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renyi-entropy
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hidden-markov-models
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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 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 | """Rational labelled hidden-state sources with bounded integer branch clocks."""
from dataclasses import dataclass
from fractions import Fraction as Q
from math import lcm
from .arithmetic import Field, positive_int
@dataclass(frozen=True)
class Edge:
start: int
end: int
digit: tuple
clock: int
probability: Q
class Source:
def __init__(self,field:Field,states:int,edges,initial):
self.field=field
self.states=positive_int(states,"states")
converted=[]
for e in edges:
if not isinstance(e,Edge):
e=Edge(*e)
if (isinstance(e.start,bool) or isinstance(e.end,bool) or
not isinstance(e.start,int) or not isinstance(e.end,int) or
not 0<=e.start<states or not 0<=e.end<states):
raise ValueError("edge endpoint outside hidden-state range")
positive_int(e.clock,"clock")
p=Q(e.probability)
if p<0 or p>1:
raise ValueError("edge probabilities must lie in [0,1]")
if p:
converted.append(Edge(e.start,e.end,field.digit(e.digit),e.clock,p))
self.edges=tuple(converted)
self.outgoing=tuple(tuple(i for i,e in enumerate(self.edges) if e.start==s)
for s in range(states))
if any(sum((self.edges[i].probability for i in ids),Q(0))!=1 for ids in self.outgoing):
raise ValueError("outgoing edge probabilities must sum to one at every state")
self.initial=tuple(map(Q,initial))
if len(self.initial)!=states or min(self.initial)<=0 or sum(self.initial)!=1:
raise ValueError("initial probabilities must be positive and sum to one; remove inactive states")
self.transition=tuple(tuple(sum((e.probability for e in self.edges if e.start==i and e.end==j),Q(0))
for j in range(states)) for i in range(states))
after=tuple(sum((self.initial[i]*self.transition[i][j] for i in range(states)),Q(0))
for j in range(states))
self.stationary=(after==self.initial)
self.digits=tuple(set(e.digit for e in self.edges))
self.clocks=tuple(e.clock for e in self.edges)
self.kmin,self.kmax=min(self.clocks),max(self.clocks)
self.denominator=lcm(*(e.probability.denominator for e in self.edges))
self.integer_weights=tuple(int(e.probability*self.denominator) for e in self.edges)
def split_bound(self,n):
return self.field.split_bound(n,self.digits,self.clocks)
def slot_successors(self,slot,completed,n):
"""(position,remaining): a ready vertex, or a busy edge countdown."""
position,remaining=slot
if remaining:
e=self.edges[position]
if remaining==1:
return (((e.end,0),completed+1,e.digit,Q(1)),)
return (((position,remaining-1),completed,self.field.zero,Q(1)),)
if completed>=n:
return ()
ans=[]
for index in self.outgoing[position]:
e=self.edges[index]
if e.clock==1:
ans.append(((e.end,0),completed+1,e.digit,e.probability))
else:
ans.append(((index,e.clock-1),completed,self.field.zero,e.probability))
return tuple(ans)
def sample_path(self,n,rng):
positive_int(n,"depth")
state=categorical(self.initial,rng)
start=state
path=[]
for _ in range(n):
ids=self.outgoing[state]
j=categorical(tuple(self.edges[i].probability for i in ids),rng)
index=ids[j]
path.append(index)
state=self.edges[index].end
return start,tuple(path),state
def path_map(self,path):
u=self.field.zero
clock=0
units=[]
for i in path:
if isinstance(i,bool) or not isinstance(i,int) or not 0<=i<len(self.edges):
raise ValueError("invalid edge index")
e=self.edges[i]
u=self.field.jump(u,e.clock,e.digit)
clock+=e.clock
units.extend([self.field.zero]*(e.clock-1))
units.append(e.digit)
return (clock,u),tuple(units)
def categorical(probabilities,rng):
denominator=lcm(*(p.denominator for p in probabilities))
r=rng.randrange(denominator)
if isinstance(r,bool) or not isinstance(r,int) or not 0<=r<denominator:
raise ValueError("random integer oracle violated its contract")
total=0
for i,p in enumerate(probabilities):
total+=int(p*denominator)
if r<total:
return i
raise ArithmeticError("invalid categorical source")
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