def getLabel(self, value):
feature = FeatureFactory()
feature.createFeature(value, "")
dict = {};
dict['attributes'] = {}
attributes = []
line = feature.datatable[0]
for i in range(len(line)):
dict['attributes'][str(i)] = line[i]
attributes.append(str(i))
res = self.model.predict(dict)
r = max(res.iterkeys(),key=lambda k:res[k])
return r
def getLabel(self, value):
feature = FeatureFactory()
feature.createFeature(value, "")
dict = {}
dict['attributes'] = {}
attributes = []
line = feature.datatable[0]
for i in range(len(line)):
dict['attributes'][str(i)] = line[i]
attributes.append(str(i))
res = self.model.predict(dict)
r = max(res.iterkeys(), key=lambda k: res[k])
return r
def getClass(setting, value):
setting = setting.decode("string-escape")
#print setting
classifier = pickle.loads(setting)
feature = FeatureFactory()
feature.createFeature(value, "")
dict = {}
dict['attributes'] = {}
attributes = []
line = feature.datatable[0]
for i in range(len(line)):
dict['attributes'][str(i)] = line[i]
attributes.append(str(i))
res = classifier.predict(dict)
r = max(res.iterkeys(), key=lambda k: res[k])
return r
def getClass(setting, value):
setting = setting.decode("string-escape")
#print setting
classifier = pickle.loads(setting)
feature = FeatureFactory()
feature.createFeature(value, "")
dict = {};
dict['attributes'] = {}
attributes = []
line = feature.datatable[0]
for i in range(len(line)):
dict['attributes'][str(i)] = line[i]
attributes.append(str(i))
res = classifier.predict(dict)
r = max(res.iterkeys(), key=lambda k: res[k])
return r
class IDCTClassifier(PartitionClassifierType):
def __init__(self):
self.path = "./"
self.featureFactory = FeatureFactory()
def addTrainingData(self, value, label):
self.featureFactory.createFeature(value, label)
def learnClassifer(self):
model = NaiveBayes()
dict = {}
dict['cases'] = 1
attributes = []
for j in range(len(self.featureFactory.datatable)):
dict = {}
dict['cases'] = 1
dict['attributes'] = {}
line = self.featureFactory.datatable[j]
for i in range(len(line)):
dict['attributes'][str(i)] = line[i]
attributes.append(str(i))
dict['label'] = self.featureFactory.classes[j]
model.add_instances(dict)
model.set_real(attributes)
model.train()
self.model = model
return pickle.dumps(model).encode('string_escape')
def getLabel(self, value):
feature = FeatureFactory()
feature.createFeature(value, "")
dict = {}
dict['attributes'] = {}
attributes = []
line = feature.datatable[0]
for i in range(len(line)):
dict['attributes'][str(i)] = line[i]
attributes.append(str(i))
res = self.model.predict(dict)
r = max(res.iterkeys(), key=lambda k: res[k])
return r
class IDCTClassifier(PartitionClassifierType):
def __init__(self):
self.path = "./"
print "building classifier"
self.featureFactory = FeatureFactory()
def addTrainingData(self, value, label):
self.featureFactory.createFeature(value, label)
def learnClassifer(self):
model = NaiveBayes()
dict = {};
dict['cases'] = 1
attributes = []
for j in range(len(self.featureFactory.datatable)):
dict = {};
dict['cases'] = 1
dict['attributes'] = {}
line = self.featureFactory.datatable[j]
for i in range(len(line)):
dict['attributes'][str(i)] = line[i]
attributes.append(str(i))
dict['label'] = self.featureFactory.classes[j]
model.add_instances(dict)
model.set_real(attributes)
model.train()
self.model = model
return pickle.dumps(model).encode('string_escape')
def getLabel(self, value):
feature = FeatureFactory()
feature.createFeature(value, "")
dict = {};
dict['attributes'] = {}
attributes = []
line = feature.datatable[0]
for i in range(len(line)):
dict['attributes'][str(i)] = line[i]
attributes.append(str(i))
res = self.model.predict(dict)
r = max(res.iterkeys(),key=lambda k:res[k])
return r
