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()