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1c08905 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | """Small exhaustive reference. Does not use the production carry/compiler code."""
from collections import defaultdict
from fractions import Fraction as Q
def multiply_add(coeff,clock,digit,polynomial):
d=len(polynomial)
work=[0]*clock+list(coeff)
for j in range(len(work)-1,d-1,-1):
c=work[j]
for k,p in enumerate(polynomial):
work[j-d+k]-=c*p
work[j]=0
work+=([0]*max(0,d-len(work)))
return tuple(work[i]+digit[i] for i in range(d))
def distributions(source,n):
all_starts=[]
for start in range(source.states):
table={(start,0,(0,)*source.field.degree):Q(1)}
for _ in range(n):
nxt=defaultdict(Q)
for (state,clock,u),weight in table.items():
for e in source.edges:
if e.start==state:
v=multiply_add(u,e.clock,e.digit,source.field.polynomial)
nxt[(e.end,clock+e.clock,v)]+=weight*e.probability
table=dict(nxt)
all_starts.append(table)
return all_starts
def moment_matrix(source,n,q):
rows=distributions(source,n)
return [[sum((p**q for (end,clock,u),p in row.items() if end==b),Q(0))
for b in range(source.states)] for row in rows]
def aggregate(source,n):
out=defaultdict(Q)
for a,row in enumerate(distributions(source,n)):
for (b,clock,u),p in row.items():
out[(clock,u)]+=source.initial[a]*p
return dict(out)
def paths(source,n,start):
if n==0:
yield (),start,Q(1)
return
for i,e in enumerate(source.edges):
if e.start==start:
for rest,end,p in paths(source,n-1,e.end):
yield (i,)+rest,end,e.probability*p
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