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| def memberFollower(x): | |
| low, avg, high = 0,0,0 | |
| if x > 20000: | |
| low = 0 | |
| elif x > 15000 and x <= 20000: | |
| low = (20000 - x) / (20000-15000) | |
| elif x <= 15000: | |
| low = 1 | |
| if x > 55000 or x < 15000: | |
| avg = 0 | |
| elif x >= 15000 and x <= 25000: | |
| avg = (x - 15000)/(25000-15000) | |
| elif x >= 55000 and x >= 25000: | |
| avg = (55000 - x)/(55000 - 25000) | |
| if x > 65000: | |
| high = 1 | |
| elif x < 55000: | |
| high = 0 | |
| elif x >= 55000 and x <= 65000: | |
| high = (65000 - x)/(65000 - 55000) | |
| return [low, avg, high] | |
| def memberRate(x): | |
| low, avg, high = 0,0,0 | |
| if x > 4: | |
| low = 0 | |
| elif x <= 2: | |
| low = 1 | |
| elif x >= 4 and x < 2: | |
| low = (4 - x)/(4-2) | |
| if x > 7 or x < 2: | |
| avg = 0 | |
| elif x >= 2 and x < 4: | |
| avg = (x-2)/(4-2) | |
| elif x >= 4 and x <= 6: | |
| avg = 1 | |
| elif x <= 7 and x > 6: | |
| avg = (7-x)/(7-6) | |
| if x >= 8: | |
| high = 1 | |
| elif x < 7: | |
| high = 0 | |
| elif x >= 7 and x < 8: | |
| high = (7-x)/(8-7) | |
| return [low, avg, high] | |
| def inference(follower, rate): | |
| rej = max(max(follower[0], rate[0]), max(follower[0], rate[1]), max(follower[1], rate[0])) | |
| consd = max(max(follower[0], rate[2]), max(follower[1], rate[1]), max(follower[2], rate[0])) | |
| acc = max(max(follower[1], rate[2]), max(follower[2], rate[1]), max(follower[2], rate[2])) | |
| return [rej, consd, acc] | |
| def sugeno(param): | |
| return ((50 * param[0]) + (70 * param[1]) + (100 * param[2]))/sum(param) | |
| datas = [ data.split(',') for data in open('influencers.csv').read().splitlines()[1:] ] | |
| datas = [ [int(data[0]), int(data[1]), float(data[2])] for data in datas ] | |
| for data in datas: | |
| follower = memberFollower(data[1]) | |
| rate = memberRate(data[2]) | |
| inf = inference(follower, rate) | |
| data.append(round(sugeno(inf),2)) | |
| datas = sorted(datas, reverse=True, key= lambda x: x[3]) | |
| print datas[:20] |
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