rename into english

code/majority_judgment_method/__init__.py

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+import json
+import sys
+from majority_judgment import majority_judgment
+
+verbose=False
+
+def collect_votes(votes,candidates,default_mention,warnings):
+	collect={}
+	for vote in votes:
+		# missing candidate in vote picks the worse one
+		for candidate in candidates:
+			if not candidate in vote:
+				vote[candidate]=default_mention            
+
+		for candidate,mention in vote.items():
+			if mention > default_mention or mention < 0:
+				mention = default_mention
+				warnings.append("index de mention invalide, soit la liste des mentions est erronnée soit la mention dans le vote est erronée")
+			if candidate in collect:
+				if mention in collect[candidate]:
+					collect[candidate][mention]=collect[candidate][mention]+1
+				else:
+					collect[candidate][mention]=1
+			else:
+				collect[candidate]={mention:1}
+	return collect
+	
+def arrange_votes_by_candidate(votes,candidates,default_mention):
+	vpc={}
+	for candidate in candidates:
+		vpc[candidate]=[vote[candidate] if candidate in vote else default_mention for vote in votes]	
+	return vpc
+
+def majority_judgment_run(poll):
+
+	verbose=False
+	crosscheck=True
+
+	voters=poll['voters']['count']
+	candidates=poll['candidates']
+	nombre_candidates=len(candidates)
+
+	# l'ordre des mentions est de la meilleure à la pire (reject)
+	nom_mentions=poll['mentions']
+	mentions=len(nom_mentions)
+	default_mention=mentions-1
+
+	votes=poll["votation"]["votes"]
+	if verbose:
+		print('candidates:' + str(candidates))
+		print(votes)
+
+	warnings=[]
+
+	collect=collect_votes(votes,list(candidates),default_mention,warnings)
+	if verbose:
+		print(collect)
+
+	voter_median_check= voters / 2 if voters % 2 == 0 else ((voters-1) / 2) +1
+
+	voter_median=voters // 2  + voters % 2
+
+	if voter_median_check != voter_median:
+			print('[ERROR] le nombre median de voters semble erroné. contactez le developpeur de ce code.' + str(voter_median_check))
+
+	merit={}
+	mention_mediane={}
+	merit_pourcent={}
+
+	votes_by_candidate=arrange_votes_by_candidate(votes,candidates,mentions-1)
+	
+	# cumul : du meilleur au pire
+	# range_mentions = range(len(mentions))
+	# cumul du pire au meilleur
+	range_mentions = range(mentions-1,-1,-1)
+
+	for candidate in list(candidates):
+		vote=collect[candidate]
+		cumul=0
+		m=[0 for i in range(mentions)]
+		mention_m=None
+		pourcent=[0 for i in range(mentions)]
+		for mention in range_mentions:
+			if mention in vote:
+				cumul=cumul+vote[mention]
+				pourcent[mention]= ( 100 * vote[mention] ) / voters
+			if cumul >= voter_median and mention_m is None:
+				mention_m=mention
+			m[mention]=cumul
+		if cumul < voters:
+			print('[ERROR] cumulated votes should match voters. Contact this code developer.')
+		mention_mediane[candidate]=mention_m
+		merit[candidate]=m
+		merit_pourcent[candidate]=pourcent
+
+	if verbose:
+		print(merit) 
+		print(mention_mediane)
+
+	# les gagnants sont ceux qui ont la mention mediane minimale ( la meilleure )
+	found=[]
+	for mention in range(0,mentions):
+		for candidate,merit_median in mention_mediane.items():
+			if merit_median == mention:
+				found.append({candidate:candidates[candidate]})
+		if len(found) > 0:
+			break
+
+	if verbose:
+		print(found) 
+		print(nom_mentions[mention])
+
+	# expanded=expand_collect(collect,list(candidates),mentions)
+	if verbose:
+		print(votes_by_candidatee)
+
+	result = {'result':{'mention':nom_mentions[mention],'candidates':found},'merit':merit,'profil':merit_pourcent,"warnings":warnings}
+
+	if crosscheck:
+		# cross check with a well known implementation
+		result["crosscheck"]=majority_judgment(votes_by_candidate, reverse=True)
+
+	return result
+