def testContValues5000Iterations(self): dataPath = os.path.join(THIS_DIR, "test/DataSets/Real/ContinuousAndNonBinaryDiscreteAttributes.csv") converter = StringEnumerator(dataPath,"Class") headers, classLabel, dataFeatures, dataPhenotypes = converter.get_params() clf = XCS(learning_iterations=5000) clf.fit(dataFeatures,dataPhenotypes) answer = 0.64 #print("Continuous Attributes 5000 Iter: "+str(clf.get_final_training_accuracy())) self.assertTrue(self.approxEqualOrBetter(0.2,clf.get_final_training_accuracy(),answer,True))
def test20BitMP5000Iterations(self): dataPath = os.path.join(THIS_DIR, "test/DataSets/Real/Multiplexer20Modified.csv") converter = StringEnumerator(dataPath,"Class") headers, classLabel, dataFeatures, dataPhenotypes = converter.get_params() clf = XCS(learning_iterations=5000,N=2000,nu=10) clf.fit(dataFeatures,dataPhenotypes) answer = 0.6634 #print("20 Bit 5000 Iter: "+str(clf.get_final_training_accuracy())) self.assertTrue(self.approxEqualOrBetter(0.2,clf.get_final_training_accuracy(),answer,True))
def testPredictInvVar(self): dataPath = os.path.join(THIS_DIR, "test/DataSets/Real/Multiplexer6Modified.csv") converter = StringEnumerator(dataPath, "Class") headers, classLabel, dataFeatures, dataPhenotypes = converter.get_params( ) clf = XCS(learning_iterations=1000, N=500, nu=10, use_inverse_varinance=True, p_explore=0.5) clf.fit(dataFeatures, dataPhenotypes) print("kkkkkkkkkkkkkkkkkkkkkkkkkkk") print(clf.predict(clf.env.formatData.savedRawTrainingData[0]))
def testInverseVariance(self): dataPath = os.path.join(THIS_DIR, "test/DataSets/Real/Multiplexer11.csv") converter = StringEnumerator(dataPath, "class") headers, classLabel, dataFeatures, dataPhenotypes = converter.get_params( ) clf = XCS(learning_iterations=5000, N=1000, mixing_method="inv-var-only-mixing") clf.fit(dataFeatures, dataPhenotypes) answer = 0.894 score = clf.get_final_training_accuracy() print("#####################################\n6 Bit 1000 Iter: " + str(score))
def testNew(self): #Use StringEnumerator to gather data converter = StringEnumerator("test/DataSets/Real/Multiplexer11.csv", "class") headers, actionLabel, dataFeatures, dataActions = converter.get_params( ) #Shuffle data formatted = np.insert(dataFeatures, dataFeatures.shape[1], dataActions, 1) np.random.shuffle(formatted) dataFeatures = np.delete(formatted, -1, axis=1) dataActions = formatted[:, -1] #Initialize and train model clf_inv_var = XCS(learning_iterations=1000, N=200, use_inverse_varinance=True) clf_inv_var.fit(dataFeatures, dataActions) breakpoint()