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klustakwik.cpp
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// MaskedKlustaKwik2.C
//
// Fast clustering using the CEM algorithm with Masks.
// Disable some Visual Studio warnings
#define _CRT_SECURE_NO_WARNINGS
#include "klustakwik.h"
#define _USE_MATH_DEFINES
#include<math.h>
// GLOBAL VARIABLES
FILE *Distfp;
integer global_numiterations = 0;
scalar iteration_metric2 = (scalar)0;
scalar iteration_metric3 = (scalar)0;
clock_t Clock0;
scalar timesofar;
// Does a memory check (should only be called for first instance of KK)
void KK::MemoryCheck()
{
integer num_bytes_required = 3 * NumBytesRequired();
scalar memory_required = (num_bytes_required*1.0) / (1024.0*1024.0*1024.0);
if (memory_required > memory_tracker.limit_gb)
{
Error("Running KlustaKwik on this data will use between %.2f and %.2f GB of RAM, and the limit is set at %.2f.\n", (double)(memory_required*2.0 / 3.0), (double)memory_required, (double)memory_tracker.limit_gb);
Error("Possible candidates are:\n");
Error("- nPoints = %d\n", (int)nPoints);
Error("- nDims = %d\n", (int)nDims);
Error("- MaxPossibleClusters = %d\n", (int)MaxPossibleClusters);
exit(EXIT_FAILURE);
}
Output("This run is expected to use between %.2f and %.2f GB of RAM.\n", (double)(memory_required*2.0 / 3.0), (double)memory_required);
}
integer KK::NumBytesRequired()
{
// we don't allocate any memory if we have already allocated memory to this
// (i.e. if we are in TrySplits)
if (Data.size())
return 0;
nDims2 = nDims*nDims;
// Compute required memory and check if it exceeds the limit set
integer num_bytes_allocated =
sizeof(scalar)*nPoints*nDims + // Data
sizeof(integer)*nPoints*nDims + // Masks
sizeof(scalar)*nPoints*nDims + // FloatMasks
sizeof(scalar)*nPoints + // UnMaskDims
sizeof(scalar)*MaxPossibleClusters + // Weight
sizeof(scalar)*MaxPossibleClusters*nDims + // Mean
(1 - UseDistributional)*sizeof(scalar)*MaxPossibleClusters*nDims2 + // Cov
sizeof(scalar)*MaxPossibleClusters*nPoints + // LogP
sizeof(integer)*nPoints + // Class
sizeof(integer)*nPoints + // OldClass
sizeof(integer)*nPoints + // Class2
sizeof(integer)*nPoints + // BestClass
sizeof(integer)*MaxPossibleClusters + // ClassAlive
sizeof(integer)*MaxPossibleClusters + // AliveIndex
sizeof(scalar)*MaxPossibleClusters + // ClassPenalty
sizeof(integer)*MaxPossibleClusters + // nClassMembers
sizeof(scalar)*nPoints*nDims + // AllVector2Mean
// UseDistributional only
UseDistributional*sizeof(scalar)*MaxPossibleClusters + // CorrectionTerm
sizeof(scalar)*(UseDistributional*MaxPossibleClusters*nDims) + // ClusterMask (vector<scalar>)
UseDistributional*sizeof(integer)*MaxPossibleClusters*nDims; // ClusterUnmaskedFeatures + ClusterMaskedFeatures
return num_bytes_allocated;
}
template<class T>
inline void resize_and_fill_with_zeros(vector<T> &x, integer newsize)
{
if (x.size() == 0)
{
x.resize((uinteger)newsize);
return;
}
if (x.size() > (uinteger)newsize)
{
fill(x.begin(), x.end(), (T)0);
x.resize((uinteger)newsize);
}
else
{
x.resize((uinteger)newsize);
fill(x.begin(), x.end(), (T)0);
}
}
// Sets storage for KK class. Needs to have nDims and nPoints defined
void KK::AllocateArrays() {
nDims2 = nDims*nDims;
NoisePoint = 1; // Ensures that the mixture weight for the noise cluster never gets to zero
integer num_bytes_allocated = NumBytesRequired();
mem.add(num_bytes_allocated);
// Set sizes for arrays
resize_and_fill_with_zeros(Data, nPoints * nDims);
//SNK
resize_and_fill_with_zeros(Masks, nPoints * nDims);
resize_and_fill_with_zeros(FloatMasks, nPoints * nDims);
resize_and_fill_with_zeros(UnMaskDims, nPoints); //SNK Number of unmasked dimensions for each data point when using float masks $\sum m_i$
resize_and_fill_with_zeros(Weight, MaxPossibleClusters);
resize_and_fill_with_zeros(Mean, MaxPossibleClusters*nDims);
if (!UseDistributional)
resize_and_fill_with_zeros(Cov, MaxPossibleClusters*nDims2);
resize_and_fill_with_zeros(LogP, MaxPossibleClusters*nPoints);
resize_and_fill_with_zeros(Class, nPoints);
resize_and_fill_with_zeros(OldClass, nPoints);
resize_and_fill_with_zeros(Class2, nPoints);
resize_and_fill_with_zeros(BestClass, nPoints);
resize_and_fill_with_zeros(ClassAlive, MaxPossibleClusters);
resize_and_fill_with_zeros(AliveIndex, MaxPossibleClusters);
resize_and_fill_with_zeros(ClassPenalty, MaxPossibleClusters);
resize_and_fill_with_zeros(nClassMembers, MaxPossibleClusters);
if(UseDistributional)
{
resize_and_fill_with_zeros(CorrectionTerm, nPoints * nDims);
resize_and_fill_with_zeros(ClusterMask, MaxPossibleClusters*nDims);
}
}
// recompute index of alive clusters (including 0, the noise cluster)
// should be called after anything that changes ClassAlive
void KK::Reindex()
{
integer c;
AliveIndex[0] = 0;
nClustersAlive=1;
for(c=1;c<MaxPossibleClusters;c++)
{
if (ClassAlive[c])
{
AliveIndex[nClustersAlive] = c;
nClustersAlive++;
}
}
}
// Penalty for standard CEM
// Penalty(nAlive) returns the complexity penalty for that many clusters
// bearing in mind that cluster 0 has no free params except p.
scalar KK::Penalty(integer n)
{
integer nParams;
if(n==1)
return 0;
nParams = (nDims*(nDims+1)/2 + nDims + 1)*(n-1); // each has cov, mean, &p
scalar p = penaltyK*(scalar)(nParams) // AIC units (Spurious factor of 2 removed from AIC units on 09.07.13)
+penaltyKLogN*((scalar)nParams*(scalar)log((scalar)nPoints)/2); // BIC units
return p;
}
// Penalties for Masked CEM
void KK::ComputeClassPenalties()
{
if(!((bool)UseDistributional)) // This function must only be called in Use Distributional mode
{
// Output("Caught in ComputeClassPenalties");
return;
}
// Output("ComputeClassPenalties: Correct if UseDistributional only");
for(integer c=0; c<MaxPossibleClusters; c++)
ClassPenalty[c] = (scalar)0;
// compute sum of nParams for each
vector<integer> NumberInClass(MaxPossibleClusters);
for(integer p=0; p<nPoints; p++)
{
integer c = Class[p];
NumberInClass[c]++;
// integer n = UnmaskedInd[p+1]-UnmaskedInd[p]; // num unmasked dimensions
scalar n = UnMaskDims[p];
scalar nParams = n*(n+1)/2+n+1;
ClassPenalty[c] += nParams;
}
// compute mean nParams for each cluster
for(integer c=0; c<MaxPossibleClusters; c++)
if(NumberInClass[c]>0)
ClassPenalty[c] /= (scalar)NumberInClass[c];
// compute penalty for each cluster
for(integer c=0; c<MaxPossibleClusters; c++)
{
scalar nParams = ClassPenalty[c];
ClassPenalty[c] = penaltyK*(scalar)(nParams*2)
+penaltyKLogN*((scalar)nParams*(scalar)log((scalar)nPoints)/2);
}
}
// Compute the cluster masks (i.e. the sets of features which are masked/unmasked
// for the whole cluster). Used by M-step and E-step
void KK::ComputeClusterMasks()
{
Reindex();
// Initialise cluster mask to 0
for(integer i=0; i<nDims*MaxPossibleClusters; i++)
ClusterMask[i] = 0;
// Compute cluster mask
for(integer p=0; p<nPoints; p++)
{
integer c = Class[p];
for (integer i = 0; i < nDims; i++)
{
ClusterMask[c*nDims + i] += FloatMasks[p*nDims + i];
}
}
// Compute the set of masked/unmasked features for each cluster
// reset all the subvectors to empty
ClusterUnmaskedFeatures.clear();
ClusterUnmaskedFeatures.resize(MaxPossibleClusters);
ClusterMaskedFeatures.clear();
ClusterMaskedFeatures.resize(MaxPossibleClusters);
// fill them in
for (integer cc = 0; cc<nClustersAlive; cc++)
{
integer c = AliveIndex[cc];
vector<integer> &CurrentUnmasked = ClusterUnmaskedFeatures[c];
vector<integer> &CurrentMasked = ClusterMaskedFeatures[c];
for (integer i = 0; i < nDims; i++)
{
if (ClusterMask[c*nDims + i]>=PointsForClusterMask)
CurrentUnmasked.push_back(i);
else
CurrentMasked.push_back(i);
}
if (Verbose>=2)
{
Output("Cluster mask: cluster %d unmasked %d iterations %d/%d init type %d.\n",
(int)cc, (int)CurrentUnmasked.size(),
(int)numiterations, (int)global_numiterations, (int)init_type);
}
}
}
// M-step: Calculate mean, cov, and weight for each living class
// also deletes any classes with fewer points than nDim
void KK::MStep()
{
integer p, c, cc, i, j;
vector<scalar> Vec2Mean(nDims);
// clear arrays
memset((void*)&nClassMembers.front(), 0, MaxPossibleClusters*sizeof(integer));
memset((void*)&Mean.front(), 0, MaxPossibleClusters*nDims*sizeof(scalar));
if (!UseDistributional)
memset((void*)&Cov.front(), 0, MaxPossibleClusters*nDims2*sizeof(scalar));
// NOTE: memset commands above replace the code below:
// for(c=0; c<MaxPossibleClusters; c++) {
// nClassMembers[c] = 0;
// for(i=0; i<nDims; i++) Mean[c*nDims + i] = 0;
// }
if (Debug) { Output("Entering Unmasked Mstep \n");}
// Accumulate total number of points in each class
for (p=0; p<nPoints; p++) nClassMembers[Class[p]]++;
// check for any dead classes
if(UseDistributional)
{
for (integer cc=0; cc<nClustersAlive; cc++)
{
integer c = AliveIndex[cc];
if (Debug){Output("DistributionalMstep: Class %d contains %d members \n", (int)c, (int)nClassMembers[c]);}
if (c>0 && nClassMembers[c]<1)//nDims)
{
ClassAlive[c]=0;
if (Debug) {Output("UnmaskedMstep_dist: Deleted class %d: no members\n", (int)c);}
}
}
}
else
{
for (cc=0; cc<nClustersAlive; cc++)
{
c = AliveIndex[cc];
if (Debug) {Output("Mstep: Class %d contains %d members \n", (int)c, (int)nClassMembers[c]);}
if (c>0 && nClassMembers[c]<=nDims)
{
ClassAlive[c]=0;
if (Debug) {Output("Deleted class %d: not enough members\n", (int)c);}
}
}
}
Reindex();
// Normalize by total number of points to give class weight
// Also check for dead classes
if(UseDistributional)
{
for (cc=0; cc<nClustersAlive; cc++)
{
c = AliveIndex[cc];
//Output("DistributionalMstep: PriorPoint on weights ");
// add "noise point" to make sure Weight for noise cluster never gets to zero
if(c==0)
{
Weight[c] = ((scalar)nClassMembers[c]+NoisePoint) / (nPoints+NoisePoint+priorPoint*(nClustersAlive-1));
}
else
{
Weight[c] = ((scalar)nClassMembers[c]+priorPoint) / (nPoints+NoisePoint+priorPoint*(nClustersAlive-1));
}
}
}
else // For Original KlustaKwik, Classical EM
{
for (cc=0; cc<nClustersAlive; cc++)
{
c = AliveIndex[cc];
// add "noise point" to make sure Weight for noise cluster never gets to zero
if(c==0)
{
Weight[c] = ((scalar)nClassMembers[c]+NoisePoint) / (nPoints+NoisePoint);
}
else
{
Weight[c] = ((scalar)nClassMembers[c]) / (nPoints+NoisePoint);
}
}
}
Reindex();
// Accumulate sums for mean calculation
for (p=0; p<nPoints; p++)
{
c = Class[p];
for(i=0; i<nDims; i++)
{
Mean[c*nDims + i] += Data[p*nDims + i];
}
}
// and normalize
for (cc=0; cc<nClustersAlive; cc++)
{
c = AliveIndex[cc];
for (i=0; i<nDims; i++) Mean[c*nDims + i] /= nClassMembers[c];
}
// Covariance matrix is quite big, and won't fit in the L1d cache
// (which is 16 or 32 k usually, corresponding to a matrix of about 64x64 or 90x90)
// so can probably improve performance by doing some sort of blocking here
// Accumulate sums for covariance calculation
// for (p=0; p<nPoints; p++)
// {
// c = Class[p];
// // calculate distance from mean
// for(i=0; i<nDims; i++)
// Vec2Mean[i] = Data[p*nDims + i] - Mean[c*nDims + i];
// for(i=0; i<nDims; i++)
// for(j=i; j<nDims; j++)
// Cov[c*nDims2 + i*nDims + j] += Vec2Mean[i] * Vec2Mean[j];
// }
if ((integer)AllVector2Mean.size() < nPoints*nDims)
{
//mem.add((nPoints*nDims-AllVector2Mean.size())*sizeof(scalar));
AllVector2Mean.resize(nPoints*nDims);
}
vector< vector<integer> > PointsInClass(MaxPossibleClusters);
for(p=0; p<nPoints; p++)
{
c = Class[p];
PointsInClass[c].push_back(p);
for (i = 0; i < nDims; i++)
AllVector2Mean[p*nDims + i] = Data[p*nDims + i] - Mean[c*nDims + i];
}
if (UseDistributional)
{
// Compute the cluster masks, used below to optimise the computation
ComputeClusterMasks();
// Empty the dynamic covariance matrices (we will fill it up as we go)
DynamicCov.clear();
for (cc = 0; cc<nClustersAlive; cc++)
{
c = AliveIndex[cc];
vector<integer> &PointsInThisClass = PointsInClass[c];
vector<integer> &CurrentUnmasked = ClusterUnmaskedFeatures[c];
vector<integer> &CurrentMasked = ClusterMaskedFeatures[c];
DynamicCov.push_back(BlockPlusDiagonalMatrix(CurrentMasked, CurrentUnmasked));
BlockPlusDiagonalMatrix &CurrentCov = DynamicCov.back();
if (CurrentUnmasked.size() == 0)
continue;
//// Correct version for dynamic cov matrix
//for (integer q = 0; q < (integer)PointsInThisClass.size(); q++)
//{
// p = PointsInThisClass[q];
// for (integer ii = 0; ii < (integer)CurrentUnmasked.size(); ii++)
// {
// i = CurrentUnmasked[ii];
// for (integer jj = 0; jj < (integer)CurrentUnmasked.size(); jj++)
// {
// j = CurrentUnmasked[jj];
// //Cov[c*nDims2 + i*nDims + j] += AllVector2Mean[p*nDims + i] * AllVector2Mean[p*nDims + j];
// CurrentCov.Block[ii*CurrentCov.NumUnmasked + jj] += AllVector2Mean[p*nDims + i] * AllVector2Mean[p*nDims + j];
// }
// }
//}
// Fast version for dynamic cov matrix
const integer npoints = (integer)PointsInThisClass.size();
const integer nunmasked = (integer)CurrentUnmasked.size();
if (npoints > 0 && nunmasked > 0)
{
const integer * __restrict pitc = &(PointsInThisClass[0]);
const integer * __restrict cu = &(CurrentUnmasked[0]);
for (integer q = 0; q < npoints; q++)
{
const integer p = pitc[q];
const scalar * __restrict av2mp = &(AllVector2Mean[p*nDims]);
for (integer ii = 0; ii < nunmasked; ii++)
{
const integer i = cu[ii];
const scalar av2mp_i = av2mp[i];
scalar * __restrict row = &(CurrentCov.Block[ii*nunmasked]);
for (integer jj = 0; jj < nunmasked; jj++)
{
const integer j = cu[jj];
//Cov[c*nDims2 + i*nDims + j] += AllVector2Mean[p*nDims + i] * AllVector2Mean[p*nDims + j];
row[jj] += av2mp_i * av2mp[j];
}
}
}
}
// Correct version
//for (integer q = 0; q < (integer)PointsInThisClass.size(); q++)
//{
// p = PointsInThisClass[q];
// for (integer ii = 0; ii < (integer)CurrentUnmasked.size(); ii++)
// {
// i = CurrentUnmasked[ii];
// for (integer jj = 0; jj < (integer)CurrentUnmasked.size(); jj++)
// {
// j = CurrentUnmasked[jj];
// Cov[c*nDims2 + i*nDims + j] += AllVector2Mean[p*nDims + i] * AllVector2Mean[p*nDims + j];
// }
// }
//}
// Faster version (equivalent)
// Doesn't make any use of cache structure, but no need to upgrade now because
// we will move to a sparse block matrix structure that will make this more
// natural
/*
const integer * __restrict cu = &(CurrentUnmasked[0]);
const integer ncu = (integer)CurrentUnmasked.size();
scalar * __restrict cov_c = &(Cov[c*nDims2]);
const integer * __restrict pitc = &(PointsInThisClass[0]);
const integer npitc = (integer)PointsInThisClass.size();
const scalar * __restrict av2m = &(AllVector2Mean[0]);
for (integer q = 0; q < npitc; q++)
{
const integer p = pitc[q];
const scalar * __restrict av2m_p = av2m + p*nDims;
for (integer ii = 0; ii < ncu; ii++)
{
const integer i = cu[ii];
const scalar av2m_p_i = av2m_p[i];
scalar * __restrict cov_c_i = cov_c + i*nDims;
for (integer jj = 0; jj < ncu; jj++)
{
const integer j = cu[jj];
cov_c_i[j] += av2m_p_i*av2m_p[j];
//Cov[c*nDims2 + i*nDims + j] += AllVector2Mean[p*nDims + i] * AllVector2Mean[p*nDims + j];
}
}
}
*/
}
}
else
{
// I think this code gives wrong results (but only slightly) (DFMG: 2014/10/13)
for (c = 0; c < MaxPossibleClusters; c++)
{
vector<integer> &PointsInThisClass = PointsInClass[c];
SafeArray<scalar> safeCov(Cov, c*nDims2, "safeCovMStep");
for (integer iblock = 0; iblock < nDims; iblock += COVARIANCE_BLOCKSIZE)
{
for (integer jblock = iblock; jblock < nDims; jblock += COVARIANCE_BLOCKSIZE)
{
for (integer q = 0; q < (integer)PointsInThisClass.size(); q++)
{
p = PointsInThisClass[q];
scalar *cv2m = &AllVector2Mean[p*nDims];
for (i = iblock; i < MIN(nDims, iblock + COVARIANCE_BLOCKSIZE); i++)
{
scalar cv2mi = cv2m[i];
integer jstart;
if (jblock != iblock)
jstart = jblock;
else
jstart = i;
scalar *covptr = &safeCov[i*nDims + jstart];
scalar *cv2mjptr = &cv2m[jstart];
//scalar *cv2mjend = cv2m+MIN(nDims, jblock+COVARIANCE_BLOCKSIZE);
//for(j=jstart; j<MIN(nDims, jblock+COVARIANCE_BLOCKSIZE); j++)
//for(; cv2mjptr!=cv2mjend;)
for (j = MIN(nDims, jblock + COVARIANCE_BLOCKSIZE) - jstart; j; j--)
*covptr++ += cv2mi*(*cv2mjptr++);
}
}
}
}
}
}
if(UseDistributional)
{
for(cc=0; cc<nClustersAlive; cc++)
{
c = AliveIndex[cc];
vector<integer> &PointsInThisClass = PointsInClass[c];
integer NumPointsInThisClass = PointsInThisClass.size();
BlockPlusDiagonalMatrix &CurrentCov = DynamicCov[cc];
for (integer ii = 0; ii<CurrentCov.NumUnmasked; ii++)
{
i = (*CurrentCov.Unmasked)[ii];
scalar ccf = 0.0; // class correction factor
for (integer q = 0; q<NumPointsInThisClass; q++)
{
p = PointsInThisClass[q];
ccf += CorrectionTerm[p*nDims + i];
}
CurrentCov.Block[ii*CurrentCov.NumUnmasked + ii] += ccf;
}
for (integer ii = 0; ii<CurrentCov.NumMasked; ii++)
{
i = (*CurrentCov.Masked)[ii];
scalar ccf = 0.0; // class correction factor
for (integer q = 0; q<NumPointsInThisClass; q++)
{
p = PointsInThisClass[q];
ccf += CorrectionTerm[p*nDims + i];
}
CurrentCov.Diagonal[ii] += ccf;
}
}
// Add a diagonal matrix of Noise variances to the covariance matrix for renormalization
for (cc = 0; cc < nClustersAlive; cc++)
{
c = AliveIndex[cc];
BlockPlusDiagonalMatrix &CurrentCov = DynamicCov[cc];
for (integer ii = 0; ii < CurrentCov.NumUnmasked; ii++)
CurrentCov.Block[ii*CurrentCov.NumUnmasked + ii] += priorPoint*NoiseVariance[(*CurrentCov.Unmasked)[ii]];
for (integer ii = 0; ii < CurrentCov.NumMasked; ii++)
CurrentCov.Diagonal[ii] += priorPoint*NoiseVariance[(*CurrentCov.Masked)[ii]];
}
}
// and normalize
if(UseDistributional)
{
for (cc=0; cc<nClustersAlive; cc++)
{
c = AliveIndex[cc];
BlockPlusDiagonalMatrix &CurrentCov = DynamicCov[cc];
scalar factor = 1.0 / (nClassMembers[c] + priorPoint - 1);
for (i = 0; i < (integer)CurrentCov.Block.size(); i++)
CurrentCov.Block[i] *= factor;
for (i = 0; i < (integer)CurrentCov.Diagonal.size(); i++)
CurrentCov.Diagonal[i] *= factor;
}
}
else
{ //For original KlustaKwik classical EM
for (cc=0; cc<nClustersAlive; cc++)
{
c = AliveIndex[cc];
for(i=0; i<nDims; i++)
for(j=i; j<nDims; j++)
Cov[c*nDims2 + i*nDims + j] /= (nClassMembers[c]-1);
}
}
// That's it!
// Diagnostics
if (Debug)
{
for (cc=0; cc<nClustersAlive; cc++)
{
c = AliveIndex[cc];
Output("Class %d - Weight %.2g\n", (int)c, Weight[c]);
Output("Mean: ");
MatPrint(stdout, &Mean.front() + c*nDims, 1, nDims);
if (!UseDistributional)
{
Output("\nCov:\n");
MatPrint(stdout, &Cov.front() + c*nDims2, nDims, nDims);
}
Output("\n");
}
}
}
// E-step. Calculate Log Probs for each point to belong to each living class
// will delete a class if covariance matrix is singular
// also counts number of living classes
void KK::EStep()
{
integer p, c, cc, i;
integer nSkipped;
scalar LogRootDet; // log of square root of covariance determinant
scalar Mahal; // Mahalanobis distance of point from cluster center
scalar correction_factor = (scalar)1; // for partial correction in distributional step
scalar InverseClusterNorm;
vector<scalar> Chol(nDims2); // to store choleski decomposition
vector<scalar> Vec2Mean(nDims); // stores data point minus class mean
vector<scalar> Root(nDims); // stores result of Chol*Root = Vec
vector<scalar> InvCovDiag;
if(UseDistributional)
InvCovDiag.resize(nDims);
SafeArray<scalar> safeChol(Chol, "safeChol");
SafeArray<scalar> safeVec2Mean(Vec2Mean, "safeVec2Mean");
SafeArray<scalar> safeRoot(Root, "safeRoot");
SafeArray<scalar> safeInvCovDiag(InvCovDiag, "safeInvCovDiag");
nSkipped = 0;
if (Debug) {Output("Entering Unmasked Estep \n");}
// start with cluster 0 - uniform distribution over space
// because we have normalized all dims to 0...1, density will be 1.
vector<integer> NumberInClass(MaxPossibleClusters); // For finding number of points in each class
for (p=0; p<nPoints; p++)
{
LogP[p*MaxPossibleClusters + 0] = (float)-log(Weight[0]);
integer ccc = Class[p];
NumberInClass[ccc]++;
}
BlockPlusDiagonalMatrix *CurrentCov;
BlockPlusDiagonalMatrix *CholBPD = NULL;
for (cc = 1; cc<nClustersAlive; cc++)
{
c = AliveIndex[cc];
// calculate cholesky decomposition for class c
integer chol_return;
if (UseDistributional)
{
CurrentCov = &(DynamicCov[cc]);
if (CholBPD)
{
delete CholBPD;
CholBPD = NULL;
}
CholBPD = new BlockPlusDiagonalMatrix(*(CurrentCov->Masked), *(CurrentCov->Unmasked));
chol_return = BPDCholesky(*CurrentCov, *CholBPD);
//if (MinMaskOverlap>0)
//{
// // compute the norm of the cluster mask (used for skipping points)
// const scalar * __restrict cm = &(ClusterMask[c*nDims]);
// scalar ClusterNorm = 0.0;
// for (i = 0; i < nDims; i++)
// {
// scalar m = cm[i];
// //if (m > ClusterNorm)
// // ClusterNorm = m;
// ClusterNorm += m*m;
// }
// //InverseClusterNorm = 1.0 / ClusterNorm;
// InverseClusterNorm = 1.0 / sqrt(ClusterNorm);
// //InverseClusterNorm = sqrt((scalar)nDims) / sqrt(ClusterNorm);
//}
}
else
{
SafeArray<scalar> safeCov(Cov, c*nDims2, "safeCov");
chol_return = Cholesky(safeCov, safeChol, nDims);
}
if(chol_return)
{
// If Cholesky returns 1, it means the matrix is not positive definite.
// So kill the class.
// Cholesky is defined in linalg.cpp
Output("Unmasked E-step: Deleting class %d (%d points): covariance matrix is singular \n", (int)c, (int)NumberInClass[c]);
ClassAlive[c] = 0;
continue;
}
// LogRootDet is given by log of product of diagonal elements
if (UseDistributional)
{
LogRootDet = 0;
for (integer ii = 0; ii < CholBPD->NumUnmasked; ii++)
LogRootDet += log(CholBPD->Block[ii*CholBPD->NumUnmasked + ii]);
for (integer ii = 0; ii < CholBPD->NumMasked; ii++)
LogRootDet += log(CholBPD->Diagonal[ii]);
}
else
{
LogRootDet = 0;
for (i = 0; i < nDims; i++)
LogRootDet += log(Chol[i*nDims + i]);
}
// if distributional E step, compute diagonal of inverse of cov matrix
if(UseDistributional)
{
vector<scalar> BasisVector(nDims);
SafeArray<scalar> safeBasisVector(BasisVector, "BasisVector");
for(integer i=0; i<nDims; i++)
safeBasisVector[i] = (scalar)0;
for(integer i=0; i<nDims; i++)
{
safeBasisVector[i] = (scalar)1;
// calculate Root vector - by Chol*Root = BasisVector
BPDTriSolve(*CholBPD, safeBasisVector, safeRoot);
// add half of Root vector squared to log p
scalar Sii = (scalar)0;
for(integer j=0; j<nDims; j++)
Sii += Root[j]*Root[j];
safeInvCovDiag[i] = Sii;
safeBasisVector[i] = (scalar)0;
}
}
for(p=0; p<nPoints; p++)
{
// to save time -- only recalculate if the last one was close
if (
!FullStep
&& (Class[p] == OldClass[p])
&& (LogP[p*MaxPossibleClusters+c] - LogP[p*MaxPossibleClusters+Class[p]] > DistThresh)
)
{
nSkipped++;
continue;
}
// to save time, skip points with mask overlap below threshold
if (MinMaskOverlap > 0)
{
// compute dot product of point mask with cluster mask
const scalar * __restrict PointMask = &(FloatMasks[p*nDims]);
const scalar * __restrict cm = &(ClusterMask[c*nDims]);
scalar dotprod = 0.0;
//// InverseClusterNorm is computed above, uncomment it if you uncomment any of this
//for (i = 0; i < nDims; i++)
//{
// dotprod += cm[i] * PointMask[i] * InverseClusterNorm;
// if (dotprod >= MinMaskOverlap)
// break;
//}
const integer NumUnmasked = CurrentCov->NumUnmasked;
if (NumUnmasked)
{
const integer * __restrict cu = &((*(CurrentCov->Unmasked))[0]);
for (integer ii = 0; ii < NumUnmasked; ii++)
{
const integer i = cu[ii];
dotprod += PointMask[i];
if (dotprod >= MinMaskOverlap)
break;
}
}
//dotprod *= InverseClusterNorm;
if (dotprod < MinMaskOverlap)
{
nSkipped++;
continue;
}
}
// Compute Mahalanobis distance
Mahal = 0;
// calculate data minus class mean
//for (i = 0; i<nDims; i++)
// Vec2Mean[i] = Data[p*nDims + i] - Mean[c*nDims + i];
scalar * __restrict Data_p = &(Data[p*nDims]);
scalar * __restrict Mean_c = &(Mean[c*nDims]);
scalar * __restrict v2m = &(Vec2Mean[0]);
for (i = 0; i < nDims; i++)
v2m[i] = Data_p[i] - Mean_c[i];
// calculate Root vector - by Chol*Root = Vec2Mean
if (UseDistributional)
BPDTriSolve(*CholBPD, safeVec2Mean, safeRoot);
else
TriSolve(safeChol, safeVec2Mean, safeRoot, nDims);
// add half of Root vector squared to log p
for(i=0; i<nDims; i++)
Mahal += Root[i]*Root[i];
// if distributional E step, add correction term
if (UseDistributional)
{
scalar * __restrict ctp = &(CorrectionTerm[p*nDims]);
scalar * __restrict icd = &(InvCovDiag[0]);
scalar subMahal = 0.0;
for (i = 0; i < nDims; i++)
subMahal += ctp[i] * icd[i];
Mahal += subMahal*correction_factor;
}
// Score is given by Mahal/2 + log RootDet - log weight
LogP[p*MaxPossibleClusters + c] = Mahal/2
+ LogRootDet
- log(Weight[c])
+ (0.5*log(2 * M_PI))*nDims;
} // for(p=0; p<nPoints; p++)
} // for(cc=1; cc<nClustersAlive; cc++)
if (CholBPD)
delete CholBPD;
}
// Choose best class for each point (and second best) out of those living
void KK::CStep(bool allow_assign_to_noise)
{
integer p, c, cc, TopClass, SecondClass;
integer ccstart = 0;
if(!allow_assign_to_noise)
ccstart = 1;
scalar ThisScore, BestScore, SecondScore;
for (p=0; p<nPoints; p++)
{
OldClass[p] = Class[p];
BestScore = HugeScore;
SecondScore = HugeScore;
TopClass = SecondClass = 0;
for (cc=ccstart; cc<nClustersAlive; cc++)
{
c = AliveIndex[cc];
ThisScore = LogP[p*MaxPossibleClusters + c];
if (ThisScore < BestScore)
{
SecondClass = TopClass;
TopClass = c;
SecondScore = BestScore;
BestScore = ThisScore;
}
else if (ThisScore < SecondScore)
{
SecondClass = c;
SecondScore = ThisScore;
}
}
Class[p] = TopClass;
Class2[p] = SecondClass;
}
}
// Sometimes deleting a cluster will improve the score, when you take into account
// the BIC. This function sees if this is the case. It will not delete more than
// one cluster at a time.
void KK::ConsiderDeletion()
{
integer c, p, CandidateClass=0;
scalar Loss, DeltaPen;
vector<scalar> DeletionLoss(MaxPossibleClusters); // the increase in log P by deleting the cluster
if (Debug)
Output(" Entering ConsiderDeletion: ");
for(c=1; c<MaxPossibleClusters; c++)
{
if (ClassAlive[c]) DeletionLoss[c] = 0;
else DeletionLoss[c] = HugeScore; // don't delete classes that are already there
}
// compute losses by deleting clusters
vector<integer> NumberInClass(MaxPossibleClusters);
for(p=0; p<nPoints; p++)
{
DeletionLoss[Class[p]] += LogP[p*MaxPossibleClusters + Class2[p]] - LogP[p*MaxPossibleClusters + Class[p]];
integer ccc = Class[p];
NumberInClass[ccc]++; // For computing number of points in each class
}
// find class with smallest increase in total score
Loss = HugeScore;
if (UseDistributional) //For UseDistribution, we use the ClusterPenalty
{
for(c=1; c<MaxPossibleClusters; c++)
{
if ((DeletionLoss[c]-ClassPenalty[c])<Loss)
{
Loss = DeletionLoss[c]-ClassPenalty[c];
CandidateClass = c;
}
}
}// or in the case of fixed penalty find class with least to lose
else
{
for(c=1; c<MaxPossibleClusters; c++)
{
if (DeletionLoss[c]<Loss)
{
Loss = DeletionLoss[c];
CandidateClass = c;
}
}
}
// what is the change in penalty?
if(UseDistributional) //For the distributional algorithm we need to use the ClusterPenalty
DeltaPen = ClassPenalty[CandidateClass];
else
DeltaPen = Penalty(nClustersAlive) - Penalty(nClustersAlive-1);
//Output("cand Class %d would lose " SCALARFMT " gain is " SCALARFMT "\n", (int)CandidateClass, Loss, DeltaPen);
// is it worth it?
//06/12/12 fixing bug introduced which considered DeltaPen twice!
if (UseDistributional) //For the distributional algorithm we need to use the ClusterPenalty
{
if (Loss<0)
{
Output("Deleting Class %d (%d points): Lose " SCALARFMT " but Gain " SCALARFMT "\n", (int)CandidateClass, (int)NumberInClass[CandidateClass], DeletionLoss[CandidateClass], DeltaPen);
// set it to dead
ClassAlive[CandidateClass] = 0;
// re-allocate all of its points
for(p=0;p<nPoints; p++) if(Class[p]==CandidateClass) Class[p] = Class2[p];
// recompute class penalties
ComputeClassPenalties();
}
}
else
{
if (Loss<DeltaPen)
{
Output("Deleting Class %d (%d points): Lose " SCALARFMT " but Gain " SCALARFMT "\n", (int)CandidateClass, (int)NumberInClass[CandidateClass], DeletionLoss[CandidateClass], DeltaPen);
// set it to dead
ClassAlive[CandidateClass] = 0;
// re-allocate all of its points
for(p=0;p<nPoints; p++) if(Class[p]==CandidateClass) Class[p] = Class2[p];
// recompute class penalties
ComputeClassPenalties();
}
}
Reindex();
}
// LoadClu(CluFile)
void KK::LoadClu(char *CluFile)
{
FILE *fp;
integer p, c;
int val; // read in from %d
integer status;
fp = fopen_safe(CluFile, "r");
status = fscanf(fp, "%d", &nStartingClusters);
nClustersAlive = nStartingClusters;// -1;
for(c=0; c<MaxPossibleClusters; c++) ClassAlive[c]=(c<nStartingClusters);
for(p=0; p<nPoints; p++)
{
status = fscanf(fp, "%d", &val);
if (status==EOF) Error("Error reading cluster file");
Class[p] = val-1;
}