/*************************************************************************
Processing functions test
*************************************************************************/
private static void testprocessing(int nkind,
int nin,
int nhid1,
int nhid2,
int nout,
int ec,
int passcount,
ref bool err)
{
mlpe.mlpensemble ensemble = new mlpe.mlpensemble();
mlpe.mlpensemble ensemble2 = new mlpe.mlpensemble();
bool zeronet = new bool();
double a1 = 0;
double a2 = 0;
int pass = 0;
int i = 0;
bool allsame = new bool();
int rlen = 0;
double[] x1 = new double[0];
double[] x2 = new double[0];
double[] y1 = new double[0];
double[] y2 = new double[0];
double[] ra = new double[0];
double[] ra2 = new double[0];
double v = 0;
//
// Prepare network
//
a1 = 0;
a2 = 0;
if( nkind==2 )
{
a1 = 1000*AP.Math.RandomReal()-500;
a2 = 2*AP.Math.RandomReal()-1;
}
if( nkind==3 )
{
a1 = 1000*AP.Math.RandomReal()-500;
a2 = a1+(2*AP.Math.RandomInteger(2)-1)*(0.1+0.9*AP.Math.RandomReal());
}
//
// Initialize arrays
//
x1 = new double[nin-1+1];
x2 = new double[nin-1+1];
y1 = new double[nout-1+1];
y2 = new double[nout-1+1];
//
// Main cycle
//
for(pass=1; pass<=passcount; pass++)
{
createensemble(ref ensemble, nkind, a1, a2, nin, nhid1, nhid2, nout, ec);
//
// Same inputs leads to same outputs
//
for(i=0; i<=nin-1; i++)
{
x1[i] = 2*AP.Math.RandomReal()-1;
x2[i] = x1[i];
}
for(i=0; i<=nout-1; i++)
{
y1[i] = 2*AP.Math.RandomReal()-1;
y2[i] = 2*AP.Math.RandomReal()-1;
}
mlpe.mlpeprocess(ref ensemble, ref x1, ref y1);
mlpe.mlpeprocess(ref ensemble, ref x2, ref y2);
allsame = true;
for(i=0; i<=nout-1; i++)
{
allsame = allsame & (double)(y1[i])==(double)(y2[i]);
}
err = err | !allsame;
//
// Same inputs on original network leads to same outputs
// on copy created using MLPCopy
//
unsetensemble(ref ensemble2);
mlpe.mlpecopy(ref ensemble, ref ensemble2);
for(i=0; i<=nin-1; i++)
{
x1[i] = 2*AP.Math.RandomReal()-1;
x2[i] = x1[i];
}
for(i=0; i<=nout-1; i++)
{
y1[i] = 2*AP.Math.RandomReal()-1;
y2[i] = 2*AP.Math.RandomReal()-1;
}
mlpe.mlpeprocess(ref ensemble, ref x1, ref y1);
mlpe.mlpeprocess(ref ensemble2, ref x2, ref y2);
allsame = true;
for(i=0; i<=nout-1; i++)
{
allsame = allsame & (double)(y1[i])==(double)(y2[i]);
}
err = err | !allsame;
//
// Same inputs on original network leads to same outputs
// on copy created using MLPSerialize
//
unsetensemble(ref ensemble2);
mlpe.mlpeserialize(ref ensemble, ref ra, ref rlen);
ra2 = new double[rlen-1+1];
for(i=0; i<=rlen-1; i++)
{
ra2[i] = ra[i];
}
mlpe.mlpeunserialize(ref ra2, ref ensemble2);
for(i=0; i<=nin-1; i++)
{
x1[i] = 2*AP.Math.RandomReal()-1;
x2[i] = x1[i];
}
for(i=0; i<=nout-1; i++)
{
y1[i] = 2*AP.Math.RandomReal()-1;
y2[i] = 2*AP.Math.RandomReal()-1;
}
mlpe.mlpeprocess(ref ensemble, ref x1, ref y1);
mlpe.mlpeprocess(ref ensemble2, ref x2, ref y2);
allsame = true;
for(i=0; i<=nout-1; i++)
{
allsame = allsame & (double)(y1[i])==(double)(y2[i]);
}
err = err | !allsame;
//
// Different inputs leads to different outputs (non-zero network)
//
for(i=0; i<=nin-1; i++)
{
x1[i] = 2*AP.Math.RandomReal()-1;
x2[i] = 2*AP.Math.RandomReal()-1;
}
for(i=0; i<=nout-1; i++)
{
y1[i] = 2*AP.Math.RandomReal()-1;
y2[i] = y1[i];
}
mlpe.mlpeprocess(ref ensemble, ref x1, ref y1);
mlpe.mlpeprocess(ref ensemble, ref x2, ref y2);
allsame = true;
for(i=0; i<=nout-1; i++)
{
allsame = allsame & (double)(y1[i])==(double)(y2[i]);
}
err = err | allsame;
//
// Randomization changes outputs (when inputs are unchanged, non-zero network)
//
for(i=0; i<=nin-1; i++)
{
x1[i] = 2*AP.Math.RandomReal()-1;
x2[i] = 2*AP.Math.RandomReal()-1;
}
for(i=0; i<=nout-1; i++)
{
y1[i] = 2*AP.Math.RandomReal()-1;
y2[i] = y1[i];
}
mlpe.mlpecopy(ref ensemble, ref ensemble2);
mlpe.mlperandomize(ref ensemble2);
mlpe.mlpeprocess(ref ensemble, ref x1, ref y1);
mlpe.mlpeprocess(ref ensemble2, ref x1, ref y2);
allsame = true;
for(i=0; i<=nout-1; i++)
{
allsame = allsame & (double)(y1[i])==(double)(y2[i]);
}
err = err | allsame;
//
// Normalization properties
//
if( nkind==1 )
{
//
// Classifier network outputs are normalized
//
for(i=0; i<=nin-1; i++)
{
x1[i] = 2*AP.Math.RandomReal()-1;
}
mlpe.mlpeprocess(ref ensemble, ref x1, ref y1);
v = 0;
for(i=0; i<=nout-1; i++)
{
v = v+y1[i];
err = err | (double)(y1[i])<(double)(0);
}
err = err | (double)(Math.Abs(v-1))>(double)(1000*AP.Math.MachineEpsilon);
}
if( nkind==2 )
{
//
// B-type network outputs are bounded from above/below
//
for(i=0; i<=nin-1; i++)
{
x1[i] = 2*AP.Math.RandomReal()-1;
}
mlpe.mlpeprocess(ref ensemble, ref x1, ref y1);
for(i=0; i<=nout-1; i++)
{
if( (double)(a2)>=(double)(0) )
{
err = err | (double)(y1[i])<(double)(a1);
}
else
{
err = err | (double)(y1[i])>(double)(a1);
}
}
}
if( nkind==3 )
{
//
// R-type network outputs are within [A1,A2] (or [A2,A1])
//
for(i=0; i<=nin-1; i++)
{
x1[i] = 2*AP.Math.RandomReal()-1;
}
mlpe.mlpeprocess(ref ensemble, ref x1, ref y1);
for(i=0; i<=nout-1; i++)
{
err = err | (double)(y1[i])<(double)(Math.Min(a1, a2)) | (double)(y1[i])>(double)(Math.Max(a1, a2));
}
}
}
}
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;
}
/*************************************************************************
Iformational functions test
*************************************************************************/
private static void testinformational(int nkind,
int nin,
int nhid1,
int nhid2,
int nout,
int ec,
int passcount,
ref bool err)
{
mlpe.mlpensemble ensemble = new mlpe.mlpensemble();
int n1 = 0;
int n2 = 0;
createensemble(ref ensemble, nkind, -1.0, 1.0, nin, nhid1, nhid2, nout, ec);
mlpe.mlpeproperties(ref ensemble, ref n1, ref n2);
err = err | n1!=nin | n2!=nout;
}
