public mlpcvreport(mlptrain.mlpcvreport obj) { _innerobj = obj; }
/************************************************************************* Internal bagging subroutine. -- ALGLIB -- Copyright 19.02.2009 by Bochkanov Sergey *************************************************************************/ private static void mlpebagginginternal(mlpensemble ensemble, double[,] xy, int npoints, double decay, int restarts, double wstep, int maxits, bool lmalgorithm, ref int info, mlptrain.mlpreport rep, mlptrain.mlpcvreport ooberrors) { double[,] xys = new double[0,0]; bool[] s = new bool[0]; double[,] oobbuf = new double[0,0]; int[] oobcntbuf = new int[0]; double[] x = new double[0]; double[] y = new double[0]; double[] dy = new double[0]; double[] dsbuf = new double[0]; int ccnt = 0; int pcnt = 0; int i = 0; int j = 0; int k = 0; double v = 0; mlptrain.mlpreport tmprep = new mlptrain.mlpreport(); int nin = 0; int nout = 0; int wcount = 0; int i_ = 0; int i1_ = 0; info = 0; nin = mlpbase.mlpgetinputscount(ensemble.network); nout = mlpbase.mlpgetoutputscount(ensemble.network); wcount = mlpbase.mlpgetweightscount(ensemble.network); // // Test for inputs // if( (!lmalgorithm && (double)(wstep)==(double)(0)) && maxits==0 ) { info = -8; return; } if( ((npoints<=0 || restarts<1) || (double)(wstep)<(double)(0)) || maxits<0 ) { info = -1; return; } if( mlpbase.mlpissoftmax(ensemble.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; } } } // // allocate temporaries // info = 2; rep.ngrad = 0; rep.nhess = 0; rep.ncholesky = 0; ooberrors.relclserror = 0; ooberrors.avgce = 0; ooberrors.rmserror = 0; ooberrors.avgerror = 0; ooberrors.avgrelerror = 0; if( mlpbase.mlpissoftmax(ensemble.network) ) { ccnt = nin+1; pcnt = nin; } else { ccnt = nin+nout; pcnt = nin+nout; } xys = new double[npoints, ccnt]; s = new bool[npoints]; oobbuf = new double[npoints, nout]; oobcntbuf = new int[npoints]; x = new double[nin]; y = new double[nout]; if( mlpbase.mlpissoftmax(ensemble.network) ) { dy = new double[1]; } else { dy = new double[nout]; } for(i=0; i<=npoints-1; i++) { for(j=0; j<=nout-1; j++) { oobbuf[i,j] = 0; } } for(i=0; i<=npoints-1; i++) { oobcntbuf[i] = 0; } // // main bagging cycle // for(k=0; k<=ensemble.ensemblesize-1; k++) { // // prepare dataset // for(i=0; i<=npoints-1; i++) { s[i] = false; } for(i=0; i<=npoints-1; i++) { j = math.randominteger(npoints); s[j] = true; for(i_=0; i_<=ccnt-1;i_++) { xys[i,i_] = xy[j,i_]; } } // // train // if( lmalgorithm ) { mlptrain.mlptrainlm(ensemble.network, xys, npoints, decay, restarts, ref info, tmprep); } else { mlptrain.mlptrainlbfgs(ensemble.network, xys, npoints, decay, restarts, wstep, maxits, ref info, tmprep); } if( info<0 ) { return; } // // save results // rep.ngrad = rep.ngrad+tmprep.ngrad; rep.nhess = rep.nhess+tmprep.nhess; rep.ncholesky = rep.ncholesky+tmprep.ncholesky; i1_ = (0) - (k*wcount); for(i_=k*wcount; i_<=(k+1)*wcount-1;i_++) { ensemble.weights[i_] = ensemble.network.weights[i_+i1_]; } i1_ = (0) - (k*pcnt); for(i_=k*pcnt; i_<=(k+1)*pcnt-1;i_++) { ensemble.columnmeans[i_] = ensemble.network.columnmeans[i_+i1_]; } i1_ = (0) - (k*pcnt); for(i_=k*pcnt; i_<=(k+1)*pcnt-1;i_++) { ensemble.columnsigmas[i_] = ensemble.network.columnsigmas[i_+i1_]; } // // OOB estimates // for(i=0; i<=npoints-1; i++) { if( !s[i] ) { for(i_=0; i_<=nin-1;i_++) { x[i_] = xy[i,i_]; } mlpbase.mlpprocess(ensemble.network, x, ref y); for(i_=0; i_<=nout-1;i_++) { oobbuf[i,i_] = oobbuf[i,i_] + y[i_]; } oobcntbuf[i] = oobcntbuf[i]+1; } } } // // OOB estimates // if( mlpbase.mlpissoftmax(ensemble.network) ) { bdss.dserrallocate(nout, ref dsbuf); } else { bdss.dserrallocate(-nout, ref dsbuf); } for(i=0; i<=npoints-1; i++) { if( oobcntbuf[i]!=0 ) { v = (double)1/(double)oobcntbuf[i]; for(i_=0; i_<=nout-1;i_++) { y[i_] = v*oobbuf[i,i_]; } if( mlpbase.mlpissoftmax(ensemble.network) ) { dy[0] = xy[i,nin]; } else { i1_ = (nin) - (0); for(i_=0; i_<=nout-1;i_++) { dy[i_] = v*xy[i,i_+i1_]; } } bdss.dserraccumulate(ref dsbuf, y, dy); } } bdss.dserrfinish(ref dsbuf); ooberrors.relclserror = dsbuf[0]; ooberrors.avgce = dsbuf[1]; ooberrors.rmserror = dsbuf[2]; ooberrors.avgerror = dsbuf[3]; ooberrors.avgrelerror = dsbuf[4]; }
/************************************************************************* Training neural networks ensemble using early stopping. INPUT PARAMETERS: Ensemble - model with initialized geometry XY - training set NPoints - training set size Decay - weight decay coefficient, >=0.001 Restarts - restarts, >0. OUTPUT PARAMETERS: Ensemble - trained model Info - return code: * -2, if there is a point with class number outside of [0..NClasses-1]. * -1, if incorrect parameters was passed (NPoints<0, Restarts<1). * 6, if task has been solved. Rep - training report. OOBErrors - out-of-bag generalization error estimate -- ALGLIB -- Copyright 10.03.2009 by Bochkanov Sergey *************************************************************************/ public static void mlpetraines(mlpensemble ensemble, double[,] xy, int npoints, double decay, int restarts, ref int info, mlptrain.mlpreport rep) { int i = 0; int k = 0; int ccount = 0; int pcount = 0; double[,] trnxy = new double[0,0]; double[,] valxy = new double[0,0]; int trnsize = 0; int valsize = 0; int tmpinfo = 0; mlptrain.mlpreport tmprep = new mlptrain.mlpreport(); int nin = 0; int nout = 0; int wcount = 0; int i_ = 0; int i1_ = 0; info = 0; nin = mlpbase.mlpgetinputscount(ensemble.network); nout = mlpbase.mlpgetoutputscount(ensemble.network); wcount = mlpbase.mlpgetweightscount(ensemble.network); if( (npoints<2 || restarts<1) || (double)(decay)<(double)(0) ) { info = -1; return; } if( mlpbase.mlpissoftmax(ensemble.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; } } } info = 6; // // allocate // if( mlpbase.mlpissoftmax(ensemble.network) ) { ccount = nin+1; pcount = nin; } else { ccount = nin+nout; pcount = nin+nout; } trnxy = new double[npoints, ccount]; valxy = new double[npoints, ccount]; rep.ngrad = 0; rep.nhess = 0; rep.ncholesky = 0; // // train networks // for(k=0; k<=ensemble.ensemblesize-1; k++) { // // Split set // do { trnsize = 0; valsize = 0; for(i=0; i<=npoints-1; i++) { if( (double)(math.randomreal())<(double)(0.66) ) { // // Assign sample to training set // for(i_=0; i_<=ccount-1;i_++) { trnxy[trnsize,i_] = xy[i,i_]; } trnsize = trnsize+1; } else { // // Assign sample to validation set // for(i_=0; i_<=ccount-1;i_++) { valxy[valsize,i_] = xy[i,i_]; } valsize = valsize+1; } } } while( !(trnsize!=0 && valsize!=0) ); // // Train // mlptrain.mlptraines(ensemble.network, trnxy, trnsize, valxy, valsize, decay, restarts, ref tmpinfo, tmprep); if( tmpinfo<0 ) { info = tmpinfo; return; } // // save results // i1_ = (0) - (k*wcount); for(i_=k*wcount; i_<=(k+1)*wcount-1;i_++) { ensemble.weights[i_] = ensemble.network.weights[i_+i1_]; } i1_ = (0) - (k*pcount); for(i_=k*pcount; i_<=(k+1)*pcount-1;i_++) { ensemble.columnmeans[i_] = ensemble.network.columnmeans[i_+i1_]; } i1_ = (0) - (k*pcount); for(i_=k*pcount; i_<=(k+1)*pcount-1;i_++) { ensemble.columnsigmas[i_] = ensemble.network.columnsigmas[i_+i1_]; } rep.ngrad = rep.ngrad+tmprep.ngrad; rep.nhess = rep.nhess+tmprep.nhess; rep.ncholesky = rep.ncholesky+tmprep.ncholesky; } }
/************************************************************************* Training neural networks ensemble using bootstrap aggregating (bagging). L-BFGS algorithm is used as base training method. INPUT PARAMETERS: Ensemble - model with initialized geometry XY - training set NPoints - training set size Decay - weight decay coefficient, >=0.001 Restarts - restarts, >0. WStep - stopping criterion, same as in MLPTrainLBFGS MaxIts - stopping criterion, same as in MLPTrainLBFGS OUTPUT PARAMETERS: Ensemble - trained model Info - return code: * -8, if both WStep=0 and MaxIts=0 * -2, if there is a point with class number outside of [0..NClasses-1]. * -1, if incorrect parameters was passed (NPoints<0, Restarts<1). * 2, if task has been solved. Rep - training report. OOBErrors - out-of-bag generalization error estimate -- ALGLIB -- Copyright 17.02.2009 by Bochkanov Sergey *************************************************************************/ public static void mlpebagginglbfgs(mlpensemble ensemble, double[,] xy, int npoints, double decay, int restarts, double wstep, int maxits, ref int info, mlptrain.mlpreport rep, mlptrain.mlpcvreport ooberrors) { info = 0; mlpebagginginternal(ensemble, xy, npoints, decay, restarts, wstep, maxits, false, ref info, rep, ooberrors); }
/************************************************************************* Training neural networks ensemble using bootstrap aggregating (bagging). Modified Levenberg-Marquardt algorithm is used as base training method. INPUT PARAMETERS: Ensemble - model with initialized geometry XY - training set NPoints - training set size Decay - weight decay coefficient, >=0.001 Restarts - restarts, >0. OUTPUT PARAMETERS: Ensemble - trained model Info - return code: * -2, if there is a point with class number outside of [0..NClasses-1]. * -1, if incorrect parameters was passed (NPoints<0, Restarts<1). * 2, if task has been solved. Rep - training report. OOBErrors - out-of-bag generalization error estimate -- ALGLIB -- Copyright 17.02.2009 by Bochkanov Sergey *************************************************************************/ public static void mlpebagginglm(mlpensemble ensemble, double[,] xy, int npoints, double decay, int restarts, ref int info, mlptrain.mlpreport rep, mlptrain.mlpcvreport ooberrors) { info = 0; mlpebagginginternal(ensemble, xy, npoints, decay, restarts, 0.0, 0, true, ref info, rep, ooberrors); }
public mlptrainer(mlptrain.mlptrainer obj) { _innerobj = obj; }