public linlsqrreport(linlsqr.linlsqrreport obj)
{
_innerobj = obj;
}
private static void buildrbfmlayersmodellsqr(double[,] x,
ref double[,] y,
ref double[,] xc,
double rval,
ref double[] r,
int n,
ref int nc,
int ny,
int nlayers,
nearestneighbor.kdtree centerstree,
double epsort,
double epserr,
int maxits,
double lambdav,
ref int annz,
ref double[,] w,
ref int info,
ref int iterationscount,
ref int nmv)
{
linlsqr.linlsqrstate state = new linlsqr.linlsqrstate();
linlsqr.linlsqrreport lsqrrep = new linlsqr.linlsqrreport();
sparse.sparsematrix spa = new sparse.sparsematrix();
double anorm = 0;
double[] omega = new double[0];
double[] xx = new double[0];
double[] tmpy = new double[0];
double[,] cx = new double[0,0];
double yval = 0;
int nec = 0;
int[] centerstags = new int[0];
int layer = 0;
int i = 0;
int j = 0;
int k = 0;
double v = 0;
double rmaxbefore = 0;
double rmaxafter = 0;
xc = new double[0,0];
r = new double[0];
nc = 0;
annz = 0;
w = new double[0,0];
info = 0;
iterationscount = 0;
nmv = 0;
alglib.ap.assert(nlayers>=0, "BuildRBFMLayersModelLSQR: invalid argument(NLayers<0)");
alglib.ap.assert(n>=0, "BuildRBFMLayersModelLSQR: invalid argument(N<0)");
alglib.ap.assert(mxnx>0 && mxnx<=3, "BuildRBFMLayersModelLSQR: internal error(invalid global const MxNX: either MxNX<=0 or MxNX>3)");
annz = 0;
if( n==0 || nlayers==0 )
{
info = 1;
iterationscount = 0;
nmv = 0;
return;
}
nc = n*nlayers;
xx = new double[mxnx];
centerstags = new int[n];
xc = new double[nc, mxnx];
r = new double[nc];
for(i=0; i<=nc-1; i++)
{
for(j=0; j<=mxnx-1; j++)
{
xc[i,j] = x[i%n,j];
}
}
for(i=0; i<=nc-1; i++)
{
r[i] = rval/Math.Pow(2, i/n);
}
for(i=0; i<=n-1; i++)
{
centerstags[i] = i;
}
nearestneighbor.kdtreebuildtagged(xc, centerstags, n, mxnx, 0, 2, centerstree);
omega = new double[n];
tmpy = new double[n];
w = new double[nc, ny];
info = -1;
iterationscount = 0;
nmv = 0;
linlsqr.linlsqrcreate(n, n, state);
linlsqr.linlsqrsetcond(state, epsort, epserr, maxits);
linlsqr.linlsqrsetlambdai(state, 1.0E-6);
//
// calculate number of non-zero elements for sparse matrix
//
for(i=0; i<=n-1; i++)
{
for(j=0; j<=mxnx-1; j++)
{
xx[j] = x[i,j];
}
annz = annz+nearestneighbor.kdtreequeryrnn(centerstree, xx, r[0]*rbfmlradius, true);
}
for(layer=0; layer<=nlayers-1; layer++)
{
//
// Fill sparse matrix, calculate norm(A)
//
anorm = 0.0;
sparse.sparsecreate(n, n, annz, spa);
for(i=0; i<=n-1; i++)
{
for(j=0; j<=mxnx-1; j++)
{
xx[j] = x[i,j];
}
nec = nearestneighbor.kdtreequeryrnn(centerstree, xx, r[layer*n]*rbfmlradius, true);
nearestneighbor.kdtreequeryresultsx(centerstree, ref cx);
nearestneighbor.kdtreequeryresultstags(centerstree, ref centerstags);
for(j=0; j<=nec-1; j++)
{
v = Math.Exp(-((math.sqr(xx[0]-cx[j,0])+math.sqr(xx[1]-cx[j,1])+math.sqr(xx[2]-cx[j,2]))/math.sqr(r[layer*n+centerstags[j]])));
sparse.sparseset(spa, i, centerstags[j], v);
anorm = anorm+math.sqr(v);
}
}
anorm = Math.Sqrt(anorm);
sparse.sparseconverttocrs(spa);
//
// Calculate maximum residual before adding new layer.
// This value is not used by algorithm, the only purpose is to make debugging easier.
//
rmaxbefore = 0.0;
for(j=0; j<=n-1; j++)
{
for(i=0; i<=ny-1; i++)
{
rmaxbefore = Math.Max(rmaxbefore, Math.Abs(y[j,i]));
}
}
//
// Process NY dimensions of the target function
//
for(i=0; i<=ny-1; i++)
{
for(j=0; j<=n-1; j++)
{
tmpy[j] = y[j,i];
}
//
// calculate Omega for current layer
//
linlsqr.linlsqrsetlambdai(state, lambdav*anorm/n);
linlsqr.linlsqrsolvesparse(state, spa, tmpy);
linlsqr.linlsqrresults(state, ref omega, lsqrrep);
if( lsqrrep.terminationtype<=0 )
{
info = -4;
return;
}
//
// calculate error for current layer
//
for(j=0; j<=n-1; j++)
{
yval = 0;
for(k=0; k<=mxnx-1; k++)
{
xx[k] = x[j,k];
}
nec = nearestneighbor.kdtreequeryrnn(centerstree, xx, r[layer*n]*rbffarradius, true);
nearestneighbor.kdtreequeryresultsx(centerstree, ref cx);
nearestneighbor.kdtreequeryresultstags(centerstree, ref centerstags);
for(k=0; k<=nec-1; k++)
{
yval = yval+omega[centerstags[k]]*Math.Exp(-((math.sqr(xx[0]-cx[k,0])+math.sqr(xx[1]-cx[k,1])+math.sqr(xx[2]-cx[k,2]))/math.sqr(r[layer*n+centerstags[k]])));
}
y[j,i] = y[j,i]-yval;
}
//
// write Omega in out parameter W
//
for(j=0; j<=n-1; j++)
{
w[layer*n+j,i] = omega[j];
}
iterationscount = iterationscount+lsqrrep.iterationscount;
nmv = nmv+lsqrrep.nmv;
}
//
// Calculate maximum residual before adding new layer.
// This value is not used by algorithm, the only purpose is to make debugging easier.
//
rmaxafter = 0.0;
for(j=0; j<=n-1; j++)
{
for(i=0; i<=ny-1; i++)
{
rmaxafter = Math.Max(rmaxafter, Math.Abs(y[j,i]));
}
}
}
info = 1;
}
public linlsqrreport()
{
_innerobj = new linlsqr.linlsqrreport();
}
private static void buildrbfmodellsqr(double[,] x,
ref double[,] y,
double[,] xc,
double[] r,
int n,
int nc,
int ny,
nearestneighbor.kdtree pointstree,
nearestneighbor.kdtree centerstree,
double epsort,
double epserr,
int maxits,
ref int gnnz,
ref int snnz,
ref double[,] w,
ref int info,
ref int iterationscount,
ref int nmv)
{
linlsqr.linlsqrstate state = new linlsqr.linlsqrstate();
linlsqr.linlsqrreport lsqrrep = new linlsqr.linlsqrreport();
sparse.sparsematrix spg = new sparse.sparsematrix();
sparse.sparsematrix sps = new sparse.sparsematrix();
int[] nearcenterscnt = new int[0];
int[] nearpointscnt = new int[0];
int[] skipnearpointscnt = new int[0];
int[] farpointscnt = new int[0];
int maxnearcenterscnt = 0;
int maxnearpointscnt = 0;
int maxfarpointscnt = 0;
int sumnearcenterscnt = 0;
int sumnearpointscnt = 0;
int sumfarpointscnt = 0;
double maxrad = 0;
int[] pointstags = new int[0];
int[] centerstags = new int[0];
double[,] nearpoints = new double[0,0];
double[,] nearcenters = new double[0,0];
double[,] farpoints = new double[0,0];
int tmpi = 0;
int pointscnt = 0;
int centerscnt = 0;
double[] xcx = new double[0];
double[] tmpy = new double[0];
double[] tc = new double[0];
double[] g = new double[0];
double[] c = new double[0];
int i = 0;
int j = 0;
int k = 0;
int sind = 0;
double[,] a = new double[0,0];
double vv = 0;
double vx = 0;
double vy = 0;
double vz = 0;
double vr = 0;
double gnorm2 = 0;
double[] tmp0 = new double[0];
double[] tmp1 = new double[0];
double[] tmp2 = new double[0];
double fx = 0;
double[,] xx = new double[0,0];
double[,] cx = new double[0,0];
double mrad = 0;
int i_ = 0;
gnnz = 0;
snnz = 0;
w = new double[0,0];
info = 0;
iterationscount = 0;
nmv = 0;
//
// Handle special cases: NC=0
//
if( nc==0 )
{
info = 1;
iterationscount = 0;
nmv = 0;
return;
}
//
// Prepare for general case, NC>0
//
xcx = new double[mxnx];
pointstags = new int[n];
centerstags = new int[nc];
info = -1;
iterationscount = 0;
nmv = 0;
//
// This block prepares quantities used to compute approximate cardinal basis functions (ACBFs):
// * NearCentersCnt[] - array[NC], whose elements store number of near centers used to build ACBF
// * NearPointsCnt[] - array[NC], number of near points used to build ACBF
// * FarPointsCnt[] - array[NC], number of far points (ones where ACBF is nonzero)
// * MaxNearCentersCnt - max(NearCentersCnt)
// * MaxNearPointsCnt - max(NearPointsCnt)
// * SumNearCentersCnt - sum(NearCentersCnt)
// * SumNearPointsCnt - sum(NearPointsCnt)
// * SumFarPointsCnt - sum(FarPointsCnt)
//
nearcenterscnt = new int[nc];
nearpointscnt = new int[nc];
skipnearpointscnt = new int[nc];
farpointscnt = new int[nc];
maxnearcenterscnt = 0;
maxnearpointscnt = 0;
maxfarpointscnt = 0;
sumnearcenterscnt = 0;
sumnearpointscnt = 0;
sumfarpointscnt = 0;
for(i=0; i<=nc-1; i++)
{
for(j=0; j<=mxnx-1; j++)
{
xcx[j] = xc[i,j];
}
//
// Determine number of near centers and maximum radius of near centers
//
nearcenterscnt[i] = nearestneighbor.kdtreequeryrnn(centerstree, xcx, r[i]*rbfnearradius, true);
nearestneighbor.kdtreequeryresultstags(centerstree, ref centerstags);
maxrad = 0;
for(j=0; j<=nearcenterscnt[i]-1; j++)
{
maxrad = Math.Max(maxrad, Math.Abs(r[centerstags[j]]));
}
//
// Determine number of near points (ones which used to build ACBF)
// and skipped points (the most near points which are NOT used to build ACBF
// and are NOT included in the near points count
//
skipnearpointscnt[i] = nearestneighbor.kdtreequeryrnn(pointstree, xcx, 0.1*r[i], true);
nearpointscnt[i] = nearestneighbor.kdtreequeryrnn(pointstree, xcx, (r[i]+maxrad)*rbfnearradius, true)-skipnearpointscnt[i];
alglib.ap.assert(nearpointscnt[i]>=0, "BuildRBFModelLSQR: internal error");
//
// Determine number of far points
//
farpointscnt[i] = nearestneighbor.kdtreequeryrnn(pointstree, xcx, Math.Max(r[i]*rbfnearradius+maxrad*rbffarradius, r[i]*rbffarradius), true);
//
// calculate sum and max, make some basic checks
//
alglib.ap.assert(nearcenterscnt[i]>0, "BuildRBFModelLSQR: internal error");
maxnearcenterscnt = Math.Max(maxnearcenterscnt, nearcenterscnt[i]);
maxnearpointscnt = Math.Max(maxnearpointscnt, nearpointscnt[i]);
maxfarpointscnt = Math.Max(maxfarpointscnt, farpointscnt[i]);
sumnearcenterscnt = sumnearcenterscnt+nearcenterscnt[i];
sumnearpointscnt = sumnearpointscnt+nearpointscnt[i];
sumfarpointscnt = sumfarpointscnt+farpointscnt[i];
}
snnz = sumnearcenterscnt;
gnnz = sumfarpointscnt;
alglib.ap.assert(maxnearcenterscnt>0, "BuildRBFModelLSQR: internal error");
//
// Allocate temporaries.
//
// NOTE: we want to avoid allocation of zero-size arrays, so we
// use max(desired_size,1) instead of desired_size when performing
// memory allocation.
//
a = new double[maxnearpointscnt+maxnearcenterscnt, maxnearcenterscnt];
tmpy = new double[maxnearpointscnt+maxnearcenterscnt];
g = new double[maxnearcenterscnt];
c = new double[maxnearcenterscnt];
nearcenters = new double[maxnearcenterscnt, mxnx];
nearpoints = new double[Math.Max(maxnearpointscnt, 1), mxnx];
farpoints = new double[Math.Max(maxfarpointscnt, 1), mxnx];
//
// fill matrix SpG
//
sparse.sparsecreate(n, nc, gnnz, spg);
sparse.sparsecreate(nc, nc, snnz, sps);
for(i=0; i<=nc-1; i++)
{
centerscnt = nearcenterscnt[i];
//
// main center
//
for(j=0; j<=mxnx-1; j++)
{
xcx[j] = xc[i,j];
}
//
// center's tree
//
tmpi = nearestneighbor.kdtreequeryknn(centerstree, xcx, centerscnt, true);
alglib.ap.assert(tmpi==centerscnt, "BuildRBFModelLSQR: internal error");
nearestneighbor.kdtreequeryresultsx(centerstree, ref cx);
nearestneighbor.kdtreequeryresultstags(centerstree, ref centerstags);
//
// point's tree
//
mrad = 0;
for(j=0; j<=centerscnt-1; j++)
{
mrad = Math.Max(mrad, r[centerstags[j]]);
}
//
// we need to be sure that 'CTree' contains
// at least one side center
//
sparse.sparseset(sps, i, i, 1);
c[0] = 1.0;
for(j=1; j<=centerscnt-1; j++)
{
c[j] = 0.0;
}
if( centerscnt>1 && nearpointscnt[i]>0 )
{
//
// first KDTree request for points
//
pointscnt = nearpointscnt[i];
tmpi = nearestneighbor.kdtreequeryknn(pointstree, xcx, skipnearpointscnt[i]+nearpointscnt[i], true);
alglib.ap.assert(tmpi==skipnearpointscnt[i]+nearpointscnt[i], "BuildRBFModelLSQR: internal error");
nearestneighbor.kdtreequeryresultsx(pointstree, ref xx);
sind = skipnearpointscnt[i];
for(j=0; j<=pointscnt-1; j++)
{
vx = xx[sind+j,0];
vy = xx[sind+j,1];
vz = xx[sind+j,2];
for(k=0; k<=centerscnt-1; k++)
{
vr = 0.0;
vv = vx-cx[k,0];
vr = vr+vv*vv;
vv = vy-cx[k,1];
vr = vr+vv*vv;
vv = vz-cx[k,2];
vr = vr+vv*vv;
vv = r[centerstags[k]];
a[j,k] = Math.Exp(-(vr/(vv*vv)));
}
}
for(j=0; j<=centerscnt-1; j++)
{
g[j] = Math.Exp(-((math.sqr(xcx[0]-cx[j,0])+math.sqr(xcx[1]-cx[j,1])+math.sqr(xcx[2]-cx[j,2]))/math.sqr(r[centerstags[j]])));
}
//
// calculate the problem
//
gnorm2 = 0.0;
for(i_=0; i_<=centerscnt-1;i_++)
{
gnorm2 += g[i_]*g[i_];
}
for(j=0; j<=pointscnt-1; j++)
{
vv = 0.0;
for(i_=0; i_<=centerscnt-1;i_++)
{
vv += a[j,i_]*g[i_];
}
vv = vv/gnorm2;
tmpy[j] = -vv;
for(i_=0; i_<=centerscnt-1;i_++)
{
a[j,i_] = a[j,i_] - vv*g[i_];
}
}
for(j=pointscnt; j<=pointscnt+centerscnt-1; j++)
{
for(k=0; k<=centerscnt-1; k++)
{
a[j,k] = 0.0;
}
a[j,j-pointscnt] = 1.0E-6;
tmpy[j] = 0.0;
}
fbls.fblssolvels(ref a, ref tmpy, pointscnt+centerscnt, centerscnt, ref tmp0, ref tmp1, ref tmp2);
for(i_=0; i_<=centerscnt-1;i_++)
{
c[i_] = tmpy[i_];
}
vv = 0.0;
for(i_=0; i_<=centerscnt-1;i_++)
{
vv += g[i_]*c[i_];
}
vv = vv/gnorm2;
for(i_=0; i_<=centerscnt-1;i_++)
{
c[i_] = c[i_] - vv*g[i_];
}
vv = 1/gnorm2;
for(i_=0; i_<=centerscnt-1;i_++)
{
c[i_] = c[i_] + vv*g[i_];
}
for(j=0; j<=centerscnt-1; j++)
{
sparse.sparseset(sps, i, centerstags[j], c[j]);
}
}
//
// second KDTree request for points
//
pointscnt = farpointscnt[i];
tmpi = nearestneighbor.kdtreequeryknn(pointstree, xcx, pointscnt, true);
alglib.ap.assert(tmpi==pointscnt, "BuildRBFModelLSQR: internal error");
nearestneighbor.kdtreequeryresultsx(pointstree, ref xx);
nearestneighbor.kdtreequeryresultstags(pointstree, ref pointstags);
//
//fill SpG matrix
//
for(j=0; j<=pointscnt-1; j++)
{
fx = 0;
vx = xx[j,0];
vy = xx[j,1];
vz = xx[j,2];
for(k=0; k<=centerscnt-1; k++)
{
vr = 0.0;
vv = vx-cx[k,0];
vr = vr+vv*vv;
vv = vy-cx[k,1];
vr = vr+vv*vv;
vv = vz-cx[k,2];
vr = vr+vv*vv;
vv = r[centerstags[k]];
vv = vv*vv;
fx = fx+c[k]*Math.Exp(-(vr/vv));
}
sparse.sparseset(spg, pointstags[j], i, fx);
}
}
sparse.sparseconverttocrs(spg);
sparse.sparseconverttocrs(sps);
//
// solve by LSQR method
//
tmpy = new double[n];
tc = new double[nc];
w = new double[nc, ny];
linlsqr.linlsqrcreate(n, nc, state);
linlsqr.linlsqrsetcond(state, epsort, epserr, maxits);
for(i=0; i<=ny-1; i++)
{
for(j=0; j<=n-1; j++)
{
tmpy[j] = y[j,i];
}
linlsqr.linlsqrsolvesparse(state, spg, tmpy);
linlsqr.linlsqrresults(state, ref c, lsqrrep);
if( lsqrrep.terminationtype<=0 )
{
info = -4;
return;
}
sparse.sparsemtv(sps, c, ref tc);
for(j=0; j<=nc-1; j++)
{
w[j,i] = tc[j];
}
iterationscount = iterationscount+lsqrrep.iterationscount;
nmv = nmv+lsqrrep.nmv;
}
info = 1;
}