def __init__(self,
trainCollection,
tpp="lemm",
feature="color64+dsift",
k=1000,
rootpath=ROOT_PATH):
self.trainCollection = trainCollection
self.k = k
self.name = "%s(%s,%s,%s,%d)" % (self.__class__.__name__,
self.trainCollection, tpp, feature, k)
vobfile = os.path.join(rootpath, trainCollection, "TextData",
"wn.%s.txt" % trainCollection)
self.vob = set(map(str.strip, open(vobfile).readlines()))
printStatus(
INFO, 'the vocabulary of %s contains %d tags' %
(trainCollection, len(self.vob)))
self.gamma = (1.0 / MEDIAN_DISTANCE[feature])**2
self.feat_dir = os.path.join(rootpath, trainCollection, 'FeatureIndex',
feature)
self.dim = FEATURE_TO_DIM[feature]
self.fcs = FlickrContextSim(trainCollection, rootpath)
printStatus(INFO, self.name + ' okay')
def __init__(self, collection, useWnVob=1, rootpath=ROOT_PATH):
SemanticTagrelLearner.__init__(self, collection, useWnVob, rootpath)
self.engine = FlickrContextSim(collection, rootpath)
