C# mlpbase类代码示例

        /*************************************************************************
        This function trains neural network passed to this function, using current
        dataset (one which was passed to MLPSetDataset() or MLPSetSparseDataset())
        and current training settings. Training  from  NRestarts  random  starting
        positions is performed, best network is chosen.

        Training is performed using current training algorithm.

        INPUT PARAMETERS:
            S           -   trainer object;
            Network     -   neural network. It must have same number of inputs and
                            output/classes as was specified during creation of the
                            trainer object;
            TNetwork    -   the training neural network.
                            User  may  look  weights  in  parameter Network  while
                            continue training process.
                            It has architecture like Network. You have to  copy or 
                            create new network with architecture like Network.
            State       -   created LBFGS optimizer;
            NRestarts   -   number of restarts, >=0:
                            * NRestarts>0 means that specified  number  of  random
                              restarts are performed, best network is chosen after
                              training
                            * NRestarts=0 means that current state of the  network
                              is used for training.
            TrnSubset   -   some subset from training set(it stores row's numbers),
                            used as trainig set;
           TrnSubsetSize-   size of subset(if TrnSubsetSize<0 - used full dataset);
                            when TrnSubsetSize=0, network is filled by zero value,
                            and ValSubset parameter is IGNORED;
            ValSubset   -   some subset from training set(it stores row's numbers),
                            used as validation set;
           ValSubsetSize-   size of subset(if ValSubsetSize<0 - used full dataset);
                            when  ValSubsetSize<>0  this  mean  that is used early
                            stopping training algorithm;
            BufWBest    -   buffer for storing interim resuls (BufWBest[0:WCOunt-1]
                            it has be allocated by user);
            BufWFinal   -   buffer for storing interim resuls(BufWFinal[0:WCOunt-1]
                            it has be allocated by user).

        OUTPUT PARAMETERS:
            Network     -   trained network;
            Rep         -   training report.

        NOTE: when no dataset was specified with MLPSetDataset/SetSparseDataset(),
              network  is  filled  by zero  values.  Same  behavior  for functions
              MLPStartTraining and MLPContinueTraining.

        NOTE: this method uses sum-of-squares error function for training.

          -- ALGLIB --
             Copyright 13.08.2012 by Bochkanov Sergey
        *************************************************************************/
        private static void mlptrainnetworkx(mlptrainer s,
            mlpbase.multilayerperceptron network,
            mlpbase.multilayerperceptron tnetwork,
            minlbfgs.minlbfgsstate state,
            int nrestarts,
            int[] trnsubset,
            int trnsubsetsize,
            int[] valsubset,
            int valsubsetsize,
            double[] bufwbest,
            double[] bufwfinal,
            mlpreport rep)
        {
            mlpbase.modelerrors modrep = new mlpbase.modelerrors();
            double eval = 0;
            double v = 0;
            double ebestcur = 0;
            double efinal = 0;
            int ngradbatch = 0;
            int nin = 0;
            int nout = 0;
            int wcount = 0;
            int twcount = 0;
            int itbest = 0;
            int itcnt = 0;
            int ntype = 0;
            int ttype = 0;
            bool rndstart = new bool();
            int pass = 0;
            int i = 0;
            int i_ = 0;

            alglib.ap.assert(s.npoints>=0, "MLPTrainNetworkX: internal error - parameter S is not initialized or is spoiled(S.NPoints<0)");
            if( s.rcpar )
            {
                ttype = 0;
            }
            else
            {
                ttype = 1;
            }
            if( !mlpbase.mlpissoftmax(network) )
            {
                ntype = 0;
            }
            else
            {
//.........这里部分代码省略.........

        /*************************************************************************
        Cross-validation estimate of generalization error.

        Base algorithm - L-BFGS.

        INPUT PARAMETERS:
            Network     -   neural network with initialized geometry.   Network is
                            not changed during cross-validation -  it is used only
                            as a representative of its architecture.
            XY          -   training set.
            SSize       -   training set size
            Decay       -   weight  decay, same as in MLPTrainLBFGS
            Restarts    -   number of restarts, >0.
                            restarts are counted for each partition separately, so
                            total number of restarts will be Restarts*FoldsCount.
            WStep       -   stopping criterion, same as in MLPTrainLBFGS
            MaxIts      -   stopping criterion, same as in MLPTrainLBFGS
            FoldsCount  -   number of folds in k-fold cross-validation,
                            2<=FoldsCount<=SSize.
                            recommended value: 10.

        OUTPUT PARAMETERS:
            Info        -   return code, same as in MLPTrainLBFGS
            Rep         -   report, same as in MLPTrainLM/MLPTrainLBFGS
            CVRep       -   generalization error estimates

          -- ALGLIB --
             Copyright 09.12.2007 by Bochkanov Sergey
        *************************************************************************/
        public static void mlpkfoldcvlbfgs(mlpbase.multilayerperceptron network,
            double[,] xy,
            int npoints,
            double decay,
            int restarts,
            double wstep,
            int maxits,
            int foldscount,
            ref int info,
            mlpreport rep,
            mlpcvreport cvrep)
        {
            info = 0;

            mlpkfoldcvgeneral(network, xy, npoints, decay, restarts, foldscount, false, wstep, maxits, ref info, rep, cvrep);
        }

        /*************************************************************************
        Internal cross-validation subroutine
        *************************************************************************/
        private static void mlpkfoldcvgeneral(mlpbase.multilayerperceptron n,
            double[,] xy,
            int npoints,
            double decay,
            int restarts,
            int foldscount,
            bool lmalgorithm,
            double wstep,
            int maxits,
            ref int info,
            mlpreport rep,
            mlpcvreport cvrep)
        {
            int i = 0;
            int fold = 0;
            int j = 0;
            int k = 0;
            mlpbase.multilayerperceptron network = new mlpbase.multilayerperceptron();
            int nin = 0;
            int nout = 0;
            int rowlen = 0;
            int wcount = 0;
            int nclasses = 0;
            int tssize = 0;
            int cvssize = 0;
            double[,] cvset = new double[0,0];
            double[,] testset = new double[0,0];
            int[] folds = new int[0];
            int relcnt = 0;
            mlpreport internalrep = new mlpreport();
            double[] x = new double[0];
            double[] y = new double[0];
            int i_ = 0;

            info = 0;

            
            //
            // Read network geometry, test parameters
            //
            mlpbase.mlpproperties(n, ref nin, ref nout, ref wcount);
            if( mlpbase.mlpissoftmax(n) )
            {
                nclasses = nout;
                rowlen = nin+1;
            }
            else
            {
                nclasses = -nout;
                rowlen = nin+nout;
            }
            if( (npoints<=0 || foldscount<2) || foldscount>npoints )
            {
                info = -1;
                return;
            }
            mlpbase.mlpcopy(n, network);
            
            //
            // K-fold out cross-validation.
            // First, estimate generalization error
            //
            testset = new double[npoints-1+1, rowlen-1+1];
            cvset = new double[npoints-1+1, rowlen-1+1];
            x = new double[nin-1+1];
            y = new double[nout-1+1];
            mlpkfoldsplit(xy, npoints, nclasses, foldscount, false, ref folds);
            cvrep.relclserror = 0;
            cvrep.avgce = 0;
            cvrep.rmserror = 0;
            cvrep.avgerror = 0;
            cvrep.avgrelerror = 0;
            rep.ngrad = 0;
            rep.nhess = 0;
            rep.ncholesky = 0;
            relcnt = 0;
            for(fold=0; fold<=foldscount-1; fold++)
            {
                
                //
                // Separate set
                //
                tssize = 0;
                cvssize = 0;
                for(i=0; i<=npoints-1; i++)
                {
                    if( folds[i]==fold )
                    {
                        for(i_=0; i_<=rowlen-1;i_++)
                        {
                            testset[tssize,i_] = xy[i,i_];
                        }
                        tssize = tssize+1;
                    }
                    else
                    {
                        for(i_=0; i_<=rowlen-1;i_++)
//.........这里部分代码省略.........

 public multilayerperceptron(mlpbase.multilayerperceptron obj)
 {
     _innerobj = obj;
 }

        /*************************************************************************
        Neural  network  training  using  L-BFGS  algorithm  with  regularization.
        Subroutine  trains  neural  network  with  restarts from random positions.
        Algorithm  is  well  suited  for  problems  of  any dimensionality (memory
        requirements and step complexity are linear by weights number).

        INPUT PARAMETERS:
            Network     -   neural network with initialized geometry
            XY          -   training set
            NPoints     -   training set size
            Decay       -   weight decay constant, >=0.001
                            Decay term 'Decay*||Weights||^2' is added to error
                            function.
                            If you don't know what Decay to choose, use 0.001.
            Restarts    -   number of restarts from random position, >0.
                            If you don't know what Restarts to choose, use 2.
            WStep       -   stopping criterion. Algorithm stops if  step  size  is
                            less than WStep. Recommended value - 0.01.  Zero  step
                            size means stopping after MaxIts iterations.
            MaxIts      -   stopping   criterion.  Algorithm  stops  after  MaxIts
                            iterations (NOT gradient  calculations).  Zero  MaxIts
                            means stopping when step is sufficiently small.

        OUTPUT PARAMETERS:
            Network     -   trained neural network.
            Info        -   return code:
                            * -8, if both WStep=0 and MaxIts=0
                            * -2, if there is a point with class number
                                  outside of [0..NOut-1].
                            * -1, if wrong parameters specified
                                  (NPoints<0, Restarts<1).
                            *  2, if task has been solved.
            Rep         -   training report

          -- ALGLIB --
             Copyright 09.12.2007 by Bochkanov Sergey
        *************************************************************************/
        public static void mlptrainlbfgs(mlpbase.multilayerperceptron network,
            double[,] xy,
            int npoints,
            double decay,
            int restarts,
            double wstep,
            int maxits,
            ref int info,
            mlpreport rep)
        {
            int i = 0;
            int pass = 0;
            int nin = 0;
            int nout = 0;
            int wcount = 0;
            double[] w = new double[0];
            double[] wbest = new double[0];
            double e = 0;
            double v = 0;
            double ebest = 0;
            minlbfgs.minlbfgsreport internalrep = new minlbfgs.minlbfgsreport();
            minlbfgs.minlbfgsstate state = new minlbfgs.minlbfgsstate();
            int i_ = 0;

            info = 0;

            
            //
            // Test inputs, parse flags, read network geometry
            //
            if( (double)(wstep)==(double)(0) && maxits==0 )
            {
                info = -8;
                return;
            }
            if( ((npoints<=0 || restarts<1) || (double)(wstep)<(double)(0)) || maxits<0 )
            {
                info = -1;
                return;
            }
            mlpbase.mlpproperties(network, ref nin, ref nout, ref wcount);
            if( mlpbase.mlpissoftmax(network) )
            {
                for(i=0; i<=npoints-1; i++)
                {
                    if( (int)Math.Round(xy[i,nin])<0 || (int)Math.Round(xy[i,nin])>=nout )
                    {
                        info = -2;
                        return;
                    }
                }
            }
            decay = Math.Max(decay, mindecay);
            info = 2;
            
            //
            // Prepare
            //
            mlpbase.mlpinitpreprocessor(network, xy, npoints);
            w = new double[wcount-1+1];
            wbest = new double[wcount-1+1];
            ebest = math.maxrealnumber;
            
//.........这里部分代码省略.........

        /*************************************************************************
        This function initializes temporaries needed for training session.

        *************************************************************************/
        private static void initmlpetrnsessions(mlpbase.multilayerperceptron individualnetwork,
            mlptrainer trainer,
            alglib.smp.shared_pool sessions)
        {
            mlpetrnsession t = new mlpetrnsession();

            if( !alglib.smp.ae_shared_pool_is_initialized(sessions) )
            {
                initmlpetrnsession(individualnetwork, trainer, t);
                alglib.smp.ae_shared_pool_set_seed(sessions, t);
            }
        }

        /*************************************************************************
        Network creation

        This function creates network with desired structure. Network  is  created
        using one of the three methods:
        a) straighforward creation using MLPCreate???()
        b) MLPCreate???() for proxy object, which is copied with PassThroughSerializer()
        c) MLPCreate???() for proxy object, which is copied with MLPCopy()
        One of these methods is chosen with probability 1/3.
        *************************************************************************/
        private static void createnetwork(mlpbase.multilayerperceptron network,
            int nkind,
            double a1,
            double a2,
            int nin,
            int nhid1,
            int nhid2,
            int nout)
        {
            int mkind = 0;
            mlpbase.multilayerperceptron tmp = new mlpbase.multilayerperceptron();

            ap.assert(((nin>0 & nhid1>=0) & nhid2>=0) & nout>0, "CreateNetwork error");
            ap.assert(nhid1!=0 | nhid2==0, "CreateNetwork error");
            ap.assert(nkind!=1 | nout>=2, "CreateNetwork error");
            mkind = math.randominteger(3);
            if( nhid1==0 )
            {
                
                //
                // No hidden layers
                //
                if( nkind==0 )
                {
                    if( mkind==0 )
                    {
                        mlpbase.mlpcreate0(nin, nout, network);
                    }
                    if( mkind==1 )
                    {
                        mlpbase.mlpcreate0(nin, nout, tmp);
                        {
                            //
                            // This code passes data structure through serializers
                            // (serializes it to string and loads back)
                            //
                            serializer _local_serializer;
                            string _local_str;
                            
                            _local_serializer = new serializer();
                            _local_serializer.alloc_start();
                            mlpbase.mlpalloc(_local_serializer, tmp);
                            _local_serializer.sstart_str();
                            mlpbase.mlpserialize(_local_serializer, tmp);
                            _local_serializer.stop();
                            _local_str = _local_serializer.get_string();
                            
                            _local_serializer = new serializer();
                            _local_serializer.ustart_str(_local_str);
                            mlpbase.mlpunserialize(_local_serializer, network);
                            _local_serializer.stop();
                        }
                    }
                    if( mkind==2 )
                    {
                        mlpbase.mlpcreate0(nin, nout, tmp);
                        mlpbase.mlpcopy(tmp, network);
                    }
                }
                else
                {
                    if( nkind==1 )
                    {
                        if( mkind==0 )
                        {
                            mlpbase.mlpcreatec0(nin, nout, network);
                        }
                        if( mkind==1 )
                        {
                            mlpbase.mlpcreatec0(nin, nout, tmp);
                            {
                                //
                                // This code passes data structure through serializers
                                // (serializes it to string and loads back)
                                //
                                serializer _local_serializer;
                                string _local_str;
                                
                                _local_serializer = new serializer();
                                _local_serializer.alloc_start();
                                mlpbase.mlpalloc(_local_serializer, tmp);
                                _local_serializer.sstart_str();
                                mlpbase.mlpserialize(_local_serializer, tmp);
                                _local_serializer.stop();
                                _local_str = _local_serializer.get_string();
                                
                                _local_serializer = new serializer();
                                _local_serializer.ustart_str(_local_str);
                                mlpbase.mlpunserialize(_local_serializer, network);
                                _local_serializer.stop();
//.........这里部分代码省略.........

        /*************************************************************************
        This function estimates generalization error using cross-validation on the
        current dataset with current training settings.

        FOR USERS OF COMMERCIAL EDITION:

          ! Commercial version of ALGLIB includes two  important  improvements  of
          ! this function:
          ! * multicore support (C++ and C# computational cores)
          ! * SSE support (C++ computational core)
          !
          ! Second improvement gives constant  speedup (2-3X).  First  improvement
          ! gives  close-to-linear  speedup  on   multicore   systems.   Following
          ! operations can be executed in parallel:
          ! * FoldsCount cross-validation rounds (always)
          ! * NRestarts training sessions performed within each of
          !   cross-validation rounds (if NRestarts>1)
          ! * gradient calculation over large dataset (if dataset is large enough)
          !
          ! In order to use multicore features you have to:
          ! * use commercial version of ALGLIB
          ! * call  this  function  with  "smp_"  prefix,  which  indicates  that
          !   multicore code will be used (for multicore support)
          !
          ! In order to use SSE features you have to:
          ! * use commercial version of ALGLIB on Intel processors
          ! * use C++ computational core
          !
          ! This note is given for users of commercial edition; if  you  use  GPL
          ! edition, you still will be able to call smp-version of this function,
          ! but all computations will be done serially.
          !
          ! We recommend you to carefully read ALGLIB Reference  Manual,  section
          ! called 'SMP support', before using parallel version of this function.

        INPUT PARAMETERS:
            S           -   trainer object
            Network     -   neural network. It must have same number of inputs and
                            output/classes as was specified during creation of the
                            trainer object. Network is not changed  during  cross-
                            validation and is not trained - it  is  used  only  as
                            representative of its architecture. I.e., we  estimate
                            generalization properties of  ARCHITECTURE,  not  some
                            specific network.
            NRestarts   -   number of restarts, >=0:
                            * NRestarts>0  means  that  for  each cross-validation
                              round   specified  number   of  random  restarts  is
                              performed,  with  best  network  being  chosen after
                              training.
                            * NRestarts=0 is same as NRestarts=1
            FoldsCount  -   number of folds in k-fold cross-validation:
                            * 2<=FoldsCount<=size of dataset
                            * recommended value: 10.
                            * values larger than dataset size will be silently
                              truncated down to dataset size

        OUTPUT PARAMETERS:
            Rep         -   structure which contains cross-validation estimates:
                            * Rep.RelCLSError - fraction of misclassified cases.
                            * Rep.AvgCE - acerage cross-entropy
                            * Rep.RMSError - root-mean-square error
                            * Rep.AvgError - average error
                            * Rep.AvgRelError - average relative error
                            
        NOTE: when no dataset was specified with MLPSetDataset/SetSparseDataset(),
              or subset with only one point  was  given,  zeros  are  returned  as
              estimates.

        NOTE: this method performs FoldsCount cross-validation  rounds,  each  one
              with NRestarts random starts.  Thus,  FoldsCount*NRestarts  networks
              are trained in total.

        NOTE: Rep.RelCLSError/Rep.AvgCE are zero on regression problems.

        NOTE: on classification problems Rep.RMSError/Rep.AvgError/Rep.AvgRelError
              contain errors in prediction of posterior probabilities.
                
          -- ALGLIB --
             Copyright 23.07.2012 by Bochkanov Sergey
        *************************************************************************/
        public static void mlpkfoldcv(mlptrainer s,
            mlpbase.multilayerperceptron network,
            int nrestarts,
            int foldscount,
            mlpreport rep)
        {
            alglib.smp.shared_pool pooldatacv = new alglib.smp.shared_pool();
            mlpparallelizationcv datacv = new mlpparallelizationcv();
            mlpparallelizationcv sdatacv = null;
            double[,] cvy = new double[0,0];
            int[] folds = new int[0];
            double[] buf = new double[0];
            double[] dy = new double[0];
            int nin = 0;
            int nout = 0;
            int wcount = 0;
            int rowsize = 0;
            int ntype = 0;
            int ttype = 0;
            int i = 0;
//.........这里部分代码省略.........

 /*************************************************************************
 Single-threaded stub. HPC ALGLIB replaces it by multithreaded code.
 *************************************************************************/
 public static void _pexec_mlpkfoldcv(mlptrainer s,
     mlpbase.multilayerperceptron network,
     int nrestarts,
     int foldscount,
     mlpreport rep)
 {
     mlpkfoldcv(s,network,nrestarts,foldscount,rep);
 }

 public modelerrors(mlpbase.modelerrors obj)
 {
     _innerobj = obj;
 }

        /*************************************************************************
        Calculation of all types of errors

          -- ALGLIB --
             Copyright 17.02.2009 by Bochkanov Sergey
        *************************************************************************/
        public static void mlpeallerrorsx(mlpensemble ensemble,
            double[,] densexy,
            sparse.sparsematrix sparsexy,
            int datasetsize,
            int datasettype,
            int[] idx,
            int subset0,
            int subset1,
            int subsettype,
            alglib.smp.shared_pool buf,
            mlpbase.modelerrors rep)
        {
            int i = 0;
            int j = 0;
            int nin = 0;
            int nout = 0;
            bool iscls = new bool();
            int srcidx = 0;
            hpccores.mlpbuffers pbuf = null;
            mlpbase.modelerrors rep0 = new mlpbase.modelerrors();
            mlpbase.modelerrors rep1 = new mlpbase.modelerrors();
            int i_ = 0;
            int i1_ = 0;

            
            //
            // Get network information
            //
            nin = mlpbase.mlpgetinputscount(ensemble.network);
            nout = mlpbase.mlpgetoutputscount(ensemble.network);
            iscls = mlpbase.mlpissoftmax(ensemble.network);
            
            //
            // Retrieve buffer, prepare, process data, recycle buffer
            //
            alglib.smp.ae_shared_pool_retrieve(buf, ref pbuf);
            if( iscls )
            {
                bdss.dserrallocate(nout, ref pbuf.tmp0);
            }
            else
            {
                bdss.dserrallocate(-nout, ref pbuf.tmp0);
            }
            apserv.rvectorsetlengthatleast(ref pbuf.x, nin);
            apserv.rvectorsetlengthatleast(ref pbuf.y, nout);
            apserv.rvectorsetlengthatleast(ref pbuf.desiredy, nout);
            for(i=subset0; i<=subset1-1; i++)
            {
                srcidx = -1;
                if( subsettype==0 )
                {
                    srcidx = i;
                }
                if( subsettype==1 )
                {
                    srcidx = idx[i];
                }
                alglib.ap.assert(srcidx>=0, "MLPEAllErrorsX: internal error");
                if( datasettype==0 )
                {
                    for(i_=0; i_<=nin-1;i_++)
                    {
                        pbuf.x[i_] = densexy[srcidx,i_];
                    }
                }
                if( datasettype==1 )
                {
                    sparse.sparsegetrow(sparsexy, srcidx, ref pbuf.x);
                }
                mlpeprocess(ensemble, pbuf.x, ref pbuf.y);
                if( mlpbase.mlpissoftmax(ensemble.network) )
                {
                    if( datasettype==0 )
                    {
                        pbuf.desiredy[0] = densexy[srcidx,nin];
                    }
                    if( datasettype==1 )
                    {
                        pbuf.desiredy[0] = sparse.sparseget(sparsexy, srcidx, nin);
                    }
                }
                else
                {
                    if( datasettype==0 )
                    {
                        i1_ = (nin) - (0);
                        for(i_=0; i_<=nout-1;i_++)
                        {
                            pbuf.desiredy[i_] = densexy[srcidx,i_+i1_];
                        }
                    }
                    if( datasettype==1 )
                    {
//.........这里部分代码省略.........

 /*************************************************************************
 Network creation
 *************************************************************************/
 private static void createnetwork(mlpbase.multilayerperceptron network,
     int nkind,
     double a1,
     double a2,
     int nin,
     int nhid1,
     int nhid2,
     int nout)
 {
     ap.assert(((nin>0 & nhid1>=0) & nhid2>=0) & nout>0, "CreateNetwork error");
     ap.assert(nhid1!=0 | nhid2==0, "CreateNetwork error");
     ap.assert(nkind!=1 | nout>=2, "CreateNetwork error");
     if( nhid1==0 )
     {
         
         //
         // No hidden layers
         //
         if( nkind==0 )
         {
             mlpbase.mlpcreate0(nin, nout, network);
         }
         else
         {
             if( nkind==1 )
             {
                 mlpbase.mlpcreatec0(nin, nout, network);
             }
             else
             {
                 if( nkind==2 )
                 {
                     mlpbase.mlpcreateb0(nin, nout, a1, a2, network);
                 }
                 else
                 {
                     if( nkind==3 )
                     {
                         mlpbase.mlpcreater0(nin, nout, a1, a2, network);
                     }
                 }
             }
         }
         return;
     }
     if( nhid2==0 )
     {
         
         //
         // One hidden layer
         //
         if( nkind==0 )
         {
             mlpbase.mlpcreate1(nin, nhid1, nout, network);
         }
         else
         {
             if( nkind==1 )
             {
                 mlpbase.mlpcreatec1(nin, nhid1, nout, network);
             }
             else
             {
                 if( nkind==2 )
                 {
                     mlpbase.mlpcreateb1(nin, nhid1, nout, a1, a2, network);
                 }
                 else
                 {
                     if( nkind==3 )
                     {
                         mlpbase.mlpcreater1(nin, nhid1, nout, a1, a2, network);
                     }
                 }
             }
         }
         return;
     }
     
     //
     // Two hidden layers
     //
     if( nkind==0 )
     {
         mlpbase.mlpcreate2(nin, nhid1, nhid2, nout, network);
     }
     else
     {
         if( nkind==1 )
         {
             mlpbase.mlpcreatec2(nin, nhid1, nhid2, nout, network);
         }
         else
         {
             if( nkind==2 )
             {
                 mlpbase.mlpcreateb2(nin, nhid1, nhid2, nout, a1, a2, network);
//.........这里部分代码省略.........

        /*************************************************************************
        This function performs step-by-step training of the neural  network.  Here
        "step-by-step" means  that training starts  with  MLPStartTrainingX  call,
        and then user subsequently calls MLPContinueTrainingX  to perform one more
        iteration of the training.

        This  function  performs  one  more  iteration of the training and returns
        either True (training continues) or False (training stopped). In case True
        was returned, Network weights are updated according to the  current  state
        of the optimization progress. In case False was  returned,  no  additional
        updates is performed (previous update of  the  network weights moved us to
        the final point, and no additional updates is needed).

        EXAMPLE:
            >
            > [initialize network and trainer object]
            >
            > MLPStartTraining(Trainer, Network, True)
            > while MLPContinueTraining(Trainer, Network) do
            >     [visualize training progress]
            >

        INPUT PARAMETERS:
            S           -   trainer object
            Network     -   neural network which receives A  COPY  of  the  actual
                            network which is trained by the algorithm. After  each
                            training roung state of the network being  trained  is
                            copied to this variable.
                            It must have same number of inputs and  output/classes
                            as was specified during creation of the trainer object
                            and  it  must  have  exactly  same architecture as the
                            second network (TNetwork).
            TNetwork    -   neural network being trained.
            State       -   LBFGS  optimizer,  already  initialized,   number   of
                            dimensions  must  be equal to number of weights in the
                            networks.
            Subset      -   some subset from training set(it stores row's numbers);
            SubsetSize  -   size of subset(if SubsetSize<0 - used full dataset).
            NGradBatch  -   number  of calls  MLPGradBatch function.  Initial value
                            is zero;
            
        OUTPUT PARAMETERS:
            Network     -   weights of the neural network  are  rewritten  by  the
                            current approximation;
            NGradBatch  -   number  of calls  MLPGradBatch function after training.

        NOTE: this method uses sum-of-squares error function for training.

        NOTE: it is expected that trainer object settings are NOT  changed  during
              step-by-step training, i.e. no  one  changes  stopping  criteria  or
              training set during training. It is possible and there is no defense
              against  such  actions,  but  algorithm  behavior  in  such cases is
              undefined and can be unpredictable.
              
        NOTE: It  is  expected that Network is the same one which  was  passed  to
              MLPStartTraining() function.  However,  THIS  function  checks  only
              following:
              * that number of network inputs is consistent with trainer object
                settings
              * that number of network outputs/classes is consistent with  trainer
                object settings
              * that number of network weights is the same as number of weights in
                the network passed to MLPStartTraining() function
              Exception is thrown when these conditions are violated.
              
              It is also expected that you do not change state of the  network  on
              your own - the only party who has right to change network during its
              training is a trainer object. Any attempt to interfere with  trainer
              may lead to unpredictable results.
              

          -- ALGLIB --
             Copyright 13.08.2012 by Bochkanov Sergey
        *************************************************************************/
        private static bool mlpcontinuetrainingx(mlptrainer s,
            mlpbase.multilayerperceptron network,
            mlpbase.multilayerperceptron tnetwork,
            minlbfgs.minlbfgsstate state,
            int[] subset,
            int subsetsize,
            ref int ngradbatch)
        {
            bool result = new bool();
            int nin = 0;
            int nout = 0;
            int wcount = 0;
            int twcount = 0;
            int ntype = 0;
            int ttype = 0;
            double decay = 0;
            double v = 0;
            int i = 0;
            int i_ = 0;

            alglib.ap.assert(s.npoints>=0, "MLPContinueTrainingX: internal error - parameter S is not initialized or is spoiled(S.NPoints<0).");
            if( s.rcpar )
            {
                ttype = 0;
            }
            else
//.........这里部分代码省略.........

        /*************************************************************************
        This function performs step-by-step training of the neural  network.  Here
        "step-by-step" means that training  starts  with  MLPStartTrainingX  call,
        and then user subsequently calls MLPContinueTrainingX  to perform one more
        iteration of the training.

        After call to this function trainer object remembers network and  is ready
        to  train  it.  However,  no  training  is  performed  until first call to 
        MLPContinueTraining() function. Subsequent calls  to MLPContinueTraining()
        will advance traing progress one iteration further.

        EXAMPLE:
            >
            > ...initialize network and trainer object....
            >
            > MLPStartTraining(Trainer, Network, True)
            > while MLPContinueTraining(Trainer, Network) do
            >     ...visualize training progress...
            >

        INPUT PARAMETERS:
            S           -   trainer object;
            Network     -   neural network which receives A  COPY  of  the  actual
                            network which is trained by the algorithm. After  each
                            training roung state of the network being  trained  is
                            copied to this variable.
                            It must have same number of inputs and  output/classes
                            as was specified during creation of the trainer object
                            and  it  must  have  exactly  same architecture as the
                            second network (TNetwork).
            TNetwork    -   neural network being trained.
            State       -   LBFGS  optimizer,  already  initialized,   number   of
                            dimensions  must  be equal to number of weights in the
                            networks.
            RandomStart -   randomize network before training or not:
                            * True  means  that  network  is  randomized  and  its
                              initial state (one which was passed to  the  trainer
                              object) is lost;
                            * False  means  that  training  is  started  from  the
                              current state of the network.
            Subset      -   some subset from training set(it stores row's numbers);
            SubsetSize  -   size of subset(if SubsetSize<0 - used full dataset).
                            
        OUTPUT PARAMETERS:
            Network     -   neural network which is ready to training (weights are
                            initialized, preprocessor is initialized using current
                            training set)

        NOTE: this method uses sum-of-squares error function for training.

        NOTE: it is expected that trainer object settings are NOT  changed  during
              step-by-step training, i.e. no  one  changes  stopping  criteria  or
              training set during training. It is possible and there is no defense
              against  such  actions,  but  algorithm  behavior  in  such cases is
              undefined and can be unpredictable.

          -- ALGLIB --
             Copyright 13.08.2012 by Bochkanov Sergey
        *************************************************************************/
        private static void mlpstarttrainingx(mlptrainer s,
            mlpbase.multilayerperceptron network,
            mlpbase.multilayerperceptron tnetwork,
            minlbfgs.minlbfgsstate state,
            bool randomstart,
            int[] subset,
            int subsetsize)
        {
            int nin = 0;
            int nout = 0;
            int wcount = 0;
            int twcount = 0;
            int ntype = 0;
            int ttype = 0;
            int i = 0;
            int i_ = 0;

            alglib.ap.assert(s.npoints>=0, "MLPStartTrainingX: internal error - parameter S is not initialized or is spoiled(S.NPoints<0)");
            if( s.rcpar )
            {
                ttype = 0;
            }
            else
            {
                ttype = 1;
            }
            if( !mlpbase.mlpissoftmax(network) )
            {
                ntype = 0;
            }
            else
            {
                ntype = 1;
            }
            alglib.ap.assert(ntype==ttype, "MLPStartTrainingX: internal error - type of the resulting network is not similar to network type in trainer object");
            if( !mlpbase.mlpissoftmax(tnetwork) )
            {
                ntype = 0;
            }
            else
            {
//.........这里部分代码省略.........

        /*************************************************************************
        This function initializes temporaries needed for training session.

        *************************************************************************/
        private static void initmlptrnsessions(mlpbase.multilayerperceptron networktrained,
            bool randomizenetwork,
            mlptrainer trainer,
            alglib.smp.shared_pool sessions)
        {
            int[] dummysubset = new int[0];
            smlptrnsession t = new smlptrnsession();
            smlptrnsession p = null;

            if( alglib.smp.ae_shared_pool_is_initialized(sessions) )
            {
                
                //
                // Pool was already initialized.
                // Clear sessions stored in the pool.
                //
                alglib.smp.ae_shared_pool_first_recycled(sessions, ref p);
                while( p!=null )
                {
                    alglib.ap.assert(mlpbase.mlpsamearchitecture(p.network, networktrained), "InitMLPTrnSessions: internal consistency error");
                    p.bestrmserror = math.maxrealnumber;
                    alglib.smp.ae_shared_pool_next_recycled(sessions, ref p);
                }
            }
            else
            {
                
                //
                // Prepare session and seed pool
                //
                initmlptrnsession(networktrained, randomizenetwork, trainer, t);
                alglib.smp.ae_shared_pool_set_seed(sessions, t);
            }
        }