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Tucked tested on compressed tensors, flattening implemented

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Jiri Pittner
2025-10-23 15:29:48 +02:00
parent a3ace7c757
commit cd09d93c27
3 changed files with 151 additions and 10 deletions
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11
tensor.h
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@@ -40,14 +40,16 @@
//TODO:
//@@@!!!!!! - implement index names and contractions, unwinding etc. by named index list
//@@@index names flat or in groups ?
//
//@@@contraction inside one tensor - compute resulting shape, loopover the shape, create index into the original tensor + loop over the contr. index, do the summation, store result
//@@@ will need to store vector of INDEX to the original tensor for the result's flatindex
//@@@ will not be particularly efficient
//
//@@@conversions to/from fourindex
//@@@conversions to/from fourindex, optional negarive rande for beta spin handling
//@@@ optional distinguish covariant and contravariant check in contraction
//
//@@@!!!!!!!!!!!const loopover and grouploopover
//maybe const loopover and grouploopover to avoid problems with shallowly copied tensors
//
//@@@?general permutation of individual indices - check the indices in sym groups remain adjacent, calculate result's shape, loopover the result and permute using unwind_callback
//
@@ -162,6 +164,7 @@ public:
NRMat<T> matrix() const {return NRMat<T>(data,data.size()/groupsizes[0],groupsizes[0],0);}; //reinterpret as matrix with column index being the tensor's leftmost index group (typically the unwound single index)
bool is_flat() const {for(int i=0; i<shape.size(); ++i) if(shape[i].number>1) return false; return true;};
bool is_compressed() const {for(int i=0; i<shape.size(); ++i) if(shape[i].number>1&&shape[i].symmetry!=0) return true; return false;};
void clear() {data.clear();};
int rank() const {return myrank;};
int calcrank(); //is computed from shape
@@ -240,9 +243,11 @@ public:
// Note that *this tensor can be e.g. antisymmetric while rhs is not and is being antisymmetrized by the PermutationAlgebra
// The efficiency is not optimal, even when avoiding the outer product, the calculation is done indexing element by element
// More efficient would be applying permutation algebra symbolically and efficiently computing term by term
void split_index_group(int group); //formal split of a non-symmetric index group WITHOUT the need for data reorganization
void split_index_group(int group); //formal in-place split of a non-symmetric index group WITHOUT the need for data reorganization
void merge_adjacent_index_groups(int groupfrom, int groupto); //formal merge of non-symmetric index groups WITHOUT the need for data reorganization
Tensor merge_index_groups(const NRVec<int> &groups) const;
Tensor flatten(int group= -1) const; //split and uncompress a given group or all of them
NRVec<NRMat<T> > Tucker(typename LA_traits<T>::normtype thr=1e-12, bool inverseorder=true); //HOSVD-Tucker decomposition, return core tensor in *this, flattened
Tensor inverseTucker(const NRVec<NRMat<T> > &x, bool inverseorder=true) const; //rebuild the original tensor from Tucker
};