310 lines
6.7 KiB
Python
Executable File
310 lines
6.7 KiB
Python
Executable File
# %%
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PROJECT_PATH = '/home/md/Work/ligalytics/leagues_stable/'
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import os, sys
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sys.path.insert(0, PROJECT_PATH)
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os.environ.setdefault("DJANGO_SETTINGS_MODULE", "leagues.settings")
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os.environ["DJANGO_ALLOW_ASYNC_UNSAFE"] = "true"
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import random
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import time
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# XPRESS ENVIRONMENT
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os.environ['XPRESSDIR'] = "/opt/xpressmp_8.4"
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os.environ['XPRESS'] = "/opt/xpressmp_8.4/bin"
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os.environ['LD_LIBRARY_PATH'] = os.environ['XPRESSDIR']+"/lib:"+os.environ['LD_LIBRARY_PATH']
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os.environ['DYLD_LIBRARY_PATH'] = os.environ['XPRESSDIR']+"/lib:"
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os.environ['SHLIB_PATH'] = os.environ['XPRESSDIR']+"/lib:"
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os.environ['LIBPATH'] = os.environ['XPRESSDIR']+"/lib:"
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# os.environ['PYTHONPATH'] =os.environ['XPRESSDIR']+"/lib:"+os.environ['PYTHONPATH']
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os.environ['PYTHONPATH'] =os.environ['XPRESSDIR']+"/lib:"
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os.environ['CLASSPATH'] =os.environ['XPRESSDIR']+"/lib/xprs.jar:"
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os.environ['CLASSPATH'] =os.environ['XPRESSDIR']+"/lib/xprb.jar:"+os.environ['CLASSPATH']
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os.environ['CLASSPATH'] =os.environ['XPRESSDIR']+"/lib/xprm.jar:"+os.environ['CLASSPATH']
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os.environ['PATH'] =os.environ['XPRESSDIR']+"/bin:"+os.environ['PATH']
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from leagues import settings
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settings.DATABASES['default']['NAME'] = PROJECT_PATH+'/db.sqlite3'
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import django
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django.setup()
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from scheduler.models import *
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from pulp import *
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import csv
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import xpress as xp
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xp.controls.outputlog = 1
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scenario = Scenario.objects.get(id=32)
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# %%
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teams = Team.objects.filter(season=scenario.season,active=True)
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getTeamByID = {
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t.id:t for t in teams
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}
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countries = teams.values_list('country', flat=True).distinct()
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teams_from_country = {
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c:[t for t in teams if t.country==c] for c in countries
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}
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pot = {}
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for i in teams.values_list('pot',flat=True).distinct():
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pot[i] = teams.filter(pot=i)
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# directed version
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# requirement = {
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# 1:[1,-1,2,-2,3,-3,4,-4],
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# 2:[1,-1,2,-2,3,-3,4,-4],
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# 3:[1,-1,2,-2,3,-3,4,-4],
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# 4:[1,-1,2,-2,3,-3,4,-4],
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# }
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# undirected version
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requirement = {
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1:{
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1:2,
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2:2,
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3:2,
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4:2,
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},
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2:{
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1:2,
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2:2,
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3:2,
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4:2,
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},
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3:{
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1:2,
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2:2,
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3:2,
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4:2,
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},
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4:{
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1:2,
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2:2,
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3:2,
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4:2,
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},
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}
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def check_feasible(fixed_games):
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model = xp.problem(name='Draws', sense=xp.minimize)
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x = {}
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for t1 in teams:
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for t2 in teams:
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if t1.country != t2.country:
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x[t1.id, t2.id] = xp.var(ub=1, vartype=xp.integer)
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model.addVariable(x)
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# REQUIREMENTS
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for t in teams:
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for r,val in requirement[t.pot].items():
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model.addConstraint(xp.Sum(x[t.id,t2.id] for t2 in pot[r] if (t.id,t2.id) in x.keys()) == val)
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for c in countries:
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if c != t.country:
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model.addConstraint(xp.Sum(x[t.id,t2.id] for t2 in teams_from_country[c]) <= 3)
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# FIXATIONS
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for (t1,t2) in fixed_games:
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# print("FIXING",t1,t2)
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model.addConstraint(x[t1,t2] == 1)
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for (t1,t2) in x.keys():
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model.addConstraint(x[t1,t2] == x[t2,t1])
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start_time = time()
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model.solve()
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comp_time = time()-start_time
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if model.getProbStatus() != 6:
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# print("INFEASIBLE FOUND")
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return False
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else:
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return True
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def random_matrix():
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model = xp.problem(name='Draws', sense=xp.minimize)
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x = {}
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for t1 in teams:
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for t2 in teams:
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if t1.country != t2.country:
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x[t1.id, t2.id] = xp.var(ub=1, vartype=xp.integer)
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model.addVariable(x)
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# REQUIREMENTS
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for t in teams:
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for r,val in requirement[t.pot].items():
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model.addConstraint(xp.Sum(x[t.id,t2.id] for t2 in pot[r] if (t.id,t2.id) in x.keys()) == val)
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for c in countries:
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if c != t.country:
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model.addConstraint(xp.Sum(x[t.id,t2.id] for t2 in teams_from_country[c]) <= 3)
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for (t1,t2) in x.keys():
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model.addConstraint(x[t1,t2] == x[t2,t1])
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model.setObjective(xp.Sum(random.uniform(0,1)*x[key] for key in x.keys()))
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start_time = time()
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model.solve()
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comp_time = time()-start_time
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if model.getProbStatus() != 6:
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# print("INFEASIBLE FOUND")
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return False
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else:
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solution = []
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for key in x.keys():
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if model.getSolution(x[key]) > 0:
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solution.append((key[0],key[1]))
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return solution
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# %%
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for k in range(1,1001):
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print("SIMULATION",k)
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teams = Team.objects.filter(season=scenario.season,active=True)
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countries = teams.values_list('country', flat=True).distinct()
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teams_from_country = {
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c:[t for t in teams if t.country==c] for c in countries
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}
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pot = {}
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for i in teams.values_list('pot',flat=True).distinct():
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pot[i] = teams.filter(pot=i)
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# directed version
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# requirement = {
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# 1:[1,-1,2,-2,3,-3,4,-4],
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# 2:[1,-1,2,-2,3,-3,4,-4],
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# 3:[1,-1,2,-2,3,-3,4,-4],
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# 4:[1,-1,2,-2,3,-3,4,-4],
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# }
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# undirected version
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requirement = {
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1:{
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1:2,
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2:2,
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3:2,
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4:2,
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},
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2:{
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1:2,
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2:2,
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3:2,
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4:2,
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},
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3:{
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1:2,
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2:2,
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3:2,
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4:2,
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},
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4:{
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1:2,
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2:2,
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3:2,
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4:2,
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},
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}
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solution = random_matrix()
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if not check_feasible(solution):
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print("INFEASIBLE")
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exit()
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opps = {
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t:{
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p:[] for p in range(1,5)
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}
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for t in teams
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}
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for (s1,s2) in solution:
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t1 = getTeamByID[s1]
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t2 = getTeamByID[s2]
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opps[t1][t2.pot].append(t2.id)
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with open('verteilung_random.csv', "a") as f:
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for t in teams.order_by('pot'):
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f.write(f"{k},{t.id},{';'.join([str(a) for a in opps[t][1]])},{';'.join([str(a) for a in opps[t][2]])},{';'.join([str(a) for a in opps[t][3]])},{';'.join([str(a) for a in opps[t][4]])}\n")
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# # %%
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# %%
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teams = list(range(1,19))
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days = list(range(1,18))
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model = xp.problem(name='Draws', sense=xp.minimize)
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x = {}
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for t1 in teams:
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for t2 in teams:
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if t1 != t2:
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for d in days:
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x[t1,t2,d] = xp.var(ub=1, vartype=xp.integer)
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model.addVariable(x)
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# REQUIREMENTS
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for t1 in teams:
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for t2 in teams:
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if t1 != t2:
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model.addConstraint(xp.Sum(x[t1,t2,d] for d in days) == 1)
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for d in days:
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model.addConstraint(xp.Sum(x[t1,t2,d] for t2 in teams if t2 != t1) == 1)
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model.setObjective(xp.Sum(random.uniform(0,1)*x[key] for key in x.keys()))
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start_time = time()
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model.solve()
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comp_time = time()-start_time
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if model.getProbStatus() != 6:
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print("INFEASIBLE FOUND")
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else:
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for key in x.keys():
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if model.getSolution(x[key]) > 0:
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print(key)
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# %%
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