def _getInstances(self, classAttr):
# create attributes
self.classAttr = classAttr
attName2Obj = {}
attVector = FastVector()
for attName in self.numericAttributes:
attr = Attribute(attName)
attVector.addElement(attr)
attName2Obj[attName] = attr
for (attName, domain) in self.attName2Domain.iteritems():
vDomain = FastVector(len(domain))
for v in domain:
#print v
vDomain.addElement(String(str(v)))
attr = Attribute(attName, vDomain)
attVector.addElement(attr)
attName2Obj[attName] = attr
self.attName2Obj = attName2Obj
# create Instances object
instances = Instances("instances", attVector, len(self.instances))
for i in self.instances:
inst = self._makeInstance(i)
instances.add(inst)
instances.setClass(attName2Obj[classAttr])
return instances
def _getInstances(self, classAttr):
# create attributes
self.classAttr = classAttr
attName2Obj = {}
attVector = FastVector()
for attName in self.numericAttributes:
attr = Attribute(attName)
attVector.addElement(attr)
attName2Obj[attName] = attr
for (attName, domain) in self.attName2Domain.iteritems():
vDomain = FastVector(len(domain))
for v in domain:
#print v
vDomain.addElement(String(str(v)))
attr = Attribute(attName, vDomain)
attVector.addElement(attr)
attName2Obj[attName] = attr
self.attName2Obj = attName2Obj
# create Instances object
instances = Instances("instances", attVector, len(self.instances))
for i in self.instances:
inst = self._makeInstance(i)
instances.add(inst)
instances.setClass(attName2Obj[classAttr])
return instances
def build_instances(state,dataset):
class_attributes = ["Sunny", "Fog", "Rain", "Snow", "Hail", "Thunder", "Tornado"]
header = ["state","lat", "lon", "day","temp","dewp","weather"]
#build attributes based on the header and types
attributes = []
for h in header[:-1]:
attributes.append(Attribute(h))
#add the classification attribute
classification_vector = FastVector(len(class_attributes))
for c in class_attributes:
classification_vector.addElement(c)
attributes.append(Attribute("toClassify", classification_vector))
fvWekaAttributes = FastVector(len(dataset[0]))
for a in attributes:
fvWekaAttributes.addElement(a)
training_set = Instances("C4.5Set", fvWekaAttributes, len(dataset))
training_set.setClassIndex(len(header)-1)
for d in dataset:
inst = Instance(len(d))
for i in range(len(d)-1):
try:
inst.setValue(fvWekaAttributes.elementAt(i), float(d[i]))
except:
pass
#print "failed on", i, d[i], d[i].__class__
inst.setValue(fvWekaAttributes.elementAt(len(d)-1), d[-1])
training_set.add(inst)
j48 = J48()
j48.buildClassifier(training_set)
return state,parse_tree(str(j48))
