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gargantext
haskell-gargantext
Commits
0a103e61
Commit
0a103e61
authored
Mar 22, 2021
by
Guillaume Chérel
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[FEAT] Implements log distributional function with accelerate (
#50
).
parent
804f9027
Pipeline
#1421
failed with stage
Changes
2
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1
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2 changed files
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62 additions
and
2 deletions
+62
-2
Distributional.hs
...ntext/Core/Methods/Distances/Accelerate/Distributional.hs
+51
-2
Utils.hs
src/Gargantext/Core/Methods/Matrix/Accelerate/Utils.hs
+11
-0
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src/Gargantext/Core/Methods/Distances/Accelerate/Distributional.hs
View file @
0a103e61
...
@@ -45,7 +45,7 @@ module Gargantext.Core.Methods.Distances.Accelerate.Distributional
...
@@ -45,7 +45,7 @@ module Gargantext.Core.Methods.Distances.Accelerate.Distributional
-- import qualified Data.Foldable as P (foldl1)
-- import qualified Data.Foldable as P (foldl1)
-- import Debug.Trace (trace)
-- import Debug.Trace (trace)
import
Data.Array.Accelerate
import
Data.Array.Accelerate
as
A
import
Data.Array.Accelerate.Interpreter
(
run
)
import
Data.Array.Accelerate.Interpreter
(
run
)
import
Gargantext.Core.Methods.Matrix.Accelerate.Utils
import
Gargantext.Core.Methods.Matrix.Accelerate.Utils
import
qualified
Gargantext.Prelude
as
P
import
qualified
Gargantext.Prelude
as
P
...
@@ -115,8 +115,57 @@ distributional m' = run result
...
@@ -115,8 +115,57 @@ distributional m' = run result
result
=
termDivNan
z_1
z_2
result
=
termDivNan
z_1
z_2
logDistributional
::
Matrix
Int
->
Matrix
Double
logDistributional
m'
=
run
result
where
m
=
map
fromIntegral
$
use
m'
n
=
dim
m'
-- Scalar. Sum of all elements of m.
to
=
the
$
sum
(
flatten
m
)
-- Diagonal matrix with the diagonal of m.
d_m
=
(
.*
)
m
(
matrixIdentity
n
)
-- Size n vector. s = [s_i]_i
s
=
sum
((
.-
)
m
d_m
)
-- Matrix nxn. Vector s replicated as rows.
s_1
=
replicate
(
constant
(
Z
:.
All
:.
n
))
s
-- Matrix nxn. Vector s replicated as columns.
s_2
=
replicate
(
constant
(
Z
:.
n
:.
All
))
s
-- Matrix nxn. ss = [s_i * s_j]_{i,j}. Outer product of s with itself.
ss
=
(
.*
)
s_1
s_2
-- Matrix nxn. mi = [m_{i,j}]_{i,j} where
-- m_{i,j} = 0 if n_{i,j} = 0 or i = j,
-- m_{i,j} = log(to * n_{i,j} / s_{i,j}) otherwise.
mi
=
(
.*
)
(
matrixEye
n
)
(
map
(
lift1
(
\
x
->
cond
(
x
==
0
)
0
(
log
(
x
*
to
))))
((
./
)
m
ss
))
-- Tensor nxnxn. Matrix mi replicated along the 2nd axis.
w_1
=
replicate
(
constant
(
Z
:.
All
:.
n
:.
All
))
mi
-- Tensor nxnxn. Matrix mi replicated along the 1st axis.
w_2
=
replicate
(
constant
(
Z
:.
n
:.
All
:.
All
))
mi
-- Tensor nxnxn.
w'
=
zipWith
min
w_1
w_2
-- A predicate that is true when the input (i, j, k) satisfy
-- k /= i AND k /= j
k_diff_i_and_j
=
lift1
(
\
(
Z
:.
i
:.
j
:.
k
)
->
((
&&
)
((
/=
)
k
i
)
((
/=
)
k
j
)))
-- Matrix nxn.
sumMin
=
sum
(
condOrDefault
k_diff_i_and_j
0
w'
)
-- Matrix nxn. All columns are the same.
sumM
=
sum
(
condOrDefault
k_diff_i_and_j
0
w_1
)
result
=
termDivNan
sumMin
sumM
--
-- The distributional metric P(c) of @i@ and @j@ terms is: \[
-- The distributional metric P(c) of @i@ and @j@ terms is: \[
-- S_{MI} = \frac {\sum_{k \neq i,j ; MI_{ik} >0}^{} \min(MI_{ik},
-- S_{MI} = \frac {\sum_{k \neq i,j ; MI_{ik} >0}^{} \min(MI_{ik},
-- MI_{jk})}{\sum_{k \neq i,j ; MI_{ik}>0}^{}} \]
-- MI_{jk})}{\sum_{k \neq i,j ; MI_{ik}>0}^{}} \]
...
...
src/Gargantext/Core/Methods/Matrix/Accelerate/Utils.hs
View file @
0a103e61
...
@@ -123,6 +123,17 @@ matrixEye n' =
...
@@ -123,6 +123,17 @@ matrixEye n' =
diagNull
::
Num
a
=>
Dim
->
Acc
(
Matrix
a
)
->
Acc
(
Matrix
a
)
diagNull
::
Num
a
=>
Dim
->
Acc
(
Matrix
a
)
->
Acc
(
Matrix
a
)
diagNull
n
m
=
zipWith
(
*
)
m
(
matrixEye
n
)
diagNull
n
m
=
zipWith
(
*
)
m
(
matrixEye
n
)
-- Returns an N-dimensional array with the values of x for the indices where
-- the condition is true, 0 everywhere else.
condOrDefault
::
forall
sh
a
.
(
Shape
sh
,
Elt
a
)
=>
(
Exp
sh
->
Exp
Bool
)
->
Exp
a
->
Acc
(
Array
sh
a
)
->
Acc
(
Array
sh
a
)
condOrDefault
theCond
def
x
=
permute
const
zeros
filterInd
x
where
zeros
=
fill
(
shape
x
)
(
def
)
filterInd
ix
=
(
cond
(
theCond
ix
))
ix
ignore
-----------------------------------------------------------------------
-----------------------------------------------------------------------
_runExp
::
Elt
e
=>
Exp
e
->
e
_runExp
::
Elt
e
=>
Exp
e
->
e
_runExp
e
=
indexArray
(
run
(
unit
e
))
Z
_runExp
e
=
indexArray
(
run
(
unit
e
))
Z
...
...
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