public mlpcvreport(mlptrain.mlpcvreport obj)
{
_innerobj = obj;
}
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 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*math.randomreal()-1;
}
}
mlpe.mlpecreate1(nin, nhid, nout, 1+math.randominteger(3), ensemble);
}
else
{
xy = new double[npoints-1+1, nin+1];
nclasses = 2+math.randominteger(2);
for(i=0; i<=npoints-1; i++)
{
for(j=0; j<=nin-1; j++)
{
xy[i,j] = 2*math.randomreal()-1;
}
xy[i,nin] = math.randominteger(nclasses);
}
mlpe.mlpecreatec1(nin, nhid, nclasses, 1+math.randominteger(3), ensemble);
}
e = mlpe.mlpermserror(ensemble, xy, npoints);
if( algtype==0 )
{
mlpe.mlpebagginglm(ensemble, xy, npoints, 0.001, 1, ref info, rep, oobrep);
}
else
{
mlpe.mlpebagginglbfgs(ensemble, xy, npoints, 0.001, 1, 0.01, 0, ref info, rep, oobrep);
}
if( info<0 )
{
trnerrors = true;
}
else
{
if( (double)(mlpe.mlpermserror(ensemble, 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 mlpcvreport()
{
_innerobj = new mlptrain.mlpcvreport();
}
public static bool testmlptrain(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 nl = 0;
int nhid1 = 0;
int nhid2 = 0;
int nkind = 0;
int i = 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];
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, network);
mlptrain.mlptrainlm(network, xy, 4, 0.001, 10, ref info, rep);
trnerrors = trnerrors | (double)(mlpbase.mlprmserror(network, 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*math.randomreal()-1;
xy[i,1] = math.randominteger(4);
}
mlpbase.mlpcreatec0(1, 4, network);
mlptrain.mlpkfoldcvlm(network, xy, ncount, 0.001, 5, 10, ref info, rep, 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;
}
