public static bool testmlpe(bool silent)
{
bool result = new bool();
bool waserrors = new bool();
int passcount = 0;
int maxn = 0;
int maxhid = 0;
int nf = 0;
int nhid = 0;
int nl = 0;
int nhid1 = 0;
int nhid2 = 0;
int ec = 0;
int nkind = 0;
int algtype = 0;
int tasktype = 0;
int pass = 0;
mlpe.mlpensemble ensemble = new mlpe.mlpensemble();
mlptrain.mlpreport rep = new mlptrain.mlpreport();
mlptrain.mlpcvreport oobrep = new mlptrain.mlpcvreport();
double[,] xy = new double[0,0];
int i = 0;
int j = 0;
int nin = 0;
int nout = 0;
int npoints = 0;
double e = 0;
int info = 0;
int nless = 0;
int nall = 0;
int nclasses = 0;
bool allsame = new bool();
bool inferrors = new bool();
bool procerrors = new bool();
bool trnerrors = new bool();
waserrors = false;
inferrors = false;
procerrors = false;
trnerrors = false;
passcount = 10;
maxn = 4;
maxhid = 4;
//
// General MLP ensembles tests
//
for(nf=1; nf<=maxn; nf++)
{
for(nl=1; nl<=maxn; nl++)
{
for(nhid1=0; nhid1<=maxhid; nhid1++)
{
for(nhid2=0; nhid2<=0; nhid2++)
{
for(nkind=0; nkind<=3; nkind++)
{
for(ec=1; ec<=3; ec++)
{
//
// Skip meaningless parameters combinations
//
if( nkind==1 & nl<2 )
{
continue;
}
if( nhid1==0 & nhid2!=0 )
{
continue;
}
//
// Tests
//
testinformational(nkind, nf, nhid1, nhid2, nl, ec, passcount, ref inferrors);
testprocessing(nkind, nf, nhid1, nhid2, nl, ec, passcount, ref procerrors);
}
}
}
}
}
}
//
// network training must reduce error
// test on random regression task
//
nin = 3;
nout = 2;
nhid = 5;
npoints = 100;
nless = 0;
nall = 0;
for(pass=1; pass<=10; pass++)
{
for(algtype=0; algtype<=1; algtype++)
{
for(tasktype=0; tasktype<=1; tasktype++)
{
if( tasktype==0 )
{
xy = new double[npoints-1+1, nin+nout-1+1];
for(i=0; i<=npoints-1; i++)
{
for(j=0; j<=nin+nout-1; j++)
{
xy[i,j] = 2*AP.Math.RandomReal()-1;
}
}
mlpe.mlpecreate1(nin, nhid, nout, 1+AP.Math.RandomInteger(3), ref ensemble);
}
else
{
xy = new double[npoints-1+1, nin+1];
nclasses = 2+AP.Math.RandomInteger(2);
for(i=0; i<=npoints-1; i++)
{
for(j=0; j<=nin-1; j++)
{
xy[i,j] = 2*AP.Math.RandomReal()-1;
}
xy[i,nin] = AP.Math.RandomInteger(nclasses);
}
mlpe.mlpecreatec1(nin, nhid, nclasses, 1+AP.Math.RandomInteger(3), ref ensemble);
}
e = mlpe.mlpermserror(ref ensemble, ref xy, npoints);
if( algtype==0 )
{
mlpe.mlpebagginglm(ref ensemble, ref xy, npoints, 0.001, 1, ref info, ref rep, ref oobrep);
}
else
{
mlpe.mlpebagginglbfgs(ref ensemble, ref xy, npoints, 0.001, 1, 0.01, 0, ref info, ref rep, ref oobrep);
}
if( info<0 )
{
trnerrors = true;
}
else
{
if( (double)(mlpe.mlpermserror(ref ensemble, ref xy, npoints))<(double)(e) )
{
nless = nless+1;
}
}
nall = nall+1;
}
}
}
trnerrors = trnerrors | (double)(nall-nless)>(double)(0.3*nall);
//
// Final report
//
waserrors = inferrors | procerrors | trnerrors;
if( !silent )
{
System.Console.Write("MLP ENSEMBLE TEST");
System.Console.WriteLine();
System.Console.Write("INFORMATIONAL FUNCTIONS: ");
if( !inferrors )
{
System.Console.Write("OK");
System.Console.WriteLine();
}
else
{
System.Console.Write("FAILED");
System.Console.WriteLine();
}
System.Console.Write("BASIC PROCESSING: ");
if( !procerrors )
{
System.Console.Write("OK");
System.Console.WriteLine();
}
else
{
System.Console.Write("FAILED");
System.Console.WriteLine();
}
System.Console.Write("TRAINING: ");
if( !trnerrors )
{
System.Console.Write("OK");
System.Console.WriteLine();
}
else
{
System.Console.Write("FAILED");
System.Console.WriteLine();
}
if( waserrors )
{
System.Console.Write("TEST SUMMARY: FAILED");
System.Console.WriteLine();
}
else
{
System.Console.Write("TEST SUMMARY: PASSED");
System.Console.WriteLine();
}
System.Console.WriteLine();
System.Console.WriteLine();
}
result = !waserrors;
return result;
}
public static bool testmlpe(bool silent)
{
bool result = new bool();
bool waserrors = new bool();
int passcount = 0;
int maxn = 0;
int maxhid = 0;
int nf = 0;
int nhid = 0;
int nl = 0;
int nhid1 = 0;
int nhid2 = 0;
int ec = 0;
int nkind = 0;
int algtype = 0;
int tasktype = 0;
int pass = 0;
mlpe.mlpensemble ensemble = new mlpe.mlpensemble();
mlptrain.mlpreport rep = new mlptrain.mlpreport();
mlptrain.mlpcvreport oobrep = new mlptrain.mlpcvreport();
double[,] xy = new double[0, 0];
int i = 0;
int j = 0;
int nin = 0;
int nout = 0;
int npoints = 0;
double e = 0;
int info = 0;
int nless = 0;
int nall = 0;
int nclasses = 0;
bool allsame = new bool();
bool inferrors = new bool();
bool procerrors = new bool();
bool trnerrors = new bool();
waserrors = false;
inferrors = false;
procerrors = false;
trnerrors = false;
passcount = 10;
maxn = 4;
maxhid = 4;
//
// General MLP ensembles tests
//
for (nf = 1; nf <= maxn; nf++)
{
for (nl = 1; nl <= maxn; nl++)
{
for (nhid1 = 0; nhid1 <= maxhid; nhid1++)
{
for (nhid2 = 0; nhid2 <= 0; nhid2++)
{
for (nkind = 0; nkind <= 3; nkind++)
{
for (ec = 1; ec <= 3; ec++)
{
//
// Skip meaningless parameters combinations
//
if (nkind == 1 & nl < 2)
{
continue;
}
if (nhid1 == 0 & nhid2 != 0)
{
continue;
}
//
// Tests
//
testinformational(nkind, nf, nhid1, nhid2, nl, ec, passcount, ref inferrors);
testprocessing(nkind, nf, nhid1, nhid2, nl, ec, passcount, ref procerrors);
}
}
}
}
}
}
//
// network training must reduce error
// test on random regression task
//
nin = 3;
nout = 2;
nhid = 5;
npoints = 100;
nless = 0;
nall = 0;
for (pass = 1; pass <= 10; pass++)
{
for (algtype = 0; algtype <= 1; algtype++)
{
for (tasktype = 0; tasktype <= 1; tasktype++)
{
if (tasktype == 0)
{
xy = new double[npoints - 1 + 1, nin + nout - 1 + 1];
for (i = 0; i <= npoints - 1; i++)
{
for (j = 0; j <= nin + nout - 1; j++)
{
xy[i, j] = 2 * AP.Math.RandomReal() - 1;
}
}
mlpe.mlpecreate1(nin, nhid, nout, 1 + AP.Math.RandomInteger(3), ref ensemble);
}
else
{
xy = new double[npoints - 1 + 1, nin + 1];
nclasses = 2 + AP.Math.RandomInteger(2);
for (i = 0; i <= npoints - 1; i++)
{
for (j = 0; j <= nin - 1; j++)
{
xy[i, j] = 2 * AP.Math.RandomReal() - 1;
}
xy[i, nin] = AP.Math.RandomInteger(nclasses);
}
mlpe.mlpecreatec1(nin, nhid, nclasses, 1 + AP.Math.RandomInteger(3), ref ensemble);
}
e = mlpe.mlpermserror(ref ensemble, ref xy, npoints);
if (algtype == 0)
{
mlpe.mlpebagginglm(ref ensemble, ref xy, npoints, 0.001, 1, ref info, ref rep, ref oobrep);
}
else
{
mlpe.mlpebagginglbfgs(ref ensemble, ref xy, npoints, 0.001, 1, 0.01, 0, ref info, ref rep, ref oobrep);
}
if (info < 0)
{
trnerrors = true;
}
else
{
if ((double)(mlpe.mlpermserror(ref ensemble, ref xy, npoints)) < (double)(e))
{
nless = nless + 1;
}
}
nall = nall + 1;
}
}
}
trnerrors = trnerrors | (double)(nall - nless) > (double)(0.3 * nall);
//
// Final report
//
waserrors = inferrors | procerrors | trnerrors;
if (!silent)
{
System.Console.Write("MLP ENSEMBLE TEST");
System.Console.WriteLine();
System.Console.Write("INFORMATIONAL FUNCTIONS: ");
if (!inferrors)
{
System.Console.Write("OK");
System.Console.WriteLine();
}
else
{
System.Console.Write("FAILED");
System.Console.WriteLine();
}
System.Console.Write("BASIC PROCESSING: ");
if (!procerrors)
{
System.Console.Write("OK");
System.Console.WriteLine();
}
else
{
System.Console.Write("FAILED");
System.Console.WriteLine();
}
System.Console.Write("TRAINING: ");
if (!trnerrors)
{
System.Console.Write("OK");
System.Console.WriteLine();
}
else
{
System.Console.Write("FAILED");
System.Console.WriteLine();
}
if (waserrors)
{
System.Console.Write("TEST SUMMARY: FAILED");
System.Console.WriteLine();
}
else
{
System.Console.Write("TEST SUMMARY: PASSED");
System.Console.WriteLine();
}
System.Console.WriteLine();
System.Console.WriteLine();
}
result = !waserrors;
return(result);
}
/*************************************************************************
Internal bagging subroutine.
-- ALGLIB --
Copyright 19.02.2009 by Bochkanov Sergey
*************************************************************************/
private static void mlpebagginginternal(ref mlpensemble ensemble,
ref double[,] xy,
int npoints,
double decay,
int restarts,
double wstep,
int maxits,
bool lmalgorithm,
ref int info,
ref mlptrain.mlpreport rep,
ref 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 nin = 0;
int nout = 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();
mlpbase.multilayerperceptron network = new mlpbase.multilayerperceptron();
int i_ = 0;
int i1_ = 0;
//
// 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( ensemble.issoftmax )
{
for(i=0; i<=npoints-1; i++)
{
if( (int)Math.Round(xy[i,ensemble.nin])<0 | (int)Math.Round(xy[i,ensemble.nin])>=ensemble.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;
nin = ensemble.nin;
nout = ensemble.nout;
if( ensemble.issoftmax )
{
ccnt = nin+1;
pcnt = nin;
}
else
{
ccnt = nin+nout;
pcnt = nin+nout;
}
xys = new double[npoints-1+1, ccnt-1+1];
s = new bool[npoints-1+1];
oobbuf = new double[npoints-1+1, nout-1+1];
oobcntbuf = new int[npoints-1+1];
x = new double[nin-1+1];
y = new double[nout-1+1];
if( ensemble.issoftmax )
{
dy = new double[0+1];
}
else
{
dy = new double[nout-1+1];
}
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;
}
mlpbase.mlpunserialize(ref ensemble.serializedmlp, ref network);
//
// 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 = AP.Math.RandomInteger(npoints);
s[j] = true;
for(i_=0; i_<=ccnt-1;i_++)
{
xys[i,i_] = xy[j,i_];
}
}
//
// train
//
if( lmalgorithm )
{
mlptrain.mlptrainlm(ref network, ref xys, npoints, decay, restarts, ref info, ref tmprep);
}
else
{
mlptrain.mlptrainlbfgs(ref network, ref xys, npoints, decay, restarts, wstep, maxits, ref info, ref 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*ensemble.wcount);
for(i_=k*ensemble.wcount; i_<=(k+1)*ensemble.wcount-1;i_++)
{
ensemble.weights[i_] = network.weights[i_+i1_];
}
i1_ = (0) - (k*pcnt);
for(i_=k*pcnt; i_<=(k+1)*pcnt-1;i_++)
{
ensemble.columnmeans[i_] = network.columnmeans[i_+i1_];
}
i1_ = (0) - (k*pcnt);
for(i_=k*pcnt; i_<=(k+1)*pcnt-1;i_++)
{
ensemble.columnsigmas[i_] = 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(ref network, ref 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( ensemble.issoftmax )
{
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( ensemble.issoftmax )
{
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, ref y, ref 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];
}
public static bool testmlp(bool silent)
{
bool result = new bool();
bool waserrors = new bool();
int passcount = 0;
int maxn = 0;
int maxhid = 0;
int info = 0;
int nf = 0;
int nhid = 0;
int nl = 0;
int nhid1 = 0;
int nhid2 = 0;
int nkind = 0;
int i = 0;
int j = 0;
mlpbase.multilayerperceptron network = new mlpbase.multilayerperceptron();
mlpbase.multilayerperceptron network2 = new mlpbase.multilayerperceptron();
mlptrain.mlpreport rep = new mlptrain.mlpreport();
mlptrain.mlpcvreport cvrep = new mlptrain.mlpcvreport();
int ncount = 0;
double[,] xy = new double[0,0];
double[,] valxy = new double[0,0];
int ssize = 0;
int valsize = 0;
bool allsame = new bool();
bool inferrors = new bool();
bool procerrors = new bool();
bool graderrors = new bool();
bool hesserrors = new bool();
bool trnerrors = new bool();
waserrors = false;
inferrors = false;
procerrors = false;
graderrors = false;
hesserrors = false;
trnerrors = false;
passcount = 10;
maxn = 4;
maxhid = 4;
//
// General multilayer network tests
//
for(nf=1; nf<=maxn; nf++)
{
for(nl=1; nl<=maxn; nl++)
{
for(nhid1=0; nhid1<=maxhid; nhid1++)
{
for(nhid2=0; nhid2<=0; nhid2++)
{
for(nkind=0; nkind<=3; nkind++)
{
//
// Skip meaningless parameters combinations
//
if( nkind==1 & nl<2 )
{
continue;
}
if( nhid1==0 & nhid2!=0 )
{
continue;
}
//
// Tests
//
testinformational(nkind, nf, nhid1, nhid2, nl, passcount, ref inferrors);
testprocessing(nkind, nf, nhid1, nhid2, nl, passcount, ref procerrors);
testgradient(nkind, nf, nhid1, nhid2, nl, passcount, ref graderrors);
testhessian(nkind, nf, nhid1, nhid2, nl, passcount, ref hesserrors);
}
}
}
}
}
//
// Test network training on simple XOR problem
//
xy = new double[3+1, 2+1];
xy[0,0] = -1;
xy[0,1] = -1;
xy[0,2] = -1;
xy[1,0] = +1;
xy[1,1] = -1;
xy[1,2] = +1;
xy[2,0] = -1;
xy[2,1] = +1;
xy[2,2] = +1;
xy[3,0] = +1;
xy[3,1] = +1;
xy[3,2] = -1;
mlpbase.mlpcreate1(2, 2, 1, ref network);
mlptrain.mlptrainlm(ref network, ref xy, 4, 0.001, 10, ref info, ref rep);
trnerrors = trnerrors | (double)(mlpbase.mlprmserror(ref network, ref xy, 4))>(double)(0.1);
//
// Test CV on random noisy problem
//
ncount = 100;
xy = new double[ncount-1+1, 1+1];
for(i=0; i<=ncount-1; i++)
{
xy[i,0] = 2*AP.Math.RandomReal()-1;
xy[i,1] = AP.Math.RandomInteger(4);
}
mlpbase.mlpcreatec0(1, 4, ref network);
mlptrain.mlpkfoldcvlm(ref network, ref xy, ncount, 0.001, 5, 10, ref info, ref rep, ref cvrep);
//
// Final report
//
waserrors = inferrors | procerrors | graderrors | hesserrors | trnerrors;
if( !silent )
{
System.Console.Write("MLP TEST");
System.Console.WriteLine();
System.Console.Write("INFORMATIONAL FUNCTIONS: ");
if( !inferrors )
{
System.Console.Write("OK");
System.Console.WriteLine();
}
else
{
System.Console.Write("FAILED");
System.Console.WriteLine();
}
System.Console.Write("BASIC PROCESSING: ");
if( !procerrors )
{
System.Console.Write("OK");
System.Console.WriteLine();
}
else
{
System.Console.Write("FAILED");
System.Console.WriteLine();
}
System.Console.Write("GRADIENT CALCULATION: ");
if( !graderrors )
{
System.Console.Write("OK");
System.Console.WriteLine();
}
else
{
System.Console.Write("FAILED");
System.Console.WriteLine();
}
System.Console.Write("HESSIAN CALCULATION: ");
if( !hesserrors )
{
System.Console.Write("OK");
System.Console.WriteLine();
}
else
{
System.Console.Write("FAILED");
System.Console.WriteLine();
}
System.Console.Write("TRAINING: ");
if( !trnerrors )
{
System.Console.Write("OK");
System.Console.WriteLine();
}
else
{
System.Console.Write("FAILED");
System.Console.WriteLine();
}
if( waserrors )
{
System.Console.Write("TEST SUMMARY: FAILED");
System.Console.WriteLine();
}
else
{
System.Console.Write("TEST SUMMARY: PASSED");
System.Console.WriteLine();
}
System.Console.WriteLine();
System.Console.WriteLine();
}
result = !waserrors;
return result;
}
/*************************************************************************
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(ref mlpensemble ensemble,
ref double[,] xy,
int npoints,
double decay,
int restarts,
ref int info,
ref 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;
mlpbase.multilayerperceptron network = new mlpbase.multilayerperceptron();
int tmpinfo = 0;
mlptrain.mlpreport tmprep = new mlptrain.mlpreport();
int i_ = 0;
int i1_ = 0;
if( npoints<2 | restarts<1 | (double)(decay)<(double)(0) )
{
info = -1;
return;
}
if( ensemble.issoftmax )
{
for(i=0; i<=npoints-1; i++)
{
if( (int)Math.Round(xy[i,ensemble.nin])<0 | (int)Math.Round(xy[i,ensemble.nin])>=ensemble.nout )
{
info = -2;
return;
}
}
}
info = 6;
//
// allocate
//
if( ensemble.issoftmax )
{
ccount = ensemble.nin+1;
pcount = ensemble.nin;
}
else
{
ccount = ensemble.nin+ensemble.nout;
pcount = ensemble.nin+ensemble.nout;
}
trnxy = new double[npoints-1+1, ccount-1+1];
valxy = new double[npoints-1+1, ccount-1+1];
mlpbase.mlpunserialize(ref ensemble.serializedmlp, ref network);
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)(AP.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(ref network, ref trnxy, trnsize, ref valxy, valsize, decay, restarts, ref tmpinfo, ref tmprep);
if( tmpinfo<0 )
{
info = tmpinfo;
return;
}
//
// save results
//
i1_ = (0) - (k*ensemble.wcount);
for(i_=k*ensemble.wcount; i_<=(k+1)*ensemble.wcount-1;i_++)
{
ensemble.weights[i_] = network.weights[i_+i1_];
}
i1_ = (0) - (k*pcount);
for(i_=k*pcount; i_<=(k+1)*pcount-1;i_++)
{
ensemble.columnmeans[i_] = network.columnmeans[i_+i1_];
}
i1_ = (0) - (k*pcount);
for(i_=k*pcount; i_<=(k+1)*pcount-1;i_++)
{
ensemble.columnsigmas[i_] = network.columnsigmas[i_+i1_];
}
rep.ngrad = rep.ngrad+tmprep.ngrad;
rep.nhess = rep.nhess+tmprep.nhess;
rep.ncholesky = rep.ncholesky+tmprep.ncholesky;
}
}