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Restructured the project

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Joshua Moerman 2025-04-14 20:38:53 +02:00
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EUPL version.

40
README.md
View file

@ -4,50 +4,38 @@ mealy-decompose
Tools to investigate decomposition of Mealy machines, aka finite state
machines (FSMs). Notable entry points are:
* `app/Main.hs` is a heuristic to decompose finite state machines into
* `hs/app/Main.hs` is a heuristic to decompose finite state machines into
multiple components, based on their outputs.
* `other/decompose_fsm_optimise.py` does the same, but optimally and with a
* `py/decompose_fsm_optimise.py` does the same, but optimally and with a
SAT solver. This can only handle state spaces of at most a few hundred
states.
* `app/RandomGen.hs` for generating FSMs which are decomposable.
* `hs/app/RandomGen.hs` for generating FSMs which are decomposable.
## How to run
The the haskell tools (tested with ghc 9.2.8 and ghc 9.10.1):
```
cabal run mealy-decompose -- <path-to-fsm>
cabal run mealy-decompose-main -- <path-to-fsm>
```
For the python tools (tested with python 3.12):
```
pip3 install python-sat
python3 decompose_fsm_optimise.py -h
pip install -r py/requirements.txt
python py/decompose_fsm_optimise.py -h
```
## Notes on `copar`
## Copyright notice and license
In the first versions of this tool, I used the `copar` library:
[CoPaR (The Coalgebraic Partition Refiner)](https://git8.cs.fau.de/software/copar).
This is a great library and in particular very fast. Despite that, I switched
to my own partition refinement implementations because of the reasons below.
The last commit to use `copar` is `2b8b79a4`.
(c) 2024-2025 Joshua Moerman, Open Universiteit, licensed under the EUPL
(European Union Public License). You may find the license in the LICENSE file.
* I needed witnesses for some tasks. So I not only construct a partition,
but also a splitting tree, where are track all the actions needed to
separate states.
If you want to use this library and find the license not suitable for you,
then please do get in touch.
* I prefer to use the actual strings (or other data type) for the states,
inputs and outputs as opposed to `Int`s. This makes it easier to debug, and
less error-prone, as one cannot mix these entities anymore.
* `copar` comes with many dependencies, and it was a bit tricky to get the
versioning correct.
## Copyright
(c) 2024 Joshua Moerman, Open Universiteit
```
SPDX-License-Identifier: EUPL-1.2
```

8
cabal.project Normal file
View file

@ -0,0 +1,8 @@
packages: hs
package *
optimization: True
package mealy-decompose
profiling: True
ghc-options: -Wall -fexpose-all-unfoldings -fspecialise-aggressively

18
docs/copar.md Normal file
View file

@ -0,0 +1,18 @@
# Notes on the `copar` library
In the first versions of this tool, I used the `copar` library:
[CoPaR (The Coalgebraic Partition Refiner)](https://git8.cs.fau.de/software/copar).
This is a great library and in particular very fast. Despite that, I switched
to my own partition refinement implementations because of the reasons below.
The last commit to use `copar` is `2b8b79a4`.
* I needed witnesses for some tasks. So I not only construct a partition,
but also a splitting tree, where are track all the actions needed to
separate states.
* I prefer to use the actual strings (or other data type) for the states,
inputs and outputs as opposed to `Int`s. This makes it easier to debug, and
less error-prone, as one cannot mix these entities anymore.
* `copar` comes with many dependencies, and it was a bit tricky to get the
versioning correct.

15
examples/RKMachine_3.dot Normal file
View file

@ -0,0 +1,15 @@
digraph RKMachine_3 {
s0Up -> s1Up [label="a/0"]
s0Up -> s0Dn [label="b/0"]
s1Up -> s2Up [label="a/1"]
s1Up -> s1Dn [label="b/0"]
s2Up -> s0Up [label="a/2"]
s2Up -> s2Dn [label="b/0"]
s0Dn -> s2Dn [label="a/0"]
s0Dn -> s0Up [label="b/0"]
s1Dn -> s0Dn [label="a/1"]
s1Dn -> s1Up [label="b/0"]
s2Dn -> s1Dn [label="a/2"]
s2Dn -> s2Up [label="b/0"]
}

19
examples/RKMachine_4.dot Normal file
View file

@ -0,0 +1,19 @@
digraph RKMachine_4 {
s0Up -> s1Up [label="a/0"]
s0Up -> s0Dn [label="b/0"]
s1Up -> s2Up [label="a/1"]
s1Up -> s1Dn [label="b/0"]
s2Up -> s3Up [label="a/2"]
s2Up -> s2Dn [label="b/0"]
s3Up -> s0Up [label="a/3"]
s3Up -> s3Dn [label="b/0"]
s0Dn -> s3Dn [label="a/0"]
s0Dn -> s0Up [label="b/0"]
s1Dn -> s0Dn [label="a/1"]
s1Dn -> s1Up [label="b/0"]
s2Dn -> s1Dn [label="a/2"]
s2Dn -> s2Up [label="b/0"]
s3Dn -> s2Dn [label="a/3"]
s3Dn -> s3Up [label="b/0"]
}

0
app/LStarMain.hs → hs/app/LStarMain.hs
View file

0
app/Main.hs → hs/app/Main.hs
View file

0
app/Playground.hs → hs/app/Playground.hs
View file

0
app/RandomGen.hs → hs/app/RandomGen.hs
View file

10
mealy-decompose.cabal → hs/mealy-decompose.cabal
View file

@ -1,11 +1,11 @@
cabal-version: 2.2
name: mealy-decompose
version: 0.2.0.0
license: AGPL-3.0-only
license-file: LICENSE
version: 0.3.0.0
license: EUPL-1.2
license-file: ../LICENSE
author: Joshua Moerman
maintainer: joshua.moerman@ou.nl
copyright: (c) 2024 Joshua Moerman, Open Universiteit
copyright: (c) 2024-2025 Joshua Moerman, Open Universiteit
build-type: Simple
common stuff
@ -38,7 +38,7 @@ library
SplittingTree,
StateIdentifiers
executable mealy-decompose
executable mealy-decompose-main
import: stuff
hs-source-dirs: app
main-is: Main.hs

0
src/Bisimulation.hs → hs/src/Bisimulation.hs
View file

0
src/Data/Partition.hs → hs/src/Data/Partition.hs
View file

0
src/Data/Preorder.hs → hs/src/Data/Preorder.hs
View file

0
src/Data/Trie.hs → hs/src/Data/Trie.hs
View file

0
src/Data/UnionFind.hs → hs/src/Data/UnionFind.hs
View file

0
src/DotParser.hs → hs/src/DotParser.hs
View file

0
src/DotWriter.hs → hs/src/DotWriter.hs
View file

0
src/LStar.hs → hs/src/LStar.hs
View file

0
src/Mealy.hs → hs/src/Mealy.hs
View file

0
src/MealyRefine.hs → hs/src/MealyRefine.hs
View file

0
src/Merger.hs → hs/src/Merger.hs
View file

0
src/SplittingTree.hs → hs/src/SplittingTree.hs
View file

0
src/StateIdentifiers.hs → hs/src/StateIdentifiers.hs
View file

0
test/Main.hs → hs/test/Main.hs
View file

227
other/decompose_observation_table.py
View file

@ -1,227 +0,0 @@
from pysat.solvers import Solver
from pysat.card import CardEnc
from pysat.formula import IDPool
from pysat.formula import CNF
### Gebruik:
# Stap 1: pip3 install python-sat
# Stap 2: python3 decompose_observation_table.py
###################################
# Wat dingetjes over Mealy machines
# Voorbeeld: 2n states, input-alfabet 'a' en 'b', outputs [0...n-1]
def rick_koenders_machine(N):
transition_fun = {((n, False), 'a'): ((n + 1) % N, False) for n in range(N)}
transition_fun |= {(((n + 1) % N, True), 'a'): (n % N, True) for n in range(N)}
transition_fun |= {((n, b), 'b'): (n, not b) for b in [False, True] for n in range(N)}
output_fun = {((n, b), 'a'): n for b in [False, True] for n in range(N)}
output_fun |= {((n, b), 'b'): 0 for b in [False, True] for n in range(N)}
initial_state = (0, False)
inputs = ['a', 'b']
outputs = [n for n in range(N)]
return {'transition_fun': transition_fun, 'output_fun': output_fun, 'initial_state': initial_state, 'inputs': inputs, 'outputs': outputs}
def mealy_sem_q(machine, word, state):
if len(word) == 0:
return None
if len(word) == 1:
return machine['output_fun'][(state, word[0])]
else:
return mealy_sem_q(machine, word[1:], machine['transition_fun'][(state, word[0])])
def mealy_sem(machine, word):
return mealy_sem_q(machine, word, machine['initial_state'])
def print_table(cell, rs, cs):
first_col_size = max([len(str(r)) for r in rs])
col_size = 1 + max([len(str(c)) for c in cs])
print(''.rjust(first_col_size), end='')
for c in cs:
print(str(c).rjust(col_size), end='')
print('')
for r in rs:
print(str(r).rjust(first_col_size), end='')
for c in cs:
print(cell(r, c).rjust(col_size), end='')
print('')
################
# Voorbeeld data
machine = rick_koenders_machine(4) # 8 states
# L* table: Niet noodzakelijk volledig, werkt ook met minder data, maar ik
# weet niet wat voor garanties we dan kunnen geven. Het is sowieso maar de
# vraag of de kolommen voldoende zijn als we projecteren.
rows = ['', 'a', 'aa', 'aaa', 'aaaa', 'b', 'ab', 'aab', 'aaab']
cols = ['a', 'aa', 'aaa', 'aaaa', 'b', 'ab', 'ba', 'abab']
print_table(lambda r, c: str(mealy_sem(machine, r + c)), rows, cols)
# We zoeken 2 componenten met gezamelijke grootte 6 (minder dan 8)
# als de de total_size te laag is => UNSAT => duurt lang
c = 2
total_size = 6
########################
# Encodering naar logica
print('Start encoding')
os = machine['outputs'] # outputs
rids = [i for i in range(c)] # components
vpool = IDPool()
cnf = CNF()
# Een hulp variabele voor False en True, maakt de andere variabelen eenvoudiger
def var_const(b):
return vpool.id(('const', b))
cnf.append([var_const(True)])
cnf.append([-var_const(False)])
# Voor elke relatie en elke twee elementen o1 en o2, is er een variabele die
# aangeeft of o1 en o2 gerelateerd zijn. Er is 1 variabele voor xRy en yRx, dus
# symmetrie is al ingebouwd. Reflexiviteit is ook ingebouwd.
def var_rel(rid, o1, o2):
if o1 == o2:
return var_const(True)
[so1, so2] = sorted([o1, o2])
return vpool.id(('rel', rid, so1, so2))
# De relatie op outputs geeft een relaties op rijen. Deze zijn geindexeerd
# met de woorden uit 'rows'.
def var_row_rel(rid, r1, r2):
if r1 == r2:
return var_const(True)
[sr1, sr2] = sorted([r1, r2])
return vpool.id(('row_rel', rid, sr1, sr2))
# Voor elke relatie, en elke equivalentie-klasse, kiezen we precies 1 rij
# als representant. Deze variabele geeft aan welk element.
def var_row_rep(rid, r):
return vpool.id(('row_rep', rid, r))
# Contraints zodat de relatie een equivalentie relatie is. We hoeven alleen
# maar transitiviteit te encoderen, want refl en symm zijn ingebouwd in de var.
# Dit stukje van het encoderen duurt het langst.
print('- transitivity (o)')
for rid in rids:
for xo in os:
for yo in os:
for zo in os:
# als xo R yo en yo R zo dan xo R zo
cnf.append([-var_rel(rid, xo, yo), -var_rel(rid, yo, zo), var_rel(rid, xo, zo)])
print('- transitivity (r)')
for rid in rids:
for rx in rows:
for ry in rows:
for rz in rows:
# als rx R ry en ry R rz dan rx R rz
cnf.append([-var_row_rel(rid, rx, ry), -var_row_rel(rid, ry, rz), var_row_rel(rid, rx, rz)])
# Constraint zodat de relaties samen alle elementen kunnen onderscheiden.
# (Aka: the bijbehorende quotienten zijn joint-injective.)
print('- injectivity (o)')
for xi, xo in enumerate(os):
for yo in os[xi + 1 :]:
# Tenminste een rid moet een verschil maken
cnf.append([-var_rel(rid, xo, yo) for rid in rids])
# Als outputs equivalent zijn, dan ook sommige rijen, en andersom.
print('rel <=> row_rel')
for rid in rids:
for rx in rows:
for ry in rows:
osx = [mealy_sem(machine, rx + c) for c in cols]
osy = [mealy_sem(machine, ry + c) for c in cols]
oss = list(zip(osx, osy))
# (ox1 ~ oy1 and ox2 ~ oy2 and ...) => rx ~ ry
cnf.append([-var_rel(rid, ox, oy) for (ox, oy) in oss] + [var_row_rel(rid, rx, ry)])
# rx ~ ry => oxi ~ oyi
for ox, oy in oss:
cnf.append([-var_row_rel(rid, rx, ry), var_rel(rid, ox, oy)])
# De constraints die zorgen dat representanten ook echt representanten zijn.
print('- representatives (r)')
for rid in rids:
for ix, rx in enumerate(rows):
# Belangrijkste: een element is een representant, of equivalent met een
# eerder element. We forceren hiermee dat de solver representanten moet
# kiezen (voor aan de lijst).
cnf.append([var_row_rep(rid, rx)] + [var_row_rel(rid, rx, ry) for ry in rows[:ix]])
for ry in rows[:ix]:
# rx en ry kunnen niet beide een representant zijn, tenzij ze
# niet gerelateerd zijn.
cnf.append([-var_row_rep(rid, rx), -var_row_rep(rid, ry), -var_row_rel(rid, rx, ry)])
# Tot slot willen we weinig representanten. Dit doen we met een "atmost"
# formule. Idealiter zoeken we naar de total_size, maar die staat nu vast.
print('- representatives at most k')
cnf_optim = CardEnc.atmost([var_row_rep(rid, rx) for rid in rids for rx in rows], total_size, vpool=vpool)
cnf.extend(cnf_optim)
def print_eqrel(rel, xs):
print_table(lambda r, c: 'Y' if rel(r, c) else '·', xs, xs)
##################################
# Probleem oplossen met solver :-)
print('Start solving')
print('- copying formula')
with Solver(bootstrap_with=cnf) as solver:
print('- actual solve')
sat = solver.solve()
if not sat:
print('unsat :-(')
exit()
print('sat :-)')
# Even omzetten in een makkelijkere data structuur
print('- get model')
m = solver.get_model()
model = {}
for l in m:
model[abs(l)] = l > 0
for rid in rids:
print(f'Relation {rid}:')
print_eqrel(lambda x, y: model[var_rel(rid, x, y)], os)
for rid in rids:
print(f'Row relation {rid}:')
print_eqrel(lambda x, y: model[var_row_rel(rid, x, y)], rows)
# print equivalence classes
count = 0
for rid in rids:
print(f'component {rid}')
# Eerst verzamelen we de representanten
for r in rows:
if model[var_row_rep(rid, r)]:
print(f'- representative row {r}')
count += 1
# count moet gelijk zijn aan cost
print(f'total size = {count}')

6
other/decompose_fsm.py → py/decompose_fsm.py
View file

@ -1,3 +1,6 @@
# Copyright 2024-2025 Joshua Moerman, Open Universiteit. All rights reserved
# SPDX-License-Identifier: EUPL-1.2
from pysat.solvers import Solver
from pysat.card import CardEnc
from pysat.formula import IDPool
@ -6,9 +9,6 @@ from pysat.formula import CNF
import math
import argparse
### Gebruik:
# Stap 1: pip3 install python-sat
# Stap 2: python3 decompose_fsm.py -h
parser = argparse.ArgumentParser(description='Decomposes a FSM into smaller components by remapping its outputs. Uses a SAT solver.')
parser.add_argument('-c', '--components', type=int, default=2, help='number of components')

7
other/decompose_fsm_optimise.py → py/decompose_fsm_optimise.py
View file

@ -1,3 +1,6 @@
# Copyright 2024-2025 Joshua Moerman, Open Universiteit. All rights reserved
# SPDX-License-Identifier: EUPL-1.2
from pysat.solvers import Solver
from pysat.card import ITotalizer
from pysat.formula import IDPool
@ -6,10 +9,6 @@ import signal
import math
import argparse
### Gebruik:
# Stap 1: pip3 install python-sat
# Stap 2: python3 decompose_fsm.py -h
keep_log = True
record_file = './results/log.txt' if keep_log else None

6
other/decompose_set.py → py/decompose_set.py
View file

@ -1,11 +1,11 @@
# Copyright 2024-2025 Joshua Moerman, Open Universiteit. All rights reserved
# SPDX-License-Identifier: EUPL-1.2
from pysat.solvers import Solver
from pysat.card import CardEnc
from pysat.formula import IDPool
from pysat.formula import CNF
### Gebruik:
# Stap 1: pip3 install python-sat
# Stap 2: python3 decompose_set.py
# Een verzameling X ontbinden in factoren X1 ... Xc, zodat X ⊆ X1 × ... × Xc.
# Hierbij is c een parameter (het aantal componenten), en ook het aantal

0
other/fsm-examples.py → py/fsm-examples.py
View file

0
other/partitions.py → py/partitions.py
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1
py/requirements.txt Normal file
View file

@ -0,0 +1 @@
python-sat

174
results/esm-log.txt Normal file
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@ -0,0 +1,174 @@
> other (main*) time python3 decompose_fsm_optimise.py -c 2 -w ~/OneDrive\ -\ Open\ Universiteit/Onderzoek/AutomataWiki/Mealy/ESM/run1/hyp.10.obf.dot
Input FSM: 674 states, 78 inputs, and 147 outputs
===============
Start encoding
transitivity (o)
injectivity
bisimulation modulo rel
representatives
weak size constraints 25 674
===============
Start solving
Trying 25 < 349 < 674 up
Trying 349 < 511 < 674 up
Trying 511 < 592 < 674 down
Trying 511 < 551 < 592 down
Trying 511 < 531 < 551 down
Trying 511 < 521 < 531 down
Trying 511 < 516 < 521 down
Trying 511 < 513 < 516 down
Trying 511 < 512 < 513 down
done searching, found bound = 512
Reduced sizes = [512, 512] = 1024
===============
Output mapping:
0 1
5.2-O2.2-O36 cls_0_0 cls_1_0
8 cls_0_0 cls_1_1_u
32 cls_0_0 cls_1_2
40.0 cls_0_1_u cls_1_3_u
22.4-O22.3-O22.2-O22.1-O22.0-O13 cls_0_0 cls_1_4
5.12-O2.12-O35 cls_0_2 cls_1_5_u
40.3 cls_0_3_u cls_1_6_u
3.13 cls_0_4_u cls_1_7
5.1-O2.1-O6-O10 cls_0_5_u cls_1_8
2.23 cls_0_6 cls_1_9
9 cls_0_0 cls_1_10_u
40.4 cls_0_0 cls_1_11_u
3.0 cls_0_7_u cls_1_12
2.8-O38 cls_0_8_u cls_1_13
3.3 cls_0_9_u cls_1_12
5.10 cls_0_0 cls_1_14_u
5.1-O2.1-O6-O36 cls_0_10 cls_1_15
5.23-O2.23-O10 cls_0_0 cls_1_16_u
2.3 cls_0_11_u cls_1_13
24.4-O24.3-O24.2-O24.1-O24.0 cls_0_0 cls_1_17_u
2.14 cls_0_0 cls_1_18_u
3.1 cls_0_12_u cls_1_12
3.19 cls_0_13 cls_1_12
10-O5.2-O2.2 cls_0_14_u cls_1_19_u
21.4-O21.3-O21.2-O21.1-O21.0 cls_0_0 cls_1_20_u
3.21 cls_0_15_u cls_1_7
3.12 cls_0_16_u cls_1_12
5.3-O2.3-O10 cls_0_17_u cls_1_21
39.3 cls_0_18_u cls_1_22_u
3.8 cls_0_19_u cls_1_12
25.4-O25.3-O25.2-O25.1-O25.0 cls_0_0 cls_1_23
5.0-O2.0-O5.8-O2.8-O38 cls_0_20_u cls_1_23
3.6 cls_0_21_u cls_1_12
5.0-O2.0 cls_0_22_u cls_1_2
5.3-O2.3-O5.9-O2.9-O33 cls_0_23_u cls_1_24
5.7-O2.7-O37 cls_0_0 cls_1_25_u
39.1 cls_0_24_u cls_1_26_u
5.14 cls_0_0 cls_1_27
5.3-O2.3 cls_0_25_u cls_1_4
2.13 cls_0_2 cls_1_24
2.0 cls_0_26_u cls_1_13
3.7 cls_0_27_u cls_1_12
2.2 cls_0_0 cls_1_28
3.4 cls_0_28_u cls_1_7
2.17 cls_0_29 cls_1_29
5.3 cls_0_30_u cls_1_30
5.23-O2.23-O36 cls_0_0 cls_1_31
5.4-O2.4 cls_0_0 cls_1_32_u
40.2 cls_0_31_u cls_1_33_u
13-O34 cls_0_0 cls_1_34_u
5.2-O2.2 cls_0_2 cls_1_35_u
5.9-O2.9-O33 cls_0_0 cls_1_36_u
2.23-O36 cls_0_32_u cls_1_27
5.13-O2.13-O28.0 cls_0_2 cls_1_37_u
5.23-O2.23 cls_0_0 cls_1_38_u
2.6 cls_0_0 cls_1_39_u
2.1-O6-O2.12-O35 cls_0_33_u cls_1_0
2.3-O10 cls_0_34_u cls_1_40
5.8-O2.8-O38 cls_0_35_u cls_1_41
5.2-O2.2-O10 cls_0_0 cls_1_21
28.3 cls_0_0 cls_1_42_u
5.4 cls_0_0 cls_1_43_u
39.5 cls_0_36_u cls_1_44_u
2.10-O36 cls_0_0 cls_1_45_u
23.4-O23.3-O23.2-O23.1-O23.0 cls_0_37 cls_1_28
2.1-O6-O10 cls_0_38_u cls_1_46_u
5.1 cls_0_39 cls_1_47
2.16 cls_0_0 cls_1_48_u
quiescence cls_0_0 cls_1_15
39.4 cls_0_40_u cls_1_49_u
3.10 cls_0_13 cls_1_7
5.7-O2.7-O26.4-O26.3-O26.2-O26.1-O26.0-O13 cls_0_41_u cls_1_2
5.18 cls_0_0 cls_1_50_u
5.1-O2.1-O6-O5.12-O2.12-O35 cls_0_42_u cls_1_4
5.3-O2.3-O36 cls_0_43_u cls_1_40
26.4-O26.3-O26.2-O26.1-O26.0-O13 cls_0_29 cls_1_51_u
18.4-O18.3-O18.2-O18.1-O18.0 cls_0_0 cls_1_52_u
5.12 cls_0_0 cls_1_47
4 cls_0_0 cls_1_53_u
2.1 cls_0_0 cls_1_8
16-O12-O12-O12-O12-O12-O12-O12-O12 cls_0_44_u cls_1_54_u
2.0-O2.8-O38 cls_0_45_u cls_1_13
2.18 cls_0_0 cls_1_55_u
5.7 cls_0_46 cls_1_47
3.17 cls_0_47_u cls_1_12
2.10 cls_0_0 cls_1_56_u
2.12-O35 cls_0_0 cls_1_57
2.1-O13-O34 cls_0_48_u cls_1_57
2.12 cls_0_0 cls_1_29
35 cls_0_0 cls_1_58_u
27.4-O27.3-O27.2-O27.1-O27.0 cls_0_49_u cls_1_41
2.2-O18.4-O18.3-O18.2-O18.1-O18.0 cls_0_0 cls_1_59_u
3.9 cls_0_50 cls_1_12
39.0 cls_0_51_u cls_1_60_u
5.0 cls_0_46 cls_1_30
2.2-O36 cls_0_0 cls_1_24
7 cls_0_10 cls_1_41
3.20 cls_0_52_u cls_1_12
2.7-O37 cls_0_0 cls_1_61_u
2.1-O6 cls_0_53_u cls_1_15
40.1 cls_0_54_u cls_1_62_u
2.9 cls_0_0 cls_1_63_u
2.1-O6-O36 cls_0_55_u cls_1_64_u
2.9-O33 cls_0_0 cls_1_65
19.4-O19.3-O19.2-O19.1-O19.0 cls_0_0 cls_1_66_u
5.10-O2.10 cls_0_0 cls_1_67_u
3.16 cls_0_50 cls_1_68_u
10-O36 cls_0_0 cls_1_13
5.9 cls_0_56 cls_1_30
5.6-O2.6 cls_0_0 cls_1_9
2.3-O2.9-O33 cls_0_57_u cls_1_9
10-O2.2 cls_0_2 cls_1_4
39.2 cls_0_58_u cls_1_69_u
2.7 cls_0_37 cls_1_41
5.2-O2.2-O18.4-O18.3-O18.2-O18.1-O18.0 cls_0_2 cls_1_31
5.1-O2.1 cls_0_0 cls_1_70_u
5.6 cls_0_0 cls_1_30
10 cls_0_0 cls_1_71_u
36 cls_0_0 cls_1_72_u
5.22-O2.22 cls_0_0 cls_1_73_u
2.7-O26.4-O26.3-O26.2-O26.1-O26.0-O13 cls_0_59_u cls_1_2
2.8 cls_0_60_u cls_1_8
3.14 cls_0_50 cls_1_7
5.23 cls_0_2 cls_1_30
5.22 cls_0_46 cls_1_27
5.10-O2.10-O36 cls_0_0 cls_1_74_u
2.21 cls_0_6 cls_1_23
2.4 cls_0_0 cls_1_41
5.1-O2.1-O6 cls_0_61_u cls_1_41
5.17 cls_0_56 cls_1_47
5.16 cls_0_0 cls_1_75_u
2.23-O10 cls_0_0 cls_1_76_u
3.18 cls_0_13 cls_1_77_u
5.1-O2.1-O13-O34 cls_0_2 cls_1_65
10-O5.23-O2.23 cls_0_0 cls_1_78_u
5.2 cls_0_2 cls_1_47
40.5 cls_0_62_u cls_1_79_u
10-O9 cls_0_0 cls_1_80_u
3.2 cls_0_63_u cls_1_12
34 cls_0_0 cls_1_81
5.13 cls_0_2 cls_1_27
2.2-O10 cls_0_0 cls_1_82_u
5.21 cls_0_64_u cls_1_27
2.22 cls_0_0 cls_1_83_u
2.3-O36 cls_0_65_u cls_1_81
28.0 cls_0_6 cls_1_84_u
5.8 cls_0_39 cls_1_30
python3 decompose_fsm_optimise.py -c 2 -w 12388.54s user 133.05s system 69% cpu 5:00:13.39 total
> other (main*)

0
other/results/optimal_decomposition_of_sets.txt → results/optimal_decomposition_of_sets.txt
View file