mirror of
https://github.com/pezkuwichain/consensus.git
synced 2026-04-21 23:47:57 +00:00
Clean everything
This commit is contained in:
kianenigma
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.vscode
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[flake8]
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max-line-length = 120
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+394
-282
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#from itertools import count
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import unittest
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import sys
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def print_list(ll):
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for item in ll:
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print(item)
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class edge:
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def __init__(self,nomid,valiid):
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self.nomid=nomid
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self.valiid=valiid
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#self.validator
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self.load=0
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self.weight=0
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def __init__(self, nominator_id, validator_id):
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self.nominator_id = nominator_id
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self.validator_id = validator_id
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self.load = 0
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self.weight = 0
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self.candidate = None
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def __str__(self):
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return "Edge({}, weight = {})".format(
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self.validator_id,
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self.weight,
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)
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class nominator:
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def __init__(self,votetuple):
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self.nomid=votetuple[0]
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self.budget=votetuple[1]
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self.edges=[edge(self.nomid,valiid) for valiid in votetuple[2]]
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self.load=0
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def __init__(self, nominator_id, budget, targets):
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self.nominator_id = nominator_id
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self.budget = budget
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self.edges = [edge(self.nominator_id, validator_id) for validator_id in targets]
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self.load = 0
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def __str__(self):
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return "Nominator({}, budget = {}, load = {}, edges = {})".format(
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self.nominator_id,
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self.budget,
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self.load,
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[str(e) for e in self.edges]
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)
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class candidate:
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def __init__(self,valiid,valindex):
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self.valiid = valiid
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self.valindex=valindex
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self.approvalstake=0
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self.elected=False
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self.backedstake=0
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self.score=0
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self.scoredenom=0
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def __init__(self, validator_id, index):
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self.validator_id = validator_id
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self.valindex = index
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self.approval_stake = 0
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self.backed_stake = 0
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self.elected = False
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self.score = 0
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self.scoredenom = 0
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def setuplists(votelist):
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#Instead of Python's dict here, you can use anything with O(log n) addition and lookup.
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#We can also use a hashmap like dict, by generating a random constant r and useing H(canid+r)
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#since the naive thing is obviously attackable.
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nomlist = [nominator(votetuple) for votetuple in votelist]
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candidatedict=dict()
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candidatearray=list()
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numcandidates=0
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#Get an array of candidates. ]#We could reference these by index
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#rather than pointer
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for nom in nomlist:
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for edge in nom.edges:
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valiid = edge.valiid
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if valiid in candidatedict:
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edge.candidate=candidatearray[candidatedict[valiid]]
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else:
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candidatedict[valiid]=numcandidates
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newcandidate=candidate(valiid,numcandidates)
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candidatearray.append(newcandidate)
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edge.candidate=newcandidate
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numcandidates += 1
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return(nomlist,candidatearray)
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def __str__(self):
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return "Candidate({}, approval = {}, backed = {}, score = {}, scoredenom = {})".format(
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self.validator_id,
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self.approval_stake,
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self.backed_stake,
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self.score,
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self.scoredenom,
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)
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def seqPhragmén(votelist,numtoelect):
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nomlist,candidates=setuplists(votelist)
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#Compute the total possible stake for each candidate
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for nom in nomlist:
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for edge in nom.edges:
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edge.candidate.approvalstake += nom.budget
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electedcandidates=list()
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for round in range(numtoelect):
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def seq_phragmen(votelist, num_to_elect):
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nomlist, candidates = setuplists(votelist)
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calculate_approval(nomlist)
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elected_candidates = list()
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for round in range(num_to_elect):
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for candidate in candidates:
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if not candidate.elected:
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candidate.score=1/candidate.approvalstake
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candidate.score = 1/candidate.approval_stake
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for nom in nomlist:
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for edge in nom.edges:
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if not edge.candidate.elected:
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edge.candidate.score +=nom.budget * nom.load / edge.candidate.approvalstake
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bestcandidate=0
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bestscore = 1000 #should be infinite but I'm lazy
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edge.candidate.score += nom.budget * nom.load / edge.candidate.approval_stake
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best_candidate = 0
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best_score = 1000 # should be infinite but I'm lazy
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for candidate in candidates:
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if not candidate.elected and candidate.score < bestscore:
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bestscore=candidate.score
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bestcandidate=candidate.valindex
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electedcandidate=candidates[bestcandidate]
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electedcandidate.elected=True
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electedcandidate.electedpos=round
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electedcandidates.append(electedcandidate)
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if not candidate.elected and candidate.score < best_score:
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best_score = candidate.score
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best_candidate = candidate.valindex
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elected_candidate = candidates[best_candidate]
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elected_candidate.elected = True
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elected_candidate.electedpos = round
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elected_candidates.append(elected_candidate)
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for nom in nomlist:
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for edge in nom.edges:
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if edge.candidate.valindex == bestcandidate:
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edge.load=electedcandidate.score - nom.load
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nom.load=electedcandidate.score
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if edge.candidate.valindex == best_candidate:
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edge.load = elected_candidate.score - nom.load
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nom.load = elected_candidate.score
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for candidate in electedcandidates:
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candidate.backedstake=0
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for candidate in elected_candidates:
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candidate.backed_stake = 0
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for nom in nomlist:
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for edge in nom.edges:
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if nom.load > 0.0:
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edge.weight = nom.budget * edge.load/nom.load
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edge.candidate.backedstake += edge.weight
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else:
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edge.weight = 0
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return (nomlist,electedcandidates)
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def calculateMaxScoreNoCutoff(nomlist,candidates):
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# First we compute the denominator of the score
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for candidate in candidates:
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if not candidate.elected:
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candidate.scoredenom=1.0
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for nom in nomlist:
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denominatorcontrib = 0
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for edge in nom.edges:
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if edge.candidate.elected:
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denominatorcontrib += edge.weight/edge.candidate.backedstake
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# print(nom.nomid, denominatorcontrib)
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for edge in nom.edges:
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if not edge.candidate.elected:
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edge.candidate.scoredenom += denominatorcontrib
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# print(edge.candidate.valiid, nom.nomid, denominatorcontrib, edge.candidate.scoredenom)
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# Then we divide. Not that score here is comparable to the recipricol of the score in seqPhragmen.
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# In particular there low scores are good whereas here high scores are good.
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bestcandidate=0
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bestscore = 0.0
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for candidate in candidates:
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# print(candidate.valiid, candidate.approvalstake, candidate.scoredenom)
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if candidate.approvalstake > 0.0:
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candidate.score = candidate.approvalstake/candidate.scoredenom
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if not candidate.elected and candidate.score > bestscore:
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bestscore=candidate.score
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bestcandidate=candidate
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else:
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candidate.score=0.0
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# print(len(candidates), bestcandidate, bestscore)
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return (bestcandidate,bestscore)
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def electWithScore(nomlist, electedcandidate, cutoff):
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for nom in nomlist:
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for newedge in nom.edges:
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if newedge.valiid == electedcandidate.valiid:
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usedbudget = sum([edge.weight for edge in nom.edges])
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newedge.weight = nom.budget-usedbudget
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electedcandidate.backedstake += nom.budget-usedbudget
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for edge in nom.edges:
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if edge.valiid != electedcandidate.valiid and edge.weight > 0.0:
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if edge.candidate.backedstake > cutoff:
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staketotake = edge.weight * cutoff / edge.candidate.backedstake
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newedge.weight += staketotake
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edge.weight -= staketotake
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edge.candidate.backedstake -= staketotake
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electedcandidate.backedstake += staketotake
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def approvalvoting(votelist,numtoelect):
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nomlist,candidates=setuplists(votelist)
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#Compute the total possible stake for each candidate
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for nom in nomlist:
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for edge in nom.edges:
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edge.candidate.approvalstake += nom.budget
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edge.weight = nom.budget/min(len(nom.edges),numtoelect)
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edge.candidate.backedstake += edge.weight
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candidates.sort( key = lambda x : x.approvalstake, reverse=True)
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electedcandidates=candidates[0:numtoelect]
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return nomlist,electedcandidates
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if nom.load > 0.0:
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edge.weight = nom.budget * edge.load/nom.load
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edge.candidate.backed_stake += edge.weight
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else:
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edge.weight = 0
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return (nomlist, elected_candidates)
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def printresult(nomlist,electedcandidates):
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for candidate in electedcandidates:
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print(candidate.valiid," is elected with stake ",candidate.backedstake, "and score ",candidate.score)
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print()
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for nom in nomlist:
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print(nom.nomid," has load ",nom.load, "and supported ")
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for edge in nom.edges:
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print(edge.valiid," with stake ",edge.weight, end=" ")
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print()
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def equalise(nom, tolerance):
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# Attempts to redistribute the nominators budget between elected validators
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# Assumes that all elected validators have backedstake set correctly
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# returns the max difference in stakes between sup
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electededges=[edge for edge in nom.edges if edge.candidate.elected]
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if len(electededges)==0:
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# Attempts to redistribute the nominators budget between elected validators. Assumes that all
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# elected validators have backed_stake set correctly. Returns the max difference in stakes
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# between sup.
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elected_edges = [edge for edge in nom.edges if edge.candidate.elected]
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if len(elected_edges) < 2:
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return 0.0
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stakeused = sum([edge.weight for edge in electededges])
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backedstakes=[edge.candidate.backedstake for edge in electededges]
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backingbackedstakes=[edge.candidate.backedstake for edge in electededges if edge.weight > 0.0]
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if len(backingbackedstakes) > 0:
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difference = max(backingbackedstakes)-min(backedstakes)
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difference += nom.budget-stakeused
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stake_used = sum([edge.weight for edge in elected_edges])
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backed_stakes = [edge.candidate.backed_stake for edge in elected_edges]
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backingbacked_stakes = [
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edge.candidate.backed_stake for edge in elected_edges if edge.weight > 0.0
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]
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if len(backingbacked_stakes) > 0:
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difference = max(backingbacked_stakes)-min(backed_stakes)
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difference += nom.budget - stake_used
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if difference < tolerance:
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return difference
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else:
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difference = nom.budget
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#remove all backing
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# remove all backing
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for edge in nom.edges:
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edge.candidate.backedstake -= edge.weight
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edge.weight=0
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electededges.sort(key=lambda x: x.candidate.backedstake)
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cumulativebackedstake=0
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lastcandidateindex=len(electededges)-1
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for i in range(len(electededges)):
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backedstake=electededges[i].candidate.backedstake
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#print(nom.nomid,electededges[i].valiid,backedstake,cumulativebackedstake,i)
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if backedstake * i - cumulativebackedstake > nom.budget:
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lastcandidateindex=i-1
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edge.candidate.backed_stake -= edge.weight
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edge.weight = 0
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elected_edges.sort(key=lambda x: x.candidate.backed_stake)
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cumulative_backed_stake = 0
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last_index = len(elected_edges) - 1
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for i in range(len(elected_edges)):
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backed_stake = elected_edges[i].candidate.backed_stake
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if backed_stake * i - cumulative_backed_stake > nom.budget:
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last_index = i-1
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break
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cumulativebackedstake +=backedstake
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laststake=electededges[lastcandidateindex].candidate.backedstake
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waystosplit=lastcandidateindex+1
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excess = nom.budget + cumulativebackedstake - laststake*waystosplit
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for edge in electededges[0:waystosplit]:
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edge.weight = excess / waystosplit + laststake - edge.candidate.backedstake
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edge.candidate.backedstake += edge.weight
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cumulative_backed_stake += backed_stake
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last_stake = elected_edges[last_index].candidate.backed_stake
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ways_to_split = last_index+1
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excess = nom.budget + cumulative_backed_stake - last_stake*ways_to_split
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for edge in elected_edges[0:ways_to_split]:
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edge.weight = excess / ways_to_split + last_stake - edge.candidate.backed_stake
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edge.candidate.backed_stake += edge.weight
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return difference
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import random
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def equaliseall(nomlist,maxiterations,tolerance):
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def equalise_all(nomlist, maxiterations, tolerance):
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for i in range(maxiterations):
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for j in range(len(nomlist)):
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nom=random.choice(nomlist)
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equalise(nom,tolerance/10)
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maxdifference=0
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# for j in range(len(nomlist)):
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# nom = random.choice(nomlist)
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# equalise(nom, tolerance)
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maxdifference = 0
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for nom in nomlist:
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difference=equalise(nom,tolerance/10)
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maxdifference=max(difference,maxdifference)
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difference = equalise(nom, tolerance)
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maxdifference = max(difference, maxdifference)
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if maxdifference < tolerance:
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return
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def seqPhragménwithpostprocessing(votelist,numtoelect):
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nomlist,electedcandidates = seqPhragmén(votelist,numtoelect)
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equaliseall(nomlist,2,0.1)
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return nomlist,electedcandidates
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def factor3point15(votelist, numtoelect,tolerance=0.1):
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nomlist,candidates=setuplists(votelist)
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def seq_phragmen_with_equalise(votelist, num_to_elect):
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nomlist, elected_candidates = seq_phragmen(votelist, num_to_elect)
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equalise_all(nomlist, 2, 0)
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return nomlist, elected_candidates
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def calculateMaxScoreNoCutoff(nomlist, candidates):
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# First we compute the denominator of the score
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for candidate in candidates:
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if not candidate.elected:
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candidate.scoredenom = 1.0
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for nom in nomlist:
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denominator_contrib = 0
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for edge in nom.edges:
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if edge.candidate.elected:
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denominator_contrib += edge.weight/edge.candidate.backed_stake
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for edge in nom.edges:
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if not edge.candidate.elected:
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edge.candidate.scoredenom += denominator_contrib
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# Then we divide. Not that score here is comparable to the recipricol of the score in
|
||||
# seq-phragmen. In particular there low scores are good whereas here high scores are good.
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best_candidate = 0
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best_score = 0.0
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for candidate in candidates:
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if candidate.approval_stake > 0.0:
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candidate.score = candidate.approval_stake / candidate.scoredenom
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print("score of {} in this round is {}".format(candidate.validator_id, candidate.score))
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if not candidate.elected and candidate.score > best_score:
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best_score = candidate.score
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best_candidate = candidate
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else:
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candidate.score = 0.0
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return (best_candidate, best_score)
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def electWithScore(nomlist, elected_candidate, cutoff):
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for nom in nomlist:
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for new_edge in nom.edges:
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if new_edge.validator_id == elected_candidate.validator_id:
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used_budget = sum([edge.weight for edge in nom.edges])
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new_edge.weight = nom.budget - used_budget
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elected_candidate.backed_stake += nom.budget - used_budget
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for edge in nom.edges:
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if edge.validator_id != elected_candidate.validator_id and edge.weight > 0.0:
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||||
if edge.candidate.backed_stake > cutoff:
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stake_to_take = edge.weight * cutoff / edge.candidate.backed_stake
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new_edge.weight += stake_to_take
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||||
edge.weight -= stake_to_take
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||||
edge.candidate.backed_stake -= stake_to_take
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||||
elected_candidate.backed_stake += stake_to_take
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||||
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||||
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def balanced_heuristic(votelist, num_to_elect, tolerance=0.1):
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nomlist, candidates = setuplists(votelist)
|
||||
calculate_approval(nomlist)
|
||||
|
||||
elected_candidates = list()
|
||||
for round in range(num_to_elect):
|
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(elected_candidate, score) = calculateMaxScoreNoCutoff(nomlist, candidates)
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||||
electWithScore(nomlist, elected_candidate, score)
|
||||
print("####\nRound {} max candidate {} with score {}".format(round, elected_candidate.validator_id, score))
|
||||
print_list(nomlist)
|
||||
|
||||
elected_candidate.elected = True
|
||||
elected_candidates.append(elected_candidate)
|
||||
elected_candidate.electedpos = round
|
||||
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||||
equalise_all(nomlist, 10, tolerance)
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||||
print("After balancing")
|
||||
print_list(nomlist)
|
||||
|
||||
return nomlist, elected_candidates
|
||||
|
||||
|
||||
def approval_voting(votelist, num_to_elect):
|
||||
nomlist, candidates = setuplists(votelist)
|
||||
# Compute the total possible stake for each candidate
|
||||
for nom in nomlist:
|
||||
for edge in nom.edges:
|
||||
edge.candidate.approvalstake += nom.budget
|
||||
|
||||
electedcandidates=list()
|
||||
for round in range(numtoelect):
|
||||
electedcandidate,score=calculateMaxScoreNoCutoff(nomlist,candidates)
|
||||
electWithScore(nomlist, electedcandidate, score)
|
||||
electedcandidate.elected=True
|
||||
electedcandidates.append(electedcandidate)
|
||||
electedcandidate.electedpos=round
|
||||
equaliseall(nomlist,100,tolerance)
|
||||
return nomlist,electedcandidates
|
||||
edge.candidate.approval_stake += nom.budget
|
||||
edge.weight = nom.budget/min(len(nom.edges), num_to_elect)
|
||||
edge.candidate.backed_stake += edge.weight
|
||||
candidates.sort(key=lambda x: x.approval_stake, reverse=True)
|
||||
elected_candidates = candidates[0:num_to_elect]
|
||||
return nomlist, elected_candidates
|
||||
|
||||
|
||||
def calculate_approval(nomlist):
|
||||
for nom in nomlist:
|
||||
for edge in nom.edges:
|
||||
edge.candidate.approval_stake += nom.budget
|
||||
|
||||
|
||||
def setuplists(votelist):
|
||||
'''
|
||||
Basically populates edge.candidate, and returns nomlist and candidate array. The former is a
|
||||
flat list of nominators and the latter is a flat list of validator candidates.
|
||||
|
||||
Instead of Python's dict here, you can use anything with O(log n) addition and lookup. We can
|
||||
also use a hashmap like dict, by generating a random constant r and useing H(canid+r) since the
|
||||
naive thing is obviously attackable.
|
||||
'''
|
||||
nomlist = [nominator(votetuple[0], votetuple[1], votetuple[2]) for votetuple in votelist]
|
||||
# Basically used as a cache.
|
||||
candidate_dict = dict()
|
||||
candidate_array = list()
|
||||
num_candidates = 0
|
||||
# Get an array of candidates.# We could reference these by index rather than pointer
|
||||
for nom in nomlist:
|
||||
for edge in nom.edges:
|
||||
validator_id = edge.validator_id
|
||||
if validator_id in candidate_dict:
|
||||
index = candidate_dict[validator_id]
|
||||
edge.candidate = candidate_array[index]
|
||||
else:
|
||||
candidate_dict[validator_id] = num_candidates
|
||||
newcandidate = candidate(validator_id, num_candidates)
|
||||
candidate_array.append(newcandidate)
|
||||
|
||||
edge.candidate = newcandidate
|
||||
num_candidates += 1
|
||||
return nomlist, candidate_array
|
||||
|
||||
|
||||
def run_and_print_all(votelist, to_elect):
|
||||
print("######\nVotes ", votelist)
|
||||
|
||||
print("\nSequential Phragmén gives")
|
||||
nomlist, elected_candidates = seq_phragmen(votelist, to_elect)
|
||||
printresult(nomlist, elected_candidates)
|
||||
|
||||
print("\nApproval voting gives")
|
||||
nomlist, elected_candidates = approval_voting(votelist, to_elect)
|
||||
printresult(nomlist, elected_candidates)
|
||||
|
||||
print("\nSequential Phragmén with post processing gives")
|
||||
nomlist, elected_candidates = seq_phragmen_with_equalise(votelist, to_elect)
|
||||
printresult(nomlist, elected_candidates)
|
||||
|
||||
print("\nBalanced Heuristic (3.15 factor) gives")
|
||||
nomlist, elected_candidates = balanced_heuristic(votelist, to_elect)
|
||||
printresult(nomlist, elected_candidates)
|
||||
|
||||
|
||||
def printresult(nomlist, elected_candidates, verbose=True):
|
||||
for candidate in elected_candidates:
|
||||
print(candidate.validator_id, " is elected with stake ",
|
||||
candidate.backed_stake, "and score ", candidate.score)
|
||||
if verbose:
|
||||
for nom in nomlist:
|
||||
print(nom.nominator_id, " has load ", nom.load, "and supported ")
|
||||
for edge in nom.edges:
|
||||
print(edge.validator_id, " with stake ", edge.weight, end=", ")
|
||||
print()
|
||||
print()
|
||||
|
||||
|
||||
def example1():
|
||||
votelist=[("A",10.0,["X","Y"]),("B",20.0,["X","Z"]),("C",30.0,["Y","Z"])]
|
||||
print("Votes ",votelist)
|
||||
nomlist, electedcandidates = seqPhragmén(votelist,2)
|
||||
print("Sequential Phragmén gives")
|
||||
printresult(nomlist, electedcandidates)
|
||||
nomlist, electedcandidates = approvalvoting(votelist,2)
|
||||
print()
|
||||
print("Approval voting gives")
|
||||
printresult(nomlist, electedcandidates)
|
||||
nomlist, electedcandidates = seqPhragménwithpostprocessing(votelist,2)
|
||||
print("Sequential Phragmén with post processing gives")
|
||||
printresult(nomlist, electedcandidates)
|
||||
nomlist, electedcandidates = factor3point15(votelist,2)
|
||||
print("Factor 3.15 thing gives")
|
||||
printresult(nomlist, electedcandidates)
|
||||
votelist = [
|
||||
("A", 10.0, ["X", "Y"]),
|
||||
("B", 20.0, ["X", "Z"]),
|
||||
("C", 30.0, ["Y", "Z"]),
|
||||
]
|
||||
run_and_print_all(votelist, 2)
|
||||
|
||||
|
||||
def example2():
|
||||
votelist = [
|
||||
("10", 1000, ["10"]),
|
||||
("20", 1000, ["20"]),
|
||||
("30", 1000, ["30"]),
|
||||
("40", 1000, ["40"]),
|
||||
('2', 500, ['10', '20', '30']),
|
||||
('4', 500, ['10', '20', '40'])
|
||||
]
|
||||
print("Votes ",votelist)
|
||||
nomlist, electedcandidates = seqPhragmén(votelist,2)
|
||||
print("Sequential Phragmén gives")
|
||||
printresult(nomlist, electedcandidates)
|
||||
nomlist, electedcandidates = approvalvoting(votelist,2)
|
||||
print()
|
||||
print("Approval voting gives")
|
||||
printresult(nomlist, electedcandidates)
|
||||
nomlist, electedcandidates = seqPhragménwithpostprocessing(votelist,2)
|
||||
print("Sequential Phragmén with post processing gives")
|
||||
printresult(nomlist, electedcandidates)
|
||||
nomlist, electedcandidates = factor3point15(votelist,2)
|
||||
print("Factor 3.15 thing gives")
|
||||
printresult(nomlist, electedcandidates)
|
||||
("10", 1000, ["10"]),
|
||||
("20", 1000, ["20"]),
|
||||
("30", 1000, ["30"]),
|
||||
("40", 1000, ["40"]),
|
||||
('2', 500, ['10', '20', '30']),
|
||||
('4', 500, ['10', '20', '40'])
|
||||
]
|
||||
run_and_print_all(votelist, 2)
|
||||
|
||||
import unittest
|
||||
class electiontests(unittest.TestCase):
|
||||
def testexample1Phragmén(self):
|
||||
votelist=[("A",10.0,["X","Y"]),("B",20.0,["X","Z"]),("C",30.0,["Y","Z"])]
|
||||
nomlist, electedcandidates = seqPhragmén(votelist,2)
|
||||
self.assertEqual(electedcandidates[0].valiid,"Z")
|
||||
self.assertAlmostEqual(electedcandidates[0].score,0.02)
|
||||
self.assertEqual(electedcandidates[1].valiid,"Y")
|
||||
self.assertAlmostEqual(electedcandidates[1].score,0.04)
|
||||
def testexample1approval(self):
|
||||
votelist=[("A",10.0,["X","Y"]),("B",20.0,["X","Z"]),("C",30.0,["Y","Z"])]
|
||||
nomlist, electedcandidates = approvalvoting(votelist,2)
|
||||
self.assertEqual(electedcandidates[0].valiid,"Z")
|
||||
self.assertAlmostEqual(electedcandidates[0].approvalstake,50.0)
|
||||
self.assertEqual(electedcandidates[1].valiid,"Y")
|
||||
self.assertAlmostEqual(electedcandidates[1].approvalstake,40.0)
|
||||
def dotests():
|
||||
|
||||
class MaxScoreTest(unittest.TestCase):
|
||||
def test_max_score_1(self):
|
||||
votelist = [
|
||||
(10, 10.0, [1, 2]),
|
||||
(20, 20.0, [1, 3]),
|
||||
(30, 30.0, [2, 3]),
|
||||
]
|
||||
nomlist, candidates = setuplists(votelist)
|
||||
calculate_approval(nomlist)
|
||||
|
||||
best, score = calculateMaxScoreNoCutoff(nomlist, candidates)
|
||||
self.assertEqual(best.validator_id, 3)
|
||||
self.assertEqual(score, 50)
|
||||
|
||||
def test_balance_heuristic_example_1(self):
|
||||
votelist = [
|
||||
(10, 10.0, [1, 2]),
|
||||
(20, 20.0, [1, 3]),
|
||||
(30, 30.0, [2, 3]),
|
||||
]
|
||||
nomlist, winners = balanced_heuristic(votelist, 2, 0)
|
||||
self.assertEqual(winners[0].validator_id, 3)
|
||||
self.assertEqual(winners[1].validator_id, 2)
|
||||
|
||||
self.assertEqual(winners[0].backed_stake, 30)
|
||||
self.assertEqual(winners[1].backed_stake, 30)
|
||||
|
||||
def test_balance_heuristic_example_linear(self):
|
||||
votelist = [
|
||||
(2, 2000, [11]),
|
||||
(4, 1000, [11, 21]),
|
||||
(6, 1000, [21, 31]),
|
||||
(8, 1000, [31, 41]),
|
||||
(110, 1000, [41, 51]),
|
||||
(120, 1000, [51, 61]),
|
||||
(130, 1000, [61, 71]),
|
||||
]
|
||||
|
||||
nomlist, winners = balanced_heuristic(votelist, 4, 0)
|
||||
self.assertEqual(winners[0].validator_id, 11)
|
||||
self.assertEqual(winners[0].backed_stake, 3000)
|
||||
|
||||
self.assertEqual(winners[1].validator_id, 31)
|
||||
self.assertEqual(winners[1].backed_stake, 2000)
|
||||
|
||||
self.assertEqual(winners[2].validator_id, 51)
|
||||
self.assertEqual(winners[2].backed_stake, 1500)
|
||||
|
||||
self.assertEqual(winners[3].validator_id, 61)
|
||||
self.assertEqual(winners[3].backed_stake, 1500)
|
||||
|
||||
|
||||
|
||||
class ElectionTest(unittest.TestCase):
|
||||
def test_phragmen(self):
|
||||
votelist = [
|
||||
("A", 10.0, ["X", "Y"]),
|
||||
("B", 20.0, ["X", "Z"]),
|
||||
("C", 30.0, ["Y", "Z"]),
|
||||
]
|
||||
nomlist, elected_candidates = seq_phragmen(votelist, 2)
|
||||
self.assertEqual(elected_candidates[0].validator_id, "Z")
|
||||
self.assertAlmostEqual(elected_candidates[0].score, 0.02)
|
||||
self.assertEqual(elected_candidates[1].validator_id, "Y")
|
||||
self.assertAlmostEqual(elected_candidates[1].score, 0.04)
|
||||
|
||||
def test_approval(self):
|
||||
votelist = [
|
||||
("A", 10.0, ["X", "Y"]),
|
||||
("B", 20.0, ["X", "Z"]),
|
||||
("C", 30.0, ["Y", "Z"]),
|
||||
]
|
||||
nomlist, elected_candidates = approval_voting(votelist, 2)
|
||||
self.assertEqual(elected_candidates[0].validator_id, "Z")
|
||||
self.assertAlmostEqual(elected_candidates[0].approval_stake, 50.0)
|
||||
self.assertEqual(elected_candidates[1].validator_id, "Y")
|
||||
self.assertAlmostEqual(elected_candidates[1].approval_stake, 40.0)
|
||||
|
||||
|
||||
def main():
|
||||
# example1()
|
||||
example2()
|
||||
# example3()
|
||||
|
||||
|
||||
if len(sys.argv) >= 2:
|
||||
if sys.argv[1] == "run":
|
||||
main()
|
||||
else:
|
||||
unittest.main()
|
||||
else:
|
||||
unittest.main()
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user