"""Independent finite construction checks. Standard library only; no model training.
Run: python this_file.py. Stdout contains the two figure datasets.
"""
import json, math
from itertools import product, combinations
from fractions import Fraction as F
figures=[]
ps=[.6,.8,.95];entropy=[-p*math.log(p)-(1-p)*math.log(1-p)for p in ps];ce=[-math.log(1-p)for p in ps]
assert all(entropy[i]>entropy[i+1]and ce[i]<ce[i+1]for i in range(2))and all(p>.5 for p in ps)
figures.append(dict(id="confidence-versus-correctness",x=ps,series=[dict(name="Prediction entropy",y=entropy),dict(name="True class-0 cross-entropy",y=ce)]))
def running(xs):
 out=[0.]
 for x in xs:out.append((out[-1]+x)/2)
 return out
a=[0,0,2,2,0,0];b=[2,2,0,0,0,0];ra=running(a);rb=running(b)
assert sorted(a)==sorted(b)and ra[-1]==.375 and rb[-1]==.09375
assert running([0,2])[-1]==1 and running([2,0])[-1]==.5
figures.append(dict(id="stream-order-state",x=list(range(7)),series=[dict(name="0,0,2,2,0,0",y=ra),dict(name="2,2,0,0,0,0",y=rb)]))
# Fixed threshold probes and actual full/partial state restoration.
def probe(state):return int(state['mean']>.75)
source={'weight':1.,'optimizer':0.,'mean':0.}
state={'weight':2.,'optimizer':7.,'mean':running([0,2])[-1]}
reversed_state=dict(state,mean=running([2,0])[-1])
assert probe(state)!=probe(reversed_state)
fully_reset=dict(source)
partly_reset=dict(state,weight=source['weight'],optimizer=source['optimizer'])
assert fully_reset==source and probe(fully_reset)==probe(source)
assert partly_reset['mean']!=source['mean']and probe(partly_reset)!=probe(source)
# Deliberately false inference: decreasing entropy proves improved 0-1 accuracy.
errors=[int(p>.5)for p in ps]
try:
 assert entropy[-1]<entropy[0]and errors[-1]<errors[0]
except AssertionError:pass
else:raise AssertionError('invalid entropy-to-accuracy inference was accepted')

assert len(figures)==2
print(json.dumps({"status":"PASS","scope":"Constructed analytic examples only; not neural training, a benchmark, or measured hardware performance.","figures":figures},indent=2))