def classical_exactcover_solver(A, w=None, num_threads=4):
nrows, ncolumns = np.shape(A)
if w is None:
w = np.ones(ncolumns)
assert(len(w) == ncolumns)
assert(sum(w >= 0))
model = CyLPModel()
# Decision variables, one for each cover
x = model.addVariable('x', ncolumns, isInt=True)
# Adding the box contraints
model += 0 <= x <= 1
# Adding the cover constraints
# Sum_j x_ij == 1
for i in range(nrows):
model += CyLPArray(A[i,:]) * x == 1
# Adding the objective function
model.objective = CyLPArray(w) * x
lp = CyClpSimplex(model)
lp.logLevel = 0
lp.optimizationDirection = 'min'
mip = lp.getCbcModel()
mip.logLevel = 0
# Setting number of threads
mip.numberThreads = num_threads
mip.solve()
return mip.objectiveValue, [int(i) for i in mip.primalVariableSolution['x']]
def branch_and_bound(G, num_threads=4):
N = len(G)
model = CyLPModel()
# Decision variables, one for each node
x = model.addVariable('x', N, isInt=True)
# Adjacency matrix (possibly weighted)
W = nx.to_numpy_matrix(G)
z_ind = dict()
ind = 0
w = []
for i in range(N):
j_range = range(N)
if (not nx.is_directed(G)):
# Reduced range for undirected graphs
j_range = range(i, N)
for j in j_range:
if (W[i,j] == 0):
continue
if (i not in z_ind):
z_ind[i] = dict()
z_ind[i][j] = ind
w.append(W[i,j])
ind += 1
# Aux variables, one for each edge
z = model.addVariable('z', len(w), isInt=True)
# Adding the box contraints
model += 0 <= x <= 1
model += 0 <= z <= 1
# Adding the cutting constraints
# If x_i == x_j then z_ij = 0
# If x_i != x_j then z_ij = 1
for i in z_ind:
for j in z_ind[i]:
model += z[z_ind[i][j]] - x[i] - x[j] <= 0
model += z[z_ind[i][j]] + x[i] + x[j] <= 2
# Adding the objective function
model.objective = CyLPArray(w) * z
lp = CyClpSimplex(model)
lp.logLevel = 0
lp.optimizationDirection = 'max'
mip = lp.getCbcModel()
mip.logLevel = 0
# Setting number of threads
mip.numberThreads = num_threads
mip.solve()
return mip.objectiveValue, [int(i) for i in mip.primalVariableSolution['x']]
def test_multiDim_Cbc_solve(self):
from cylp.cy import CyClpSimplex
from cylp.py.modeling.CyLPModel import CyLPArray
s = CyClpSimplex()
x = s.addVariable('x', (5, 3, 6))
s += 2 * x[2, :, 3].sum() + 3 * x[0, 1, :].sum() >= 5.5
s += 0 <= x <= 2.2
c = CyLPArray(list(range(18)))
s.objective = c * x[2, :, :] + c * x[0, :, :]
s.setInteger(x)
cbcModel = s.getCbcModel()
cbcModel.solve()
sol_x = cbcModel.primalVariableSolution['x']
self.assertTrue(abs(sol_x[0, 1, 0] - 1) <= 10**-6)
self.assertTrue(abs(sol_x[2, 0, 3] - 2) <= 10**-6)
def test_multiDim_Cbc_solve(self):
from cylp.cy import CyClpSimplex
from cylp.py.modeling.CyLPModel import CyLPArray
s = CyClpSimplex()
x = s.addVariable('x', (5, 3, 6))
s += 2 * x[2, :, 3].sum() + 3 * x[0, 1, :].sum() >= 5.5
s += 0 <= x <= 2.2
c = CyLPArray(range(18))
s.objective = c * x[2, :, :] + c * x[0, :, :]
s.setInteger(x)
cbcModel = s.getCbcModel()
cbcModel.solve()
sol_x = cbcModel.primalVariableSolution['x']
self.assertTrue(abs(sol_x[0, 1, 0] - 1) <= 10**-6)
self.assertTrue(abs(sol_x[2, 0, 3] - 2) <= 10**-6)
def test_NodeCompare(self):
s = CyClpSimplex()
s.readMps(join(currentFilePath, '../input/p0033.mps'))
s.copyInIntegerInformation(np.array(s.nCols * [True], np.uint8))
print("Solving relaxation")
cbcModel = s.getCbcModel()
n = SimpleNodeCompare()
cbcModel.setNodeCompare(n)
gom = CyCglGomory(limit=150)
#gom.limit = 150
cbcModel.addCutGenerator(gom, name="Gomory")
#clq = CyCglClique()
#cbcModel.addCutGenerator(clq, name="Clique")
knap = CyCglKnapsackCover(maxInKnapsack=50)
cbcModel.addCutGenerator(knap, name="Knapsack")
cbcModel.branchAndBound()
self.assertTrue(abs(cbcModel.objectiveValue - 3089.0) < 10**-6)
def test_NodeCompare(self):
s = CyClpSimplex()
s.readMps(join(currentFilePath, '../input/p0033.mps'))
s.copyInIntegerInformation(np.array(s.nCols * [True], np.uint8))
print "Solving relaxation"
cbcModel = s.getCbcModel()
n = SimpleNodeCompare()
cbcModel.setNodeCompare(n)
gom = CyCglGomory(limit=150)
#gom.limit = 150
cbcModel.addCutGenerator(gom, name="Gomory")
#clq = CyCglClique()
#cbcModel.addCutGenerator(clq, name="Clique")
knap = CyCglKnapsackCover(maxInKnapsack=50)
cbcModel.addCutGenerator(knap, name="Knapsack")
cbcModel.branchAndBound()
self.assertTrue(abs(cbcModel.objectiveValue - 3089.0) < 10 ** -6)
if (firstExample):
x = m.addVariable('x', 2, isInt=True)
A = np.matrix([[7., -2.], [0., 1], [2., -2]])
b = CyLPArray([14, 3, 3])
m += A * x <= b
m += x >= 0
c = CyLPArray([-4, 1])
m.objective = c * x
s = CyClpSimplex(m)
else:
s = CyClpSimplex()
#cylpDir = os.environ['CYLP_SOURCE_DIR']
inputFile = os.path.join('..', '..', 'input', 'p0033.mps')
m = s.extractCyLPModel(inputFile)
x = m.getVarByName('x')
s.setInteger(x)
cbcModel = s.getCbcModel()
gc = GomoryCutGenerator(m)
#cbcModel.addPythonCutGenerator(gc, name='PyGomory')
#cbcModel.branchAndBound()
cbcModel.solve()
print(cbcModel.primalVariableSolution)
def solve(self, objective, constraints, cached_data, warm_start, verbose,
solver_opts):
"""Returns the result of the call to the solver.
Parameters
----------
objective : LinOp
The canonicalized objective.
constraints : list
The list of canonicalized cosntraints.
cached_data : dict
A map of solver name to cached problem data.
warm_start : bool
Not used.
verbose : bool
Should the solver print output?
solver_opts : dict
Additional arguments for the solver.
Returns
-------
tuple
(status, optimal value, primal, equality dual, inequality dual)
"""
# Import basic modelling tools of cylp
from cylp.cy import CyClpSimplex
# Get problem data
data = self.get_problem_data(objective, constraints, cached_data)
c = data[s.C]
b = data[s.B]
A = data[s.A]
dims = data[s.DIMS]
n = c.shape[0]
# Problem
model = CyClpSimplex()
# Variables
x = model.addVariable('x', n)
if self.is_mip(data):
for i in data[s.BOOL_IDX]:
model.setInteger(x[i])
for i in data[s.INT_IDX]:
model.setInteger(x[i])
# Constraints
# eq
model += A[0:dims[s.EQ_DIM], :] * x == b[0:dims[s.EQ_DIM]]
# leq
leq_start = dims[s.EQ_DIM]
leq_end = dims[s.EQ_DIM] + dims[s.LEQ_DIM]
model += A[leq_start:leq_end, :] * x <= b[leq_start:leq_end]
# no boolean vars available in cbc -> model as int + restrict to [0,1]
if self.is_mip(data):
for i in data[s.BOOL_IDX]:
model += 0 <= x[i] <= 1
# Objective
model.objective = c
# Build model & solve
status = None
if self.is_mip(data):
cbcModel = model.getCbcModel() # need to convert model
if not verbose:
cbcModel.logLevel = 0
# Add cut-generators (optional)
for cut_name, cut_func in six.iteritems(
self.SUPPORTED_CUT_GENERATORS):
if cut_name in solver_opts and solver_opts[cut_name]:
module = importlib.import_module("cylp.cy.CyCgl")
funcToCall = getattr(module, cut_func)
cut_gen = funcToCall()
cbcModel.addCutGenerator(cut_gen, name=cut_name)
# solve
status = cbcModel.branchAndBound()
else:
if not verbose:
model.logLevel = 0
status = model.primal() # solve
results_dict = {}
results_dict["status"] = status
if self.is_mip(data):
results_dict["x"] = cbcModel.primalVariableSolution['x']
results_dict["obj_value"] = cbcModel.objectiveValue
else:
results_dict["x"] = model.primalVariableSolution['x']
results_dict["obj_value"] = model.objectiveValue
return self.format_results(results_dict, data, cached_data)
def solve(self, objective, constraints, cached_data,
warm_start, verbose, solver_opts):
"""Returns the result of the call to the solver.
Parameters
----------
objective : LinOp
The canonicalized objective.
constraints : list
The list of canonicalized cosntraints.
cached_data : dict
A map of solver name to cached problem data.
warm_start : bool
Not used.
verbose : bool
Should the solver print output?
solver_opts : dict
Additional arguments for the solver.
Returns
-------
tuple
(status, optimal value, primal, equality dual, inequality dual)
"""
# Import basic modelling tools of cylp
from cylp.cy import CyClpSimplex
from cylp.py.modeling.CyLPModel import CyLPArray
# Get problem data
data = self.get_problem_data(objective, constraints, cached_data)
c = data[s.C]
b = data[s.B]
A = data[s.A]
dims = data[s.DIMS]
n = c.shape[0]
solver_cache = cached_data[self.name()]
# Problem
model = CyClpSimplex()
# Variables
x = model.addVariable('x', n)
if self.is_mip(data):
for i in data[s.BOOL_IDX]:
model.setInteger(x[i])
for i in data[s.INT_IDX]:
model.setInteger(x[i])
# Constraints
# eq
model += A[0:dims[s.EQ_DIM], :] * x == b[0:dims[s.EQ_DIM]]
# leq
leq_start = dims[s.EQ_DIM]
leq_end = dims[s.EQ_DIM] + dims[s.LEQ_DIM]
model += A[leq_start:leq_end, :] * x <= b[leq_start:leq_end]
# no boolean vars available in cbc -> model as int + restrict to [0,1]
if self.is_mip(data):
for i in data[s.BOOL_IDX]:
model += 0 <= x[i] <= 1
# Objective
model.objective = c
# Build model & solve
status = None
if self.is_mip(data):
cbcModel = model.getCbcModel() # need to convert model
if not verbose:
cbcModel.logLevel = 0
# Add cut-generators (optional)
for cut_name, cut_func in six.iteritems(self.SUPPORTED_CUT_GENERATORS):
if cut_name in solver_opts and solver_opts[cut_name]:
module = importlib.import_module("cylp.cy.CyCgl")
funcToCall = getattr(module, cut_func)
cut_gen = funcToCall()
cbcModel.addCutGenerator(cut_gen, name=cut_name)
# solve
status = cbcModel.branchAndBound()
else:
if not verbose:
model.logLevel = 0
status = model.primal() # solve
results_dict = {}
results_dict["status"] = status
if self.is_mip(data):
results_dict["x"] = cbcModel.primalVariableSolution['x']
results_dict["obj_value"] = cbcModel.objectiveValue
else:
results_dict["x"] = model.primalVariableSolution['x']
results_dict["obj_value"] = model.objectiveValue
return self.format_results(results_dict, data, cached_data)
if (firstExample):
x = m.addVariable('x', 2, isInt=True)
A = np.matrix([[7., -2.],[0., 1], [2., -2]])
b = CyLPArray([14, 3, 3])
m += A * x <= b
m += x >= 0
c = CyLPArray([-4, 1])
m.objective = c * x
s = CyClpSimplex(m)
else:
s = CyClpSimplex()
cylpDir = os.environ['CYLP_SOURCE_DIR']
inputFile = os.path.join(cylpDir, 'cylp', 'input', 'p0033.mps')
m = s.extractCyLPModel(inputFile)
x = m.getVarByName('x')
s.setInteger(x)
cbcModel = s.getCbcModel()
gc = GomoryCutGenerator(m)
cbcModel.addPythonCutGenerator(gc, name='PyGomory')
cbcModel.branchAndBound()
print cbcModel.primalVariableSolution
def classical_maxkcut_solver(G, num_partitions, num_threads=4):
# G: NetworkX graph
# num_partitions: the number partitions or groups in which we should
# subdivide the nodes (i.e., the value of K)
N = len(G)
model = CyLPModel()
# Decision variables, one for each node
x = model.addVariable('x', num_partitions * N, isInt=True)
# Adjacency matrix (possibly weighted)
W = nx.to_numpy_matrix(G)
z_ind = dict()
ind = 0
w = []
for i in range(N):
j_range = range(N)
if (not nx.is_directed(G)):
# Reduced range for undirected graphs
j_range = range(i, N)
for j in j_range:
if (W[i, j] == 0):
continue
if (i not in z_ind):
z_ind[i] = dict()
z_ind[i][j] = ind
w.append(W[i, j])
ind += 1
# Aux variables, one for each edge
z = model.addVariable('z', len(w), isInt=True)
# Adding the box contraints
model += 0 <= x <= 1
model += 0 <= z <= 1
# Adding the selection constraints
for i in range(N):
indices = [i + k * N for k in range(num_partitions)]
model += x[indices].sum() == 1
# Adding the cutting constraints
for i in z_ind:
for j in z_ind[i]:
for k in range(num_partitions):
shift = k * N
model += z[z_ind[i][j]] + x[i + shift] + x[j + shift] <= 2
model += z[z_ind[i][j]] + x[i + shift] - x[j + shift] >= 0
model += z[z_ind[i][j]] - x[i + shift] + x[j + shift] >= 0
# Adding the objective function
model.objective = CyLPArray(w) * z
lp = CyClpSimplex(model)
lp.logLevel = 0
lp.optimizationDirection = 'max'
mip = lp.getCbcModel()
mip.logLevel = 0
# Setting number of threads
mip.numberThreads = num_threads
mip.solve()
sol = [int(i) for i in mip.primalVariableSolution['x']]
sol_formatted = []
for i in range(N):
indices = [i + k * N for k in range(num_partitions)]
for j in range(num_partitions):
if (sol[indices[j]] == 1):
sol_formatted.append(j)
break
assert (len(sol_formatted) == N)
return mip.objectiveValue, sol_formatted
def solve_ilp(self):
""" Solves problem exactly using MIP/ILP approach
Used solver: CoinOR CBC
Incidence-matrix Q holds complete information needed for opt-process
"""
if self.verbose:
print('Solve: build model')
if self.condorcet_red:
condorcet_red_mat = extended_condorcet_simple(self.votes_arr)
n = self.Q.shape[0]
x_n = n * n
model = CyClpSimplex() # MODEL
x = model.addVariable('x', x_n, isInt=True) # VARS
model.objective = self.Q.ravel() # OBJ
# x_ab = boolean (already int; need to constrain to [0,1])
model += sp.eye(x_n) * x >= np.zeros(x_n)
model += sp.eye(x_n) * x <= np.ones(x_n)
idx = lambda i, j: np.ravel_multi_index((i, j), (n, n))
# constraints for every pair
start_time = time()
n_pairwise_constr = n * (n - 1) // 2
if self.verbose:
print(' # pairwise constr: ', n_pairwise_constr)
# Somewhat bloated just to get some vectorization / speed !
combs_ = combs(range(n), 2)
inds_a = np.ravel_multi_index(combs_.T, (n, n))
inds_b = np.ravel_multi_index(combs_.T[::-1], (n, n))
row_inds = np.tile(np.arange(n_pairwise_constr), 2)
col_inds = np.hstack((inds_a, inds_b))
pairwise_constraints = sp.coo_matrix(
(np.ones(n_pairwise_constr * 2), (row_inds, col_inds)),
shape=(n_pairwise_constr, n * n))
end_time = time()
if self.verbose:
print(" Took {:.{prec}f} secs".format(end_time - start_time,
prec=3))
# and for every cycle of length 3
start_time = time()
n_triangle_constrs = n * (n - 1) * (n - 2)
if self.verbose:
print(' # triangle constr: ', n_triangle_constrs)
# Somewhat bloated just to get some vectorization / speed !
perms_ = perms(range(n), 3)
inds_a = np.ravel_multi_index(perms_.T[(0, 1), :], (n, n))
inds_b = np.ravel_multi_index(perms_.T[(1, 2), :], (n, n))
inds_c = np.ravel_multi_index(perms_.T[(2, 0), :], (n, n))
row_inds = np.tile(np.arange(n_triangle_constrs), 3)
col_inds = np.hstack((inds_a, inds_b, inds_c))
triangle_constraints = sp.coo_matrix(
(np.ones(n_triangle_constrs * 3), (row_inds, col_inds)),
shape=(n_triangle_constrs, n * n))
end_time = time()
if self.verbose:
print(" Took {:.{prec}f} secs".format(end_time - start_time,
prec=3))
model += pairwise_constraints * x == np.ones(n_pairwise_constr)
model += triangle_constraints * x >= np.ones(n_triangle_constrs)
if self.condorcet_red and condorcet_red_mat != None:
I, J, V = sp.find(condorcet_red_mat)
indices_pos = np.ravel_multi_index([J, I], (n, n))
indices_neg = np.ravel_multi_index([I, J], (n, n))
nnz = len(indices_pos)
if self.verbose:
print(
' Extended Condorcet reductions: {} * 2 relations fixed'.
format(nnz))
lhs = sp.coo_matrix(
(np.ones(nnz * 2),
(np.arange(nnz * 2), np.hstack((indices_pos, indices_neg)))),
shape=(nnz * 2, n * n))
rhs = np.hstack(
(np.ones(len(indices_pos)), np.zeros(len(indices_neg))))
model += lhs * x == rhs
cbcModel = model.getCbcModel() # Clp -> Cbc model / LP -> MIP
cbcModel.logLevel = self.verbose
if self.verbose:
print('Solve: run MIP\n')
start_time = time()
status = cbcModel.solve() #-> "Call CbcMain. Solve the problem
# "using the same parameters used
# "by CbcSolver."
# This deviates from cylp's docs which are sparse!
# -> preprocessing will be used and is very important!
end_time = time()
if self.verbose:
print(" CoinOR CBC used {:.{prec}f} secs".format(end_time -
start_time,
prec=3))
x_sol = cbcModel.primalVariableSolution['x']
self.obj_sol = cbcModel.objectiveValue
x = np.array(x_sol).reshape((n, n)).round().astype(int)
self.aggr_rank = np.argsort(x.sum(axis=0))[::-1]
def tournament(args):
# Configuration
engine = create_engine('sqlite:///' + args.bdd, echo=False)
DBSession.configure(bind=engine)
# On récupere les donnes de la BDD
students = DBSession.query(Etudiant).filter(Etudiant.enseignant.is_(None))
nb_students = DBSession.query(Etudiant).filter(
Etudiant.enseignant.is_(None)).count()
nb_parcours = DBSession.query(Parcours).count()
# Capacité des groupes
capa_min_pec = 14
capa_max_pec = 16
capa_min_pel = 14
capa_max_pel = 16
# On construit le modèle
barre_min = args.b
model = CyClpSimplex()
x = []
s_list = {}
for s in students.all():
v = model.addVariable('etu' + str(s.id), nb_parcours, isInt=True)
x.append(v)
z = 0.0
i = 0
for s in students.all():
reorder = False
s_list[str(i)] = s.id
voeux = DBSession.query(Voeu).filter(Voeu.idEtudiant == s.id).order_by(
Voeu.idParcours).all()
s_voeux = []
rang_q = voeux[nb_parcours - 1].rang
if rang_q != nb_parcours and rang_q != -1:
if s.nom not in special or 'quebec' not in special[s.nom]:
reorder = True
else:
reorder = not special[s.nom]['quebec']
for v in voeux:
if v.rang == -1:
score = 0
else:
if reorder and v.rang > rang_q:
rang = v.rang - 1
elif reorder and v.rang == rang_q:
rang = 9
else:
rang = v.rang
malus = 0.0
if s.malus > 0:
malus += s.malus
if s.absences is not None:
malus += float(s.absences) / 15
score = 2.0**(rang - malus)
s_voeux.append(score)
a = CyLPArray(s_voeux)
b = CyLPArray([1.0 for j in range(nb_parcours)])
z += a * x[i]
model += b * x[i] >= 1
model += b * x[i] <= 1
model += x[i] >= 0
model += x[i] <= 1
i += 1
model.objective = z
# Taille des groupes MpInge (PEL)
for j in [
0, 3, 6
]: # Attention, dans la BDD, le parcours vont de 1 à 8 -> décalage de 1
c = 0
for i in range(nb_students):
c += x[i][j]
model += c <= capa_max_pel
model += c >= capa_min_pel
# Taille des groupes PEC
for j in [1, 2, 4, 5, 7]:
c = 0
for i in range(nb_students):
c += x[i][j]
model += c <= capa_max_pec
model += c >= capa_min_pec
# Etudiants trop bas en maths
i = 0
for s in students.all():
if s.moyenneMaths is not None and s.moyenneMaths < barre_min:
model += x[i][0] + x[i][3] + x[i][6] == 0
i += 1
# Québec
i = 0
for s in students.all():
if s.nom not in special or 'quebec' not in special[s.nom]:
model += x[i][nb_parcours - 1] == 0
i += 1
# Cas particuliers (cf special.py)
i = 0
for s in students.all():
if s.nom in special and 'force' in special[s.nom]:
for cas in special[s.nom]['force']:
if not special[s.nom]['force'][cas]:
model += x[i][int(cas) - 1] == 0
i += 1
cbc_model = model.getCbcModel()
cbc_model.logLevel = 0
cbc_model.branchAndBound()
if cbc_model.isRelaxationOptimal() and args.v:
for s in students.all():
r = cbc_model.primalVariableSolution['etu' + str(s.id)]
print s.nom + '|',
j = 1
reorder = False
for res in r:
if res == 1:
if j == 1 or j == 4 or j == 7:
print "!",
par = DBSession.query(Parcours).filter(
Parcours.id == j).one()
print par.nom,
voeux = DBSession.query(Voeu).filter(
Voeu.idEtudiant == s.id).order_by(
Voeu.idParcours).all()
rang_q = voeux[nb_parcours - 1].rang
if rang_q != nb_parcours and rang_q != -1:
if s.nom not in special or 'quebec' not in special[
s.nom]:
reorder = True
else:
reorder = not special[s.nom]['quebec']
son_voeu = DBSession.query(Voeu).filter(
Voeu.idEtudiant == s.id).filter(
Voeu.idParcours == j).one()
if reorder and son_voeu.rang == rang_q:
rang = 9
elif reorder and son_voeu.rang > rang_q:
rang = son_voeu.rang - 1
else:
rang = son_voeu.rang
print '|' + str(rang) + '',
print '|' + str(s.moyenneMaths),
print '|' + str(s.absences),
if reorder:
print '|*'
else:
print '|'
j += 1
nb_stu_grp = [0 for n in range(nb_parcours)]
rangs_stu = [0 for n in range(nb_parcours)]
indecis = 0
pec2pel = 0
pel2pec = 0
if cbc_model.isRelaxationOptimal() and (args.s or args.csv):
for s in students.all():
r = cbc_model.primalVariableSolution['etu' + str(s.id)]
j = 1
reorder = False
for res in r:
if res == 1:
nb_stu_grp[j - 1] += 1
voeux = DBSession.query(Voeu).filter(
Voeu.idEtudiant == s.id).order_by(
Voeu.idParcours).all()
rang_q = voeux[nb_parcours - 1].rang
if rang_q != nb_parcours and rang_q != -1:
if s.nom not in special or 'quebec' not in special[
s.nom]:
reorder = True
else:
reorder = not special[s.nom]['quebec']
son_voeu = DBSession.query(Voeu).filter(
Voeu.idEtudiant == s.id).filter(
Voeu.idParcours == j).one()
if reorder and son_voeu.rang == rang_q:
rang = 9
elif reorder and son_voeu.rang > rang_q:
rang = son_voeu.rang - 1
else:
rang = son_voeu.rang
if rang != -1:
rangs_stu[rang - 1] += 1
son_voeu_un = DBSession.query(Voeu).filter(
Voeu.idEtudiant == s.id).filter(
Voeu.rang == 1).one()
if son_voeu_un.idParcours in [
1, 4, 7
] and son_voeu.idParcours not in [1, 4, 7]:
pel2pec += 1
elif son_voeu_un.idParcours not in [
1, 4, 7
] and son_voeu.idParcours in [1, 4, 7]:
pec2pel += 1
else:
indecis += 1
j += 1
if args.s:
print "Etudiants par groupe : ", nb_stu_grp
print "Etudiants par rang de voeu : ", rangs_stu
print "Passages PEC->PEL", pec2pel
print "Passage PEL->PEC", pel2pec
print "Indécis : ", indecis
if args.csv:
print barre_min, ",",
for item in nb_stu_grp:
print item, ",",
for item in rangs_stu:
print item, ",",
print pec2pel, ",", pel2pec
if cbc_model.isRelaxationInfeasible():
print "Pas de solution possible"
