def test_classifier_error(self):
cInst = 100
listInst = build_instance_generator()(cInst)
ix = random.randint(0,cInst)
rslt = dtree.EvaluationResult(listInst[:ix], listInst[ix:], None)
self.assertAlmostEqual(float(cInst-ix)/float(cInst),
dtree.classifier_error(rslt))
def test_weight_corrrect_incorrect(self):
def make_list(cLen):
listI = []
dblSum = 0.0
for _ in xrange(cLen):
dbl = math.exp(-random.random() - 0.1) * 10.0
listI.append(dtree.Instance([],randbool(),dbl))
dblSum += dbl
return listI,dblSum
listInstCorrect,dblCorrect = make_list(random.randint(0,10))
listInstIncorrect,dblIncorrect = make_list(random.randint(0,10))
rslt = dtree.EvaluationResult(listInstCorrect, listInstIncorrect,None)
dblC,dblI = dtree.weight_correct_incorrect(rslt)
self.assertAlmostEqual(dblCorrect,dblC)
self.assertAlmostEqual(dblIncorrect,dblI)