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seeding.py
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seeding.py
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import csv
import random
from statistics import mean
SIMULATION_COUNT = 1000000
mapping = {}
def read_csv(file_name, battletag_col_location, mmr_col_location):
with open(file_name, newline='', encoding='utf-8') as csvfile:
reader = csv.reader(csvfile, delimiter=',')
#skip header
next(reader)
for row in reader:
mapping[row[battletag_col_location]] = int(row[mmr_col_location])
def seed(mapping, group_num, group_size):
assert group_num * group_size == len(mapping)
tuple_list = [(k, v) for k, v in mapping.items()]
mmr_avg = mean(mapping.values())
closest_delta = 99999
closest_groups = []
for _ in range(SIMULATION_COUNT):
total_delta = 0
groups_list = []
random.shuffle(tuple_list)
start = 0
while start < len(mapping):
groups_list.append(tuple_list[start: start + group_size])
start += group_size
for group in groups_list:
group_total = 0
for tup in group:
group_total = group_total + tup[1]
group_avg = group_total / group_size
total_delta += abs(mmr_avg - group_avg)
if total_delta < closest_delta:
closest_delta = total_delta
closest_groups = groups_list
print(f'How far away a group was on average to the total average MMR of the competitors: {closest_delta/group_num}')
print(printGroups(closest_groups))
def printGroups(groups_list):
s = ''
for index, group in enumerate(groups_list):
s += f'Group {index + 1}: {group} \n'
return s
read_csv('Players.csv', 2, 3)
seed(mapping, 6, 8)