def test_classify_boosted(self):
def build_stump(fPolarity):
dt = dtree.DTree(ixAttr=0,fDefaultLabel=True)
dt.add(dtree.DTree(fLabel=fPolarity),0)
dt.add(dtree.DTree(fLabel=not fPolarity),1)
return dt
cCfer = 10
listCfer = [build_stump(bool(i%2)) for i in xrange(cCfer)]
listWeight = [math.exp(-i) for i in xrange(cCfer)]
inst = dtree.Instance([int(randbool())], randbool())
fLabel = dtree.classify_boosted(dtree.BoostResult(listWeight,listCfer),
inst)
self.assertEqual(bool(inst.listAttrs[0]), fLabel)
def dt_predict(data, m):
"""Classify an input."""
inst = dtree.Instance(data, False)
if dtree.classify_boosted(m, inst):
return 1
return 0
def dt_predict(data, m):
"""Classify an input."""
inst = dtree.Instance(data, False)
if dtree.classify_boosted(m, inst):
return 1
return 0
