def PersnalizedPageRank_top10_SimilarActors(seed):
DataHandler.createDictionaries1()
DataHandler.create_actor_actorid_map()
actact = DataHandler.actor_actor_invSimilarity_matrix()
actor_actorid_map = DataHandler.actor_actorid_map
alpha = constants.ALPHA
act_similarities = ppr.personalizedPageRank(actact,seed,alpha)
actors = list(actact.index)
actorDF = pd.DataFrame(pd.Series(actors),columns = ['Actor'])
actorDF['Actor'] = actorDF['Actor'].map(lambda x:actor_actorid_map.get(x))
Result = pd.concat([act_similarities,actorDF],axis = 1)
sortedResult=Result.sort_values(by=0,ascending=False).head(15)
seedAcotorNames = [actor_actorid_map.get(i) for i in seed]
print('Actors similar to the following seed actors: '+str(seedAcotorNames))
for index in sortedResult.index:
if sortedResult.loc[index,'Actor'] not in seedAcotorNames:
print(sortedResult.loc[index,'Actor']+' '+ str(sortedResult.loc[index,0]))
