working on tensor
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23
tensor.cc
23
tensor.cc
@ -23,6 +23,29 @@
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namespace LA {
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template<typename T>
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LA_largeindex Tensor<T>::index(const SUPERINDEX &I)
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{
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//check index structure and ranges
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#ifndef DEBUG
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if(I.size()!=shape.size()) laerror("mismatch in the number of tensor index groups");
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for(int i=0; i<I.size; ++i)
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{
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if(shape[i].number!=I[i].size()) {std::cerr<<"error in index group no. "<<i<<std::endl; laerror("mismatch in the size of tensor index group");}
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for(int j=0; j<shape[i].number; ++j)
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{
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if(I[i][j] <shape[i].offset || I[i][j] >= shape[i].offset+shape[i].size())
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{
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std::cerr<<"error in index group no. "<<i<<" index no. "<<j<<std::endl;
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laerror("tensor index out of range");
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}
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}
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}
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#endif
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//@@@@@@@@@
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}
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26
tensor.h
26
tensor.h
@ -17,7 +17,7 @@
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*/
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//a simple tensor class with arbitrary summetry of index subgroups
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//a simple tensor class with arbitrary symmetry of index subgroups
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//stored in an efficient way
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//presently only a rudimentary implementation
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//presently limited to 2G data size due to NRVec - maybe use a typedef LA_index
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@ -28,6 +28,7 @@
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#define _TENSOR_H
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#include <stdint.h>
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#include <cstdarg>
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#include "vec.h"
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#include "miscfunc.h"
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@ -44,20 +45,41 @@ LA_index offset; //indices start at
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LA_index size; //indices span this range
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} INDEXGROUP;
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typedef NRVec<LA_index> FLATINDEX; //all indices but in a single vector
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typedef NRVec<NRVec<LA_index> > SUPERINDEX; //all indices in the INDEXGROUP structure
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template<typename T>
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class Tensor {
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NRVec<indexgroup> shape;
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NRVec<T> data;
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private:
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LA_largeindex index(const SUPERINDEX &I); //map the tensor indices to the position in data
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LA_largeindex index(const FLATINDEX &I); //map the tensor indices to the position in data
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LA_largeindex vindex(int i1,va_list args); //map list of indices to the position in data @@@must call va_end
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public:
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Tensor() {};
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Tensor(const NRVec<indexgroup> &s) : shape(s), data((int)size()) {data.clear();};
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int rank() const; //is computed from shape
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LA_largeindex size() const; //expensive, is computed from shape
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void copyonwrite() {shape.copyonwrite(); data.copyonwrite();};
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//@@@operator() lhs and rhs both via vararg a via superindex of flat and nested types, get/put to file, stream i/o
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inline T& operator()(const SUPERINDEX &I) {return data[index(I)];};
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inline const T& operator()(const SUPERINDEX &I) const {return data[index(I)];};
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inline T& operator()(const FLATINDEX &I) {return data[index(I)];};
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inline const T& operator()(const FLATINDEX &I) const {return data[index(I)];};
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inline T& operator()(int i1...) {va_list args; va_start(args,i1); return data[vindex(i1,args)];};
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inline const T& operator()(int i1...) const {va_list args; va_start(args,i1); return data[vindex(i1,args)];};
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//@@@TODO - unwinding to full size in a specified index
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//@@@TODO - contractions - basic and efficient
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//@@@TODO get/put to file, stream i/o
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};
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template<typename T>
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int Tensor<T>:: rank() const
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{
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