JOONE(Java Object-Oriented Network Engine)简单实例源代码

 /*    * JOONE - Java Object Oriented Neural Engine   *  http://joone.sourceforge.net    *   * XOR_using_NeuralNet.java   *    */    package  study;      import  org.joone.engine. * ;   import  org.joone.engine.learning. * ;   import  org.joone.io. * ;   import  org.joone.net. * ;   import  java.util.Vector;      public class XOR_using_NeuralNet implements  NeuralNetListener  {       private  NeuralNet            nnet  =   null ;       private  MemoryInputSynapse  inputSynapse, desiredOutputSynapse;       private  MemoryOutputSynapse outputSynapse;       LinearLayer    input;       SigmoidLayer hidden, output;       boolean  singleThreadMode  =   true ;             //  XOR input         private   double [][]            inputArray  =   new   double [][]  {            { 0.0 ,  0.0 } ,            { 0.0 ,  1.0 } ,            { 1.0 ,  0.0 } ,            { 1.0 ,  1.0 }       } ;             //  XOR desired output         private   double [][]            desiredOutputArray  =   new   double [][]  {            { 0.0 } ,            { 1.0 } ,            { 1.0 } ,            { 0.0 }       } ;              /**        *  @param  args the command line arguments        */         public   static   void  main(String args[])  {          XOR_using_NeuralNet xor  =   new  XOR_using_NeuralNet();                    xor.initNeuralNet();          xor.train();          xor.interrogate();      }               /**        * Method declaration        */         public   void  train()  {                     //  set the inputs            inputSynapse.setInputArray(inputArray);          inputSynapse.setAdvancedColumnSelector( " 1,2 " );           //  set the desired outputs            desiredOutputSynapse.setInputArray(desiredOutputArray);          desiredOutputSynapse.setAdvancedColumnSelector( " 1 " );                     //  get the monitor object to train or feed forward            Monitor monitor  =  nnet.getMonitor();                     //  set the monitor parameters            monitor.setLearningRate( 0.8 );          monitor.setMomentum( 0.3 );          monitor.setTrainingPatterns(inputArray.length);          monitor.setTotCicles( 5000 );          monitor.setLearning( true );                     long  initms  =  System.currentTimeMillis();           //  Run the network in single-thread, synchronized mode            nnet.getMonitor().setSingleThreadMode(singleThreadMode);          nnet.go( true );          System.out.println( " Total time=  " + (System.currentTimeMillis()  -  initms) + "  ms " );      }               private   void  interrogate()  {           //  set the inputs            inputSynapse.setInputArray(inputArray);          inputSynapse.setAdvancedColumnSelector( " 1,2 " );          Monitor monitor = nnet.getMonitor();          monitor.setTrainingPatterns( 4 );          monitor.setTotCicles( 1 );          monitor.setLearning( false );          MemoryOutputSynapse memOut  =   new  MemoryOutputSynapse();           //  set the output synapse to write the output of the net                        if (nnet != null )  {              nnet.addOutputSynapse(memOut);              System.out.println(nnet.check());              nnet.getMonitor().setSingleThreadMode(singleThreadMode);              nnet.go();                              for ( int  i = 0 ; i < 4 ; i ++ )  {                   double [] pattern  =  memOut.getNextPattern();                  System.out.println( " Output pattern # "   +  (i + 1 )  +   " = "   +  pattern[ 0 ]);              }               System.out.println( " Interrogating Finished " );          }       }               /**        * Method declaration        */         protected   void  initNeuralNet()  {                     //  First create the three layers            input  =   new  LinearLayer();          hidden  =   new  SigmoidLayer();          output  =   new  SigmoidLayer();                     //  set the dimensions of the layers            input.setRows( 2 );          hidden.setRows( 3 );          output.setRows( 1 );                    input.setLayerName( " L.input " );          hidden.setLayerName( " L.hidden " );          output.setLayerName( " L.output " );                     //  Now create the two Synapses            FullSynapse synapse_IH  =   new  FullSynapse();     /*  input -> hidden conn.  */            FullSynapse synapse_HO  =   new  FullSynapse();     /*  hidden -> output conn.  */                      //  Connect the input layer whit the hidden layer            input.addOutputSynapse(synapse_IH);          hidden.addInputSynapse(synapse_IH);                     //  Connect the hidden layer whit the output layer            hidden.addOutputSynapse(synapse_HO);          output.addInputSynapse(synapse_HO);                     //  the input to the neural net            inputSynapse  =   new  MemoryInputSynapse();                    input.addInputSynapse(inputSynapse);                     //  The Trainer and its desired output            desiredOutputSynapse  =   new  MemoryInputSynapse();                    TeachingSynapse trainer  =   new  TeachingSynapse();                    trainer.setDesired(desiredOutputSynapse);                     //  Now we add this structure to a NeuralNet object            nnet  =   new  NeuralNet();                    nnet.addLayer(input, NeuralNet.INPUT_LAYER);          nnet.addLayer(hidden, NeuralNet.HIDDEN_LAYER);          nnet.addLayer(output, NeuralNet.OUTPUT_LAYER);          nnet.setTeacher(trainer);          output.addOutputSynapse(trainer);          nnet.addNeuralNetListener( this );      }               public   void  cicleTerminated(NeuralNetEvent e)  {      }               public   void  errorChanged(NeuralNetEvent e)  {          Monitor mon  =  (Monitor)e.getSource();           if  (mon.getCurrentCicle()  %   100   ==   0 )              System.out.println( " Epoch:  " + (mon.getTotCicles() - mon.getCurrentCicle()) + "  RMSE: " + mon.getGlobalError());      }               public   void  netStarted(NeuralNetEvent e)  {          Monitor mon  =  (Monitor)e.getSource();          System.out.print( " Network started for  " );           if  (mon.isLearning())              System.out.println( " training. " );           else               System.out.println( " interrogation. " );      }               public   void  netStopped(NeuralNetEvent e)  {          Monitor mon  =  (Monitor)e.getSource();          System.out.println( " Network stopped. Last RMSE= " + mon.getGlobalError());      }               public   void  netStoppedError(NeuralNetEvent e, String error)  {          System.out.println( " Network stopped due the following error:  " + error);      }         }