int matrix_eigMaxSym_float (float* l,float* v,float* M,size_t n){
size_t i,k;
float r;
float* y = (float*)malloc(n*sizeof(float));
*l = 1;
for (i=0;i<n;i++)
v[i] = 1.0/sqrt(n);
for (k=0;k<500;k++)
{
/* y = A'*v */
for (i=0;i<n;i++)
{
y[i] = vector_ps_float (v, &M[i*n], n);
}
/* l = v'*y */
*l = vector_ps_float (v, y, n);
if (fabs(*l) < 1E-7) {
free (y);
return 0;
}
/* r = ||y - t*v||^2 */
vector_linear_float (v,y,-(*l),v,n);
r = vector_ps_float (v, v, n);
r = fabs(r/(*l));
/*printf ("l=%f,r=%e\n",*l,r); */
if (r < 1E-14){
vector_cpym_float (v, 1.0/vector_n2_float (y, n), y, n);
break;
}
/* r = ||y||^2 */
r = vector_n2p2_float (y, n);
if (r < 1E-7) {
free (y);
return 0;
}
/* v = y/sqrt(r) */
r = 1.0/sqrt(r);
vector_cpym_float (v, r, y, n);
}
/* cherche la plus grande valeur de v; */
/* si elle est negative, inverse v; */
r = -1;
for (i=0;i<n;i++)
if (v[i]<r)
r = v[i];
if (r < 0)
{
for (i=0;i<n;i++)
v[i] = -v[i];
}
free (y);
return 1;
}
void SparseVlatCluster::add(const float* feature, size_t featureDim){
size_t meanDim = getTensorDim(1);
if (featureDim != meanDim)
retinThrowException2("Invalid feature dim %d != %d", featureDim, meanDim);
vector<float> featureCentred(meanDim);
const float* mean = getMeanTensor(1);
if (mean)
vector_linear_float(&featureCentred[0], feature, -1, mean, meanDim);
else
memcpy(&featureCentred[0],feature,meanDim*sizeof(float));
vector<float> featureDual(meanDim);
matrix_CpAtB_float(&featureDual[0], &U[0], &featureCentred[0], meanDim, meanDim, 1);
if(mainOrder == 1 || sparsityOn == SparseVlatCluster::sparsityOnDiag){
for(size_t i = 0 ; i < meanDim ; i++)
varDual[i] += featureDual[i]*featureDual[i];
}
else if(sparsityOn == SparseVlatCluster::sparsityOnFull){
vector<float> temp(meanDim*meanDim);
matrix_Cpaat_float(&temp[0], &featureDual[0], meanDim);
for(size_t i = 0 ; i < meanDim*meanDim ; i++)
temp[i] *= temp[i];
vector_add_float (&varDual[0], &featureDual[0], meanDim*meanDim);
}
vector<pair<size_t, float> > sortTable;
if (mainOrder == 1) {
if(sparsityOn == sparsityMaxValues){
sortTable.resize(meanDim);
for(size_t i = 0 ; i < meanDim ; i++){
sortTable[i].first = i;
sortTable[i].second = featureDual[i];
}
sort(sortTable.begin(), sortTable.end(), myfunction);
for(size_t i = sparsityDim ; i < meanDim ; i++){
featureDual[sortTable[i].first] = 0;
}
}
vector_add_float (&vlat[0], &featureDual[0], meanDim);
}
else if (mainOrder == 2) {
if(sparsityOn == sparsityMaxValues){
vector<float> temp(meanDim*meanDim);
memset(&temp[0],0,meanDim*meanDim*sizeof(float));
matrix_Cpaat_float(&temp[0], &featureDual[0], meanDim);
sortTable.resize(meanDim*meanDim);
for(size_t i = 0 ; i < meanDim*meanDim ; i++){
sortTable[i].first = i;
sortTable[i].second = temp[i];
}
sort(sortTable.begin(), sortTable.end(), myfunction);
for(size_t i = sparsityDim ; i < meanDim*meanDim ; i++){
temp[sortTable[i].first] = 0;
}
vector_add_float (&vlat[0], &temp[0], meanDim*meanDim);
}
else
matrix_Cpaat_float(&vlat[0], &featureDual[0], meanDim);
}
counter++;
}
