def classify_example(x, weights):
"""Given a pattern x, return the predicted label."""
number_events = len(x.events)
length_feat= x.length_feat()
L = []
#weights :
w = weights
longueurs = []
xTilde = x.featuresMatrix()
#SET UP LIKELIHOOD FUNCTION
for k in range(number_events):
Mf = np.transpose(xTilde[k])
# print Mf.shape #type :np.ndarray, taille: length_feat, length_hyp(k)
if Mf.shape==(0,):
longueurs.append(0)
continue
longueurs.append(Mf.shape[1])
wC = w[length_feat*k:length_feat*(k+1)]
# print len(wC) #type : list longueur: length_feat
L = np.hstack((L, np.dot(wC, Mf)))
#print L.shape#normalement c'est length_hyp
costFunc = L
constraints = x.constraintsMatrix()
#print constraints.shape#(t1+t2, length_hyp)
#FIND ARGMAX LIKELIHOOD FUNCTION
output2, msg = cplexSolving.solve(costFunc, constraints, x.singletsSize)#c'est ybar qu'il faut retourner
print "--",
#print msg
output = [int(j) for j in output2]
ybar=[];last=0
for k in range(number_events):
ybar.append(list(output[last:last+longueurs[k]]))
last +=longueurs[k]
# print "ybar : "
# for k in range(number_events):
# print ybar[k]
# print "loss :", loss(y, ybar, sparm)
return ybar
def find_most_violated_constraint_margin(x, y, sm, sparm):
"""Return ybar associated with x's most violated constraint.
The find most violated constraint function for margin rescaling.
The default behavior is that this returns the value from the
general find_most_violated_constraint function."""
#dans l'exemple donne il y a une iteration sur toutes les classifications possibles pour trouver la contrainte la plus violee
#nous on ne peut pas faire ca donc on doit utiliser cplex
number_events = len(y)
length_feat= sm.size_psi
sigma = 0
Y = [] ; L = []
#weights :
w = sm.w
longueurs = []
for k in range(number_events):
sigma+=np.sum(y[k])
longueurs.append(len(y[k]))
Y=np.hstack((Y, y[k]))
Y=Y/sigma#type np.array, taille length_hyp,
xTilde = x.featuresMatrix()
#SET UP COST FUNCTION
for k in range(number_events):
Mf = np.transpose(xTilde[k])
print Mf.shape #type :np.ndarray, taille: length_feat, length_hyp(k)
wC = w[length_feat*k:length_feat*(k+1)]
print len(wC) #type : list longueur: length_feat
L = np.hstack((L, np.dot(wC, Mf)))
print L.shape#normalement c'est length_hyp
costFunc = L-Y
constraints = x.constraintsMatrix()
print constraints.shape#(t1+t2, length_hyp)
output = cplexSolving.solve(costFunc, constraints, x.singletsSize)#c'est ybar qu'il faut retourner
ybar=[];last=0
for k in range(number_events):
ybar.append(list(output[last:last+longueurs[k]]))
last +=longueurs[k]
return ybar
