def argmax(self, w, feat_idx, training):
feature_vector = self.get_features().get_feature_vector(feat_idx)
label = None
if training == True:
label = int(
RealNumber.obtain_from_generic(
self.get_labels().get_label(feat_idx)).value)
ypred = 0
max_score = -1e10
for c in xrange(self.n_classes):
score = 0.0
for i in xrange(self.n_feats):
score += w[i + self.n_feats * c] * feature_vector[i]
if training == True:
score += (c != label)
if score > max_score:
max_score = score
ypred = c
res = ResultSet()
res.score = max_score
res.psi_pred = RealVector(self.dim)
res.psi_pred.zero()
for i in xrange(self.n_feats):
res.psi_pred[i + self.n_feats * ypred] = feature_vector[i]
res.argmax = RealNumber(ypred)
if training == True:
res.delta = (label != ypred)
res.psi_truth = RealVector(self.dim)
res.psi_truth.zero()
for i in xrange(self.n_feats):
res.psi_truth[i + self.n_feats * label] = feature_vector[i]
for i in xrange(self.n_feats):
res.score -= w[i + self.n_feats * label] * feature_vector[i]
return res
