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xerus
xerus
Commits
d82eb788
Commit
d82eb788
authored
Jul 04, 2017
by
Sebastian Wolf
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Chnage Tensor from data constructor for GCC 7
parent
bb2379b9
Pipeline
#765
passed with stages
in 6 minutes and 49 seconds
Changes
3
Pipelines
1
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3 changed files
with
70 additions
and
40 deletions
+70
-40
include/xerus/algorithms/randomSVD.h
include/xerus/algorithms/randomSVD.h
+68
-38
include/xerus/tensor.h
include/xerus/tensor.h
+1
-1
src/xerus/tensor.cpp
src/xerus/tensor.cpp
+1
-1
No files found.
include/xerus/algorithms/randomSVD.h
View file @
d82eb788
...
...
@@ -28,44 +28,74 @@
namespace
xerus
{
TTTensor
randomTTSVD
(
const
Tensor
&
_x
,
const
std
::
vector
<
size_t
>&
_ranks
)
{
std
::
random_device
rd
;
std
::
mt19937
rnd
(
rd
());
std
::
normal_distribution
<
double
>
dist
(
0
,
1
);
const
size_t
d
=
_x
.
degree
();
TTTensor
u
(
d
);
Tensor
b
=
_x
;
for
(
long
j
=
d
;
j
--
;
j
>=
2
)
{
std
::
vector
<
size_t
>
gDims
(
_b
.
dimensions
.
cbegin
(),
_b
.
dimensions
.
begin
()
+
(
d
-
1
));
Tensor
g
(
gDims
,
Tensor
::
Representation
::
Sparse
);
const
auto
&
data
=
b
.
get_unsanitized_sparse_data
();
for
(
const
auto
&
entry
:
data
)
{
auto
pos
=
Tensor
::
position_to_multiIndex
(
entry
.
first
,
b
.
dimensions
);
pos
.
pop_back
();
g
[
pos
]
=
dist
(
rnd
);
}
Tensor
a
;
contract
(
a
,
g
,
false
,
b
,
false
,
j
-
1
);
Tensor
R
,
Q
;
calculate_rq
(
R
,
Q
,
a
,
1
);
u
.
set_component
(
j
,
Q
);
if
(
j
==
d
)
{
contract
(
b
,
b
,
false
,
Q
,
true
,
1
);
}
else
{
contract
(
b
,
b
,
false
,
Q
,
true
,
2
);
}
}
u
.
set_component
(
1
,
b
);
}
// TTTensor randomTTSVD(const Tensor& _x, const std::vector<size_t>& _ranks, const std::vector<size_t>& _oversampling) {
// std::normal_distribution<double> dist(0, 1);
//
// const size_t d = _x.degree();
// TTTensor u(d);
// Tensor b = _x;
//
// for(size_t j = d; j >= 2; --j) {
// const size_t s = _ranks[j-2] + _oversampling[j-2];
//
// const std::vector<size_t> mixDims(b.dimensions.cbegin(), b.dimensions.cbegin()+(j-1));
//
// std::vector<size_t> outDims({s});
// outDims.insert(outDims.end(), b.dimensions.cbegin()+(j-1), b.dimensions.cend());
//
// Tensor a(outDims, Tensor::Representation::Sparse, Tensor::Initialisation::Zero);
//
// if(b.is_sparse()) {
// const size_t staySize = misc::product(b.dimensions, j-1, b.dimensions.size());
//
// std::map<size_t, std::vector<value_t>> usedG;
//
// const auto& data = b.get_sparse_data();
// for(const auto& entry : data) {
// const size_t pos = entry.first/staySize;
// const size_t outPos = entry.first%staySize;
//
// auto& gEntry = usedG[pos];
// if(gEntry.empty()) {
// gEntry.reserve(s);
// for(size_t k = 0; k < s; ++k) {
// gEntry.push_back(dist(xerus::misc::randomEngine));
// }
// }
//
// for(size_t k = 0; k < s; ++k) {
// a[outPos+k*staySize] += gEntry[k]*entry.second;
// }
// }
//
// } else {
// std::vector<size_t> gDims({s});
// gDims.insert(gDims.end(), mixDims.cbegin(), mixDims.cend());
// const Tensor g = Tensor::random(gDims, dist, xerus::misc::randomEngine);
// contract(a, g, false, b, false, j-1);
// }
//
//
// Tensor R, Q;
// calculate_rq(R, Q, a, 1);
//
//
// if(j == d) {
// contract(b, b, false, Q, true, 1);
// Q.reinterpret_dimensions(Q.dimensions | std::vector<size_t>({1}));
// u.set_component(j-1, Q);
// } else {
// contract(b, b, false, Q, true, 2);
// u.set_component(j-1, Q);
// }
// }
//
// b.reinterpret_dimensions(std::vector<size_t>({1}) | b.dimensions);
// u.set_component(0, b);
//
// u.round(_ranks);
//
// return u;
}
include/xerus/tensor.h
View file @
d82eb788
...
...
@@ -159,7 +159,7 @@ namespace xerus {
* @param _dimensions the dimensions of the new tensor.
* @param _data inital dense data in row-major order.
*/
explicit
Tensor
(
DimensionTuple
_dimensions
,
std
::
unique_ptr
<
value_t
[]
>&&
_data
);
explicit
Tensor
(
DimensionTuple
_dimensions
,
std
::
unique_ptr
<
value_t
>&&
_data
);
/**
...
...
src/xerus/tensor.cpp
View file @
d82eb788
...
...
@@ -65,7 +65,7 @@ namespace xerus {
}
Tensor
::
Tensor
(
DimensionTuple
_dimensions
,
std
::
unique_ptr
<
value_t
[]
>&&
_data
)
Tensor
::
Tensor
(
DimensionTuple
_dimensions
,
std
::
unique_ptr
<
value_t
>&&
_data
)
:
dimensions
(
std
::
move
(
_dimensions
)),
size
(
misc
::
product
(
dimensions
)),
representation
(
Representation
::
Dense
),
denseData
(
std
::
move
(
_data
))
{
REQUIRE
(
size
!=
0
,
"May not create tensors with an dimension == 0."
);
}
...
...
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