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)