for (unsigned int i = 0; i < output->size(); i++) {
 			struct neuronrecord r;
-			fread(&r, sizeof(struct neuronrecord), 1, fp);
+			
+			if (in.read((char*) &r, sizeof(struct neuronrecord)) <= 0)
+				throw NetworkFileNotFoundException();
 
 			(*output)[i].setProp(r.prop);
 			(*output)[i].setActv(r.actv);
@@ -323,12 +348,16 @@ namespace neuralpp {
 		// Restore synapsis
 		for (unsigned int i = 0; i < input->size(); i++) {
 			int nout;
-			fread(&nout, sizeof(int), 1, fp);
 
+			if (in.read((char*) &nout, sizeof(int)) <= 0 )
+				throw NetworkFileNotFoundException();
+			
 			for (int j = 0; j < nout; j++) {
 				struct synrecord r;
-				fread(&r, sizeof(struct synrecord), 1, fp);
 
+				if (in.read((char*) &r, sizeof(struct synrecord)) <= 0)
+					throw NetworkFileNotFoundException();
+				
 				(*input)[i].synOut(j).setWeight(r.w);
 				(*input)[i].synOut(j).setDelta(r.d);
 			}
@@ -336,11 +365,15 @@ namespace neuralpp {
 
 		for (unsigned int i = 0; i < output->size(); i++) {
 			int nin;
-			fread(&nin, sizeof(int), 1, fp);
+
+			if (in.read((char*) &nin, sizeof(int)) <= 0)
+				throw NetworkFileNotFoundException();
 
 			for (int j = 0; j < nin; j++) {
 				struct synrecord r;
-				fread(&r, sizeof(struct synrecord), 1, fp);
+				
+				if (in.read((char*) &r, sizeof(struct synrecord)) <= 0)
+					throw NetworkFileNotFoundException();
 
 				(*output)[i].synIn(j).setWeight(r.w);
 				(*output)[i].synIn(j).setDelta(r.d);
@@ -349,11 +382,15 @@ namespace neuralpp {
 
 		for (unsigned int i = 0; i < hidden->size(); i++) {
 			int nin;
-			fread(&nin, sizeof(int), 1, fp);
+			
+			if (in.read((char*) &nin, sizeof(int)) <= 0)
+				throw NetworkFileNotFoundException();
 
 			for (int j = 0; j < nin; j++) {
 				struct synrecord r;
-				fread(&r, sizeof(struct synrecord), 1, fp);
+				
+				if (in.read((char*) &r, sizeof(struct synrecord)) <= 0)
+					throw NetworkFileNotFoundException();
 
 				(*hidden)[i].synIn(j).setWeight(r.w);
 				(*hidden)[i].synIn(j).setDelta(r.d);
@@ -362,18 +399,22 @@ namespace neuralpp {
 
 		for (unsigned int i = 0; i < hidden->size(); i++) {
 			int nout;
-			fread(&nout, sizeof(int), 1, fp);
+			
+			if (in.read((char*) &nout, sizeof(int)) <= 0)
+				throw NetworkFileNotFoundException();
 
 			for (int j = 0; j < nout; j++) {
 				struct synrecord r;
-				fread(&r, sizeof(struct synrecord), 1, fp);
+				
+				if (in.read((char*) &r, sizeof(struct synrecord)) <= 0)
+					throw NetworkFileNotFoundException();
 
 				(*hidden)[i].synOut(j).setWeight(r.w);
 				(*hidden)[i].synOut(j).setDelta(r.d);
 			}
 		}
 
-		fclose(fp);
+		in.close();
 	}
 
 	void NeuralNet::train(string xmlsrc, NeuralNet::source src =
@@ -438,7 +479,7 @@ namespace neuralpp {
 		return;
 	}
 
-	void NeuralNet::initXML(string & 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"

src/synapsis.cpp

@@ -72,14 +72,17 @@ namespace neuralpp {
 		return prev_delta;
 	}
 
-	void Synapsis::setWeight(double w) {
+	void Synapsis::setWeight(double w) throw(InvalidSynapticalWeightException)  {
 		if (weight > 1.0)
-			weight = 1.0;
-		else
-			weight = w;
+			throw InvalidSynapticalWeightException();
+
+		weight = w;
 	}
 
-	void Synapsis::setDelta(double d) {
+	void Synapsis::setDelta(double d) throw(InvalidSynapticalWeightException)  {
+		if (d > 1.0)
+			throw InvalidSynapticalWeightException();
+
 		prev_delta = delta;
 		delta = d;
 	}