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))
