def formate_teams(k, agents):
alpha = 0.58/3
beta = 3 * alpha
gamma = 0.1
alpha_vector = np.array([0, alpha, alpha, alpha, 0])
beta_vector = np.array([0, 0, (-1) * beta, 0, 0])
#gamma_vector = np.array([0, 0, 0, 0, gamma])
team_distribution = list(calculate_number_of_teams(agents.shape[0],k))
print "team distribution", team_distribution
agentsAssigned =[]
teamDistribution = []
time1 = time.time()
while (team_distribution[0][0] > 0 or team_distribution[1][0] > 0):
if (team_distribution[0][0] > 0):
combinations = list(itertools.combinations(enumerate(agents), team_distribution[0][1]))
#generate all possible combinations of number_of_teams calculated above
elements = list(itertools.combinations(agents, team_distribution[0][1]))
Result = calculate_g_foreach_combination(elements, alpha_vector, beta_vector, gamma)
teams_generated, team_dist,agentsUsed = generate_teams(team_distribution[0], Result, combinations,agentsAssigned)
team_distribution[0] = team_dist
elif (team_distribution[1][0] > 0):
combinations = list(itertools.combinations(enumerate(agents), team_distribution[1][1]))
#print "combinations", combinations
#generate all possible combinations of number_of_teams calculated above
elements = list(itertools.combinations(agents, team_distribution[1][1]))
Result = calculate_g_foreach_combination(elements, alpha_vector, beta_vector, gamma)
teams_generated, team_dist, agentsUsed = generate_teams(team_distribution[1], Result, combinations,agentsAssigned)
team_distribution[1] = team_dist
else:
print "no teams left"
break
time2 = time.time()
print "time of generating all combinations: ", time2-time1
for m in teams_generated:
teamDistribution.append(m)
for i in agentsUsed:
if i not in agentsAssigned:
agentsAssigned.append(i)
#print "first distribution: "
#for i in teamDistribution:
# print i
print "teamDistribution: ", teamDistribution
old_F_value=calculate_f(teamDistribution)
print "first value for all teams: ", old_F_value
new_F_value =0.0
t_start= time.time()
t_end = t_start + 0.5
count_runs = 0
end_constraint = 50
time_table=[]
f_results=[]
while (time.time() < t_end and count_runs < end_constraint):
randomList = random.sample(xrange(len(teamDistribution)),2)
#print "these are random 2 elements: ", randomList
agentsValues = []
newAgentList=[]
agentsIds = []
old_g_value = 1
for z in randomList:
old_g_value = old_g_value * teamDistribution[z][1]
for l in teamDistribution[z][0]:
agentsValues.append(l[1])
agentsIds.append(l[0])
newAgentList.append(teamDistribution[z])
print "current g value for selected teams: ", old_g_value
agentsValues = np.array(agentsValues)
a = calculate_number_of_teams(len(agentsValues),k)
length = int(len(agentsValues))
number_of_teams = int(a[0][0] + a[1][0])
number1 = a[0][0]
number2 = a[1][0]
teamSizes = []
for i in range(0,number_of_teams):
if number1 >0:
teamSizes.append(a[0][1])
number1 -=1
else:
teamSizes.append(a[1][1])
number2 -=1
#print teamSizes
team = []
Result = {}
numberOfTeams = len(teamSizes)
teamSizes.sort()
if numberOfTeams > 1:
(partition,maximums) = initPartition(length,number_of_teams)
#print partition
while True:
(partition,maximums) = nextPartition(partition,maximums,number_of_teams,length)
if partition == None:
break
if filterPartition(partition,teamSizes):
#print "Filtered partition = ", partition
for i in range(0,number_of_teams):
for p in range(0,len(partition)):
if (partition[p] == i):
team.append(agentsValues[p])
x= []
y= []
z =[]
for a in team:
x.append(a[0])
y.append(a[1])
z.append(a[4])
max1, max2 = calculate_max(alpha_vector, beta_vector, team)
gender = calculate_gender_balance(z)
combination = str(partition).replace(', ',"")
if combination in Result:
Result[combination] = Result[combination] * calculate_g(x, y, gamma, max1, max2, gender)
else:
Result[combination] = calculate_g(x, y, gamma, max1, max2, gender)
team =[]
elif numberOfTeams == 1:
print length * [0]
#print Result
maxim = max(Result.iteritems(), key=operator.itemgetter(1))[0]
#sorted_x = sorted(Result.items(), key=operator.itemgetter(1), reverse=True)
#print "new value for selected teams:", maxim, Result.get(maxim)
#print "sorted ", sorted_x
first_team = []
second_team=[]
first_team_g = []
second_team_g=[]
if old_g_value < Result.get(maxim):
#print "I'm updating team distribution"
#print "agentsIds: ", agentsIds
for index, elem in enumerate(maxim):
#print index, elem
if (elem == "0"):
first_team.append((agentsIds[index-1], agentsValues[index-1]))
first_team_g.append(agentsValues[index-1])
elif (elem == "1"):
second_team.append((agentsIds[index-1], agentsValues[index-1]))
second_team_g.append(agentsValues[index-1])
#print "first_team, second_team: ", first_team, second_team
g_values = calculate_g_foreach_combination((first_team_g, second_team_g), alpha_vector, beta_vector, gamma)
new_teams = ((tuple(first_team), g_values.get(0)), (tuple(second_team), g_values.get(1)))
for index, value in enumerate(randomList):
#print index, value
#print teamDistribution[value]
#print new_teams[index]
teamDistribution[value] = new_teams[index]
#print "updated teams: ", teamDistribution
new_F_value=calculate_f(teamDistribution)
f_results.append(new_F_value)
print "updated value for all teams: ", new_F_value
count_runs = 0
time_effect = time.time()
time_table.append(time_effect-t_start)
else:
count_runs += 1
#print "time_table: ", time_table
#print "f_results: ", f_results
optimum = 1.15793102184 #3 members 15 agents[010232021343414] 0.299874562273
distance_to_optimum = []
for i in f_results:
distance_to_optimum.append(i/optimum)
#all_runs_optimum_ratio.append(distance_to_optimum[-1])
#all_runs_time.append(time_table[-1])
# plt.plot(time_table, distance_to_optimum)
# plt.xlabel('Time')
# plt.ylabel('Distance to optimum')
# plt.show()
return distance_to_optimum[-1], time_table[-1]
def main():
time_start= time.time()
alpha = 0.58/3
beta = 3 * alpha
gamma = 0.1
alpha_vector = np.array([0, alpha, alpha, alpha, 0])
beta_vector = np.array([0, 0, (-1) * beta, 0, 0])
## S T E J
## N F I P
agent0 = np.array([-0.2, 0.6, 0.6, -0.4, 1]) ##Maggy
agent1 = np.array([-0.2, 0.6, 0.5, 0.6, 0]) ##Steve ETJ
agent2 = np.array([-0.2, -0.2, -0.4, 0.6, 0]) ##Marsh I
agent3 = np.array([ 0.6, 0.2, 0.2, -0.6, 0]) ##Chaz
agent4 = np.array([-0.2, -0.6, 0.2, -0.2, 0]) ##Benny
agent5 = np.array([ 0.0, 0.4, -0.8, 0.6, 1]) ##Olive I
agent6 = np.array([ 0.6, 0.2, 0.6, 0.4, 0]) ##Sam ETJ
agent7 = np.array([-0.2, -0.2, 0.4, 0.6, 1]) ##Micheline
agent8 = np.array([ 0.4, -0.4, 0.6, 0.0, 0]) ##Robin
agent9 = np.array([ 0.1, 0.2, -0.4, 0.2, 0]) ##John I
agent10 = np.array([ 0.2, 0.8, -0.6, -0.4, 0]) ##Jimmy I
agent11 = np.array([ 0.6, 0.2, 0.8, 0.6, 1]) ##Camille ETJ
agent12 = np.array([ 0.4, -0.4, 0.4, 0.6, 1]) ##Annie
agent13 = np.array([ 0.6, -0.4, 0.6, -0.6, 1]) ##Sandra
agent14 = np.array([-0.6, -0.4, 0.2, -0.2, 0]) ##Lars
agent15 = np.array([-0.2, -0.2, 0.4, 0.6, 1]) ##Selena
agent16 = np.array([-0.6, 1.0, 0.6, 0.6, 0]) ##Brad ETJ
agent17 = np.array([ 0.6, 0.2, 0.2, -0.2, 0]) ##Carl
agent18 = np.array([ 0.0, 0.2, 0.4, -0.4, 0]) ##Danton
agent19 = np.array([-0.2, -0.6, 0.0, -0.8, 1]) ##Lisa
agents = np.array([agent0 , agent1, agent2, agent3, agent4, agent5, agent6, agent7, agent8, agent9, agent10, agent11, agent12, agent13])#, agent14, agent15, agent16, agent17, agent18, agent19])
k = 2
a = calculate_number_of_teams(agents.shape[0],k)
#print a
#print a[0]
#print agents.shape[0]
length = int(agents.shape[0])
size = int(a[0][0] + a[1][0])
number1 = a[0][0]
number2 = a[1][0]
teamSizes = []
for i in range(0,size):
if number1 >0:
teamSizes.append(a[0][1])
number1 -=1
else:
teamSizes.append(a[1][1])
number2 -=1
print length, size, teamSizes
teamSizes.sort()
team = []
#initial=[1]*(comb(agents.shape[0],k))
#Result = {n: initial[n] for n in range(comb(agents.shape[0],k))}
Result = {}
numberOfTeams = len(teamSizes)
if numberOfTeams > 1:
(partition,maximums) = initPartition(length,size)
while True:
(partition,maximums) = nextPartition(partition,maximums,size,length)
if partition == None:
break
if filterPartition(partition,teamSizes):
#print "Filtered partition = ", partition
for i in range(0,size):
for p in range(0,len(partition)):
if (partition[p] == i):
team.append(agents[p])
x= []
y= []
z = []
for a in team:
x.append(a[0])
y.append(a[1])
z.append(a[4])
max1, max2 = calculate_max(alpha_vector, beta_vector, team)
#print max1, max2
count= str(partition).replace(', ',"")
gender = calculate_gender_balance(z)
if count in Result:
#print calculate_g(x, y, gamma, max1, max2, gender)
Result[count] = Result[count] * calculate_g(x, y, gamma, max1, max2, gender)
else:
#print calculate_g(x, y, gamma, max1, max2, gender)
Result[count] = calculate_g(x, y, gamma, max1, max2, gender)
#if count in Result:
# Result[count] = Result[count] * calculate_g(x, y, max1, max2)
#else:
# Result[count] = calculate_g(x, y, max1, max2)
#Result.update(count: Result[count] * calculate_g(x, y, max1, max2))
#print team
team =[]
elif numberOfTeams == 1:
print length * [0]
#print Result
maxim = max(Result.iteritems(), key=operator.itemgetter(1))[0]
#sorted_x = sorted(Result.items(), key=operator.itemgetter(1), reverse=True)
print k," members ", len(agents), " agents", maxim, Result.get(maxim)
#print "sorted ", sorted_x
print "time in sec: ", time.time() - time_start