Current File : //opt/alt/python37/lib64/python3.7/site-packages/guppy/etc/KanExtension.py
class LeftKanExtension:
# Implementation of algorithms described by Brown and Heyworth (ref.251)
# and Heyworth (ref.253).
def __init__(self, mod, A, B, R, X, F):
# External subsystem dependencies
# mod.KnuthBendix
# mod.FiniteAutomaton
# mod.SolveFSA
# mod.Cat
# mod.Cat.Function
# mod.Cat.Functor
# mod.Cat.check_graph
# mod.Cat.check_rules
self.mod = mod
self.Cat = mod.Cat
#
self.Cat.check_graph(A)
self.Cat.check_graph(B)
self.Cat.check_rules(R, B)
#
self.A = A
self.B = B
self.R = [(tuple(g), tuple(h)) for (g, h) in R]
self.X = X
self.F = F
self.general_procedure()
def general_procedure(self):
self.initialize_tables()
self.make_confluent_system()
self.make_automaton() # self.make_catalogue()
self.make_natural_transformation()
def initialize_tables(self):
self.obj_to_str_table = {}
self.str_to_obj_table = {}
self.make_initial_rules()
def make_initial_rules(self):
# Algorithm 6.1 in (251)
Re = []
def add_rule(a, b):
aw = self.make_word(a)
bw = self.make_word(b)
if aw != bw:
Re.append((aw, bw))
for a in self.A.arrows:
srca = self.A.source(a)
tgta = self.A.target(a)
XA = self.X.fo(srca)
Xa = self.X.fa(a)
Fa = tuple(self.F.fa(a))
Fsrca = self.F.fo(srca)
Ftgta = self.F.fo(tgta)
if Fa:
t = Fsrca
for b in Fa:
srcb = self.B.source(b)
if srcb != t:
raise ValueError(
'Arrow [%s] with source %s does not compose with target %s' % (b, srcb, t))
t = self.B.target(b)
if t != Ftgta:
raise ValueError(
'Arrow %s with target %s does not compose with %s' % (Fa, t, Ftgta))
else:
if Fsrca != Ftgta:
raise ValueError(
'Source %s does not match target %s' % (Fsrca, Ftgta))
for x in XA:
add_rule(((srca, x),) + Fa, ((tgta, Xa(x)),))
Rk = [(self.make_word(x), self.make_word(y)) for (x, y) in self.R]
self.Re = Re
self.Rk = Rk
self.Rinit = Re + Rk
def make_confluent_system(self):
self.rs = self.mod.KnuthBendix(self.Rinit, delim='.')
self.Rconf = self.rs.reductions
def make_automaton(self):
# Make nondeterministic finite automaton
def target(e):
if len(e) == 1 and isinstance(e[0], tuple):
return self.F.fo(e[0][0])
else:
return self.B.target(e[-1])
XA = []
for A in self.A.objects:
for x in self.X.fo(A):
XA.append(((A, x),))
follows = dict([(B, []) for B in self.B.objects])
for b, (srcb, tgtb) in list(self.B.arrows.items()):
follows[srcb].append((b, tgtb))
IR = dict([(self.make_term(u), self.make_term(v))
for u, v in self.Rconf])
pplR = {}
for l, r in self.Rconf:
t = self.make_term(l)
for i in range(1, len(t)):
pplR[t[:i]] = 1
s0 = ('s0',)
fsa = self.mod.FiniteAutomaton(s0)
for xi in XA:
if xi not in IR:
fsa.add_transition(s0, xi[0], xi)
for xi in XA:
for b, tgtb in follows[target(xi)]:
bterm = (b,)
xib = xi + bterm
if xib in pplR:
fsa.add_transition(xi, b, xib, tgtb)
elif (bterm in pplR and xib not in IR):
fsa.add_transition(xi, b, bterm, tgtb)
elif xib not in IR:
fsa.add_transition(xi, b, tgtb)
for Bi in self.B.objects:
for b, tgtb in follows[Bi]:
bterm = (b,)
if bterm in pplR:
fsa.add_transition(Bi, b, bterm, tgtb)
elif bterm not in IR:
fsa.add_transition(Bi, b, tgtb)
for u in pplR:
if u in XA:
continue
for b, tgtb in follows[target(u)]:
bterm = (b,)
ub = u + bterm
if ub in pplR:
fsa.add_transition(u, b, ub, tgtb)
elif self.irreducible(ub): # ub not in IR:
fsa.add_transition(u, b, tgtb)
def get_RS(Bi):
finals = {}
finals[Bi] = 1
for xi in XA:
if self.F.fo(xi[0][0]) == Bi:
finals[xi] = 1
for u in pplR:
if target(u) == Bi:
finals[u] = 1
for c in fsa.get_composites():
for s in c:
if s not in finals:
break
else:
finals[c] = 1
dfa = fsa.get_minimized_dfa(finals)
regexp = self.mod.SolveFSA(dfa)
return RegularSet(regexp)
KB = self.Cat.Function(get_RS, self.B.objects, None)
Kb = self.Cat.Function(
lambda a: KanAction(self.B, KB, a, target,
self.irreducible, self.reduce),
self.B.arrows,
KanAction,
)
self.KB = KB
self.Kb = Kb
self.K = self.Cat.Functor(KB, Kb)
def make_catalogue(self):
# Catalogue the elements of the sets pointed to by extension functor K,
# according to algorithm described in 7.1 in (251).
# Precondition:
# Tables initialized and a confluent system created.
# The system is assumed to be finite, otherwise we won't terminate.
# Postcondition:
# Functor self.K represented as:
#
# self.K.tabo = self.KB = dict mapping,
# source: {each B in self.B.objects}
# target: sets represented as lists
# self.K.taba = self.Kb = dict, mapping
# source: {each a in self.B.arrows}
# target: tabulated function, mapping
# source: KB[source of a]
# target: KB[target of a]
def target(e):
if len(e) == 1:
return self.F.fo(e[0][0])
else:
return self.B.target(e[-1])
def add_element(e):
if self.irreducible(e):
block.append(e)
KB[target(e)].append(e)
else:
pass
KB = dict([(B, []) for B in self.B.objects])
block = []
for A in self.A.objects:
for x in self.X.fo(A):
add_element(((A, x),))
while block:
oblock = block
block = []
for e in oblock:
tgt = target(e)
for a in self.B.arrows:
if self.B.source(a) == tgt:
add_element(e + (a,))
Kb = {}
for a in self.B.arrows:
src = KB[self.B.source(a)]
tgt = KB[self.B.target(a)]
tab = dict([(s, self.reduce(s + (a,))) for s in src])
Kb[a] = self.Cat.Function(tab, src, tgt)
KB = self.Cat.Function(KB, self.B.objects, list(KB.values()))
Kb = self.Cat.Function(Kb, self.B.arrows, list(Kb.values()))
self.KB = KB
self.Kb = Kb
self.K = self.Cat.Functor(KB, Kb)
def make_natural_transformation(self):
# Precondition:
# initial tables should be initialized
# self.K.fo should exist
# Postcondition:
#
# self.nat[A] for A in self.A.objects
get_nat_memo = {}
def get_nat(A):
if A in get_nat_memo:
return get_nat_memo[A]
src = self.X.fo(A)
tgt = self.K.fo(self.F.fo(A))
tab = dict([(x, self.reduce(((A, x),))) for x in src])
get_nat_memo[A] = self.Cat.Function(tab, src, tgt)
return get_nat_memo[A]
self.nat = self.Cat.Function(get_nat, self.A.objects, None)
def make_word(self, x):
ots = self.obj_to_str
return '.'.join([ots(e) for e in x if e != ''])
def obj_to_str(self, x):
otn = self.obj_to_str_table
try:
return otn[x]
except KeyError:
assert not (isinstance(x, tuple) and len(x) > 2)
n = str(len(otn))
#n = '%d:%s'%(len(otn), x)
#n = str(x)
otn[x] = n
self.str_to_obj_table[n] = x
return n
def str_to_obj(self, x):
return self.str_to_obj_table[x]
def irreducible(self, x):
tx = self.make_word(x)
return tx == self.rs.reduce(tx)
def reduce(self, x):
w = self.rs.reduce(self.make_word(x))
return self.make_term(w)
def make_term(self, word):
sto = self.str_to_obj_table
return tuple([sto[s] for s in word.split('.') if s])
class KanAction:
def __init__(self, B, KB, a, targetof, irreducible, reduce):
srca = B.source(a)
tgta = B.target(a)
self.src = KB(srca)
self.tgt = KB(tgta)
self.a = a
self.srca = srca
self.targetof = targetof
self.irreducible = irreducible
self.reduce = reduce
def __call__(self, s):
if self.targetof(s) != self.srca:
raise TypeError('''\
Target of %r (= %r) does not match source of %r (= %r)''' % (
s, self.targetof(s), self.a, self.srca))
if not self.irreducible(s):
raise TypeError('''\
Argument %r is reducible to %r; and is thus not in the source set K.fo(%r)''' % (
s, self.reduce(s), self.srca))
return self.reduce(s + (self.a,))
class RegularSet:
# Wraps a regular expression;
# provides a set protocol for the underlying set of sequences:
# o If the RE specifies a finite language, iteration over its strings
# [ o set inclusion ]
is_simplified = 0
def __init__(self, re):
self.re = re
def __iter__(self):
return iter(self.uniform)
def __getitem__(self, x):
return self.uniform[x]
def __len__(self):
return len(self.uniform)
def get_xs_covered(self, coverage):
N = coverage
X = self.re.limited(coverage)
xs = X.sequni()
return [tuple(x) for x in xs]
def get_uniform(self):
self.simplify()
return self.re.sequni()
uniform = property(fget=get_uniform)
def simplify(self):
if not self.is_simplified:
self.re = self.re.simplified()
self.is_simplified = 1
class ObjectTester:
def __init__(self, category_tester, object, code):
self.category_tester = category_tester
self.functor = category_tester.functor
self.object = object
self.code = code
def get_all_arrows(self):
return self.category_tester.arrows[self.object]
def get_intermediate_test_code(self):
return self.code
def get_python_test_source_code(self):
cmap = {
'aseq': 'assert e[%r] == e[%r]',
'evalfa': 'e[%r] = fa[%r](e[%r])',
'asfo': 'assert fo[%r](e[%r])'
}
return '\n'.join([cmap[c[0]] % c[1:] for c in self.code])
def execode(self, arg):
code = self.get_python_test_source_code()
e = {'arg': arg}
d = {'fa': self.functor.fa,
'fo': self.functor.fo,
'e': e,
}
exec(code, d)
return e
def intercode(self, arg):
e = {'arg': arg}
fa = self.functor.fa
fo = self.functor.fo
for c in self.code:
a = c[0]
if a == 'evalfa':
dst, ar, src = c[1:]
e[dst] = fa[ar](e[src])
elif a == 'asfo':
ob, src = c[1:]
if not fo[ob](e[src]):
raise ValueError('Predicate failed')
elif a == 'aseq':
na, nb = c[1:]
if e[na] != e[nb]:
raise ValueError('e[%r] != e[%r]' % (na, nb))
else:
raise ValueError('Invalid code: %r' % (a,))
def test(self, arg):
return self.intercode(arg)
class CategoryTester:
def __init__(self, mod, functor, arrows, get_arrow_name=None):
self.mod = mod
self.cat = functor.src
self.functor = functor
self.arrows = arrows
if get_arrow_name is not None:
self.get_arrow_name = get_arrow_name
def get_arrow_name(self, a):
return '.'.join(a)
def get_eval_arrows_code(self, object, argname):
fa = self.functor.fa
name = argname
memo = {(): name}
memolist = [((), name)]
codes = []
def eval_arrow(a):
if a in memo:
return memo[a]
a0 = a[:-1]
a1 = a[-1]
name = self.get_arrow_name(a)
na0 = eval_arrow(a0)
#codes.append('%s = fa[%r](%s)'%(name, a1, na0))
codes.append(('evalfa', name, a1, na0))
memo[a] = name
memolist.append((a, name))
return name
for ar in self.arrows[object]:
eval_arrow(ar)
return codes, memolist
def get_object_tester(self, object):
code = self.get_test_object_code(object)
return ObjectTester(self, object, code)
def get_test_inclusion_code(self, object, ml):
codes = []
src = self.functor.fo.src
for arrow, value in ml:
ob = object
if arrow:
ob = self.cat.graph.target(arrow[-1])
#codes.append('assert fo[%r](%s)'%(ob, value))
if src is None or ob in src:
codes.append(('asfo', ob, value))
return codes
def get_test_object_code(self, object):
argname = 'arg'
evalcodes, memolist = self.get_eval_arrows_code(object, argname)
relcodes = self.get_test_relations_code(object, memolist)
incodes = self.get_test_inclusion_code(object, memolist)
return evalcodes+relcodes+incodes
def get_test_relations_code(self, object, memolist):
codes = []
cat = self.cat
fa = self.functor.fa
memo = dict(memolist)
def teval_arrow(ar):
if ar in memo:
return memo[ar]
a0 = teval_arrow(ar[:-1])
name = self.get_arrow_name(ar)
#codes.append('%s = fa[%r](%s)'%(name, ar[-1], a0))
codes.append(('evalfa', name, ar[-1], a0))
memo[ar] = name
return name
# Check that the equality relations really match up
# for all arrows in old memolist, i.e. original unique arrows
# which is arguably overkill sometimes?..
for a, b in cat.relations:
a = tuple(a)
b = tuple(b)
src = cat.graph.source(a[0])
for (arr, val) in memolist:
if arr:
tgt = cat.graph.target(arr[-1])
else:
tgt = object
if src == tgt:
ara = arr + a
arb = arr + b
if ara != arb:
va = teval_arrow(ara)
vb = teval_arrow(arb)
assert va != vb
#codes.append('assert %s == %s'%(va, vb))
codes.append(('aseq', va, vb))
return codes
def test_object(self, object, value):
tester = self.get_object_tester(object)
tester.test(value)
return tester
def test_object_fail(self, object, value):
try:
self.test_object(object, value)
except Exception:
pass
else:
raise Exception('Exception excepted')
class _GLUECLAMP_:
# 'imports'
def _get_KnuthBendix(self): return self._parent.KnuthBendix.KnuthBendix
def _get_FiniteAutomaton(self): return self._parent.FSA.FiniteAutomaton
def _get_SolveFSA(self): return self._parent.RE.SolveFSA
def _get_Cat(self): return self._parent.Cat
# Main exported interface is the lke method
# which provides a context for the LeftKanExtension class.
def lke(self, A, B, R, X, F):
return LeftKanExtension(self, A, B, R, X, F)
# Other functions - examples of applications of Kan extension
# in alphabetic order
def arrows_map(self, cat, from_objects=0, coverage=1):
if from_objects:
cat = cat.get_dual()
A = self.Cat.Graph(cat.graph.objects, [])
B = cat.graph
R = cat.relations
X = self.Cat.Functor(lambda x: [1], lambda x: lambda y: y)
F = self.Cat.Functor(lambda x: x, lambda x: [])
ke = self.lke(A, B, R, X, F)
memo = {}
def get_arrows(object):
if object in memo:
return memo[object]
re = ke.K.fo[object].re.rempretup()
if from_objects:
re = re.reversed()
if str(coverage).startswith('length'):
maxlen = int(coverage[6:])
ar = []
xs = re.get_words_memo()
for i in range(1, maxlen+1):
ar.extend([tuple(x) for x in xs.get_words_of_length(i)])
else:
re = re.limited(coverage)
xs = re.sequni()
ar = [tuple(x) for x in xs]
memo[object] = ar
return ar
return self.Cat.Function(
get_arrows,
src=ke.K.fo.src,
tgt=None
)
def category_tester(self, functor, arrows=None, coverage=1):
if isinstance(functor, tuple):
fo, fa, src = functor
if fo is None:
def fo(x): return lambda y: 1
functor = self.Cat.Functor(fo, fa, src)
if arrows is None:
arrows = self.arrows_map(
functor.src, from_objects=1, coverage=coverage)
return CategoryTester(self, functor, arrows)
def coequalizer(self, S0, S1, f0, f1):
# Given
#
# S0, S1 sets (objects that can be iterated over)
# f0, f1 functions from S0 to S1
#
# Return a coequalizing function,
# such that in the following diagram:
#
# S0 ===== S0
# | |
# | f0 | f1
# | |
# V V
# S1 ===== S1 ==== coequalizing_function.src
# |
# | coequalizing_function
# |
# V
# coequalizing_function.tgt
# both paths from S0 to coequalizing_function.tgt will be equivalent,
# and coequalizing_function.tgt is a colimit of all such sets.
#
# The coequalizing_function object is callable with
# an argument from S1, and has the following attributes:
# .src is identical to S1
# .tgt is a set in iterable form
# .asdict() returns a dict representing the mapping
objects = [0, 1]
arrows = {'a0': (0, 1), 'a1': (0, 1)}
A = self.Cat.Graph(objects, arrows)
Xo = self.Cat.Function({0: S0, 1: S1}, objects, [S0, S1])
Xa = self.Cat.Function({'a0': f0, 'a1': f1}, arrows, [f0, f1])
X = self.Cat.Functor(Xo, Xa)
colimit_object, colimit_functions = self.colimit(A, X)
return colimit_functions[1]
def colimit(self, A, X):
# According to 9.6 in (ref.251)
B = self.Cat.Graph([0], {})
R = []
F = self.Cat.Functor(lambda x: 0, lambda x: ())
lka = self.lke(A, B, R, X, F)
colimit_object = lka.KB[0]
colimit_functions = lka.nat
# Reduce elements to a smaller (but isomorphic) form
# I.E since elements are all of the form
# ((A, X),)
# they can be reduced to the form
# (A, X)
#
colimit_object = [x[0] for x in colimit_object]
colimit_functions = dict([
(A, self.Cat.Function(
dict([(a, k[0])
for (a, k) in list(cof.items())]),
cof.src,
colimit_object,
)
)
for (A, cof) in list(colimit_functions.items())])
return colimit_object, colimit_functions
def test_arrows(self, functor, object, value):
# Application of arrow listing to test sequencing
# Discussed in Notes Mar 9 2005
tester = self.category_tester(functor)
return tester.test_object(object, value)
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