def test_load_iris(self):
iris_mat_train, iris_label_train = dataset.load_iris(
"sample_data", "training")
iris_mat_test, iris_label_test = dataset.load_iris(
"sample_data", "testing")
self.tlog("iris train data size : " + str(len(iris_mat_train)))
self.tlog("iris test data size : " + str(len(iris_mat_test)))
def test_process(self):
iris_mat_train, iris_label_train = dataset.load_iris("sample_data", "training", one_hot=True)
iris_mat_test, iris_label_test = dataset.load_iris("sample_data", "testing", one_hot=True)
linear_reg = LinearRegression(iris_mat_train, iris_label_train)
linear_reg.fit(lr = 0.0001, epoch = 1000, batch_size = 20)
error_rate = autotest.eval_predict(linear_reg, iris_mat_test, iris_label_test, self.logging, one_hot=True)
self.tlog("iris predict (with linear regression) error rate :" + str(error_rate))
def test_process(self):
iris_mat_train, iris_label_train = dataset.load_iris("sample_data", "training", one_hot=True)
iris_mat_test, iris_label_test = dataset.load_iris("sample_data", "testing", one_hot=True)
fnn = FNN(iris_mat_train, iris_label_train, [2])
fnn.fit(lr = 0.001, epoch = 4000, err_th = 0.00001, batch_size = 30)
error_rate = autotest.eval_predict(fnn, iris_mat_test, iris_label_test, self.logging, one_hot=True)
self.tlog("iris predict (with fnn) error rate :" + str(error_rate))
def test_process(self):
iris_mat_train, iris_label_train = dataset.load_iris(
"sample_data", "training")
iris_mat_test, iris_label_test = dataset.load_iris(
"sample_data", "testing")
knn = KNN(iris_mat_train, iris_label_train, 3, 'manhattan')
error_rate = autotest.eval_predict(knn, iris_mat_test, iris_label_test,
self.logging)
self.tlog("iris predict (with basic knn) error rate :" +
str(error_rate))
def test_process(self):
iris_mat_train, iris_label_train = dataset.load_iris("sample_data",
"training",
one_hot=True)
iris_mat_test, iris_label_test = dataset.load_iris("sample_data",
"testing",
one_hot=True)
fnn = FNN(iris_mat_train, iris_label_train, [2])
fnn.fit(lr=0.001, epoch=4000, err_th=0.00001, batch_size=30)
error_rate = autotest.eval_predict(fnn,
iris_mat_test,
iris_label_test,
self.logging,
one_hot=True)
self.tlog("iris predict (with fnn) error rate :" + str(error_rate))
def test_process(self):
iris_mat_train, iris_label_train = dataset.load_iris("sample_data",
"training",
one_hot=True)
iris_mat_test, iris_label_test = dataset.load_iris("sample_data",
"testing",
one_hot=True)
logistic_reg = LogisticRegression(iris_mat_train, iris_label_train)
logistic_reg.fit(lr=0.001, epoch=2000, batch_size=30)
error_rate = autotest.eval_predict(logistic_reg,
iris_mat_test,
iris_label_test,
self.logging,
one_hot=True)
self.tlog("iris predict (with logistic regression) error rate :" +
str(error_rate))
def test_process(self):
iris_mat_train, iris_label_train = dataset.load_iris("sample_data",
"training",
one_hot=True)
iris_mat_test, iris_label_test = dataset.load_iris("sample_data",
"testing",
one_hot=True)
svc = SVC(iris_mat_train, iris_label_train)
svc.fit(C=1.5,
toler=0.0001,
epoch=1000,
kernel="Polynomial",
kernel_params={"degree": 3})
error_rate = autotest.eval_predict(svc,
iris_mat_test,
iris_label_test,
self.logging,
one_hot=True)
self.tlog("iris predict (with svc) error rate :" + str(error_rate))
def test_load_iris_one_hot(self):
iris_mat_train, iris_label_train = dataset.load_iris("sample_data", "training", one_hot = True)
iris_mat_test, iris_label_test = dataset.load_iris("sample_data", "testing", one_hot = True)
self.tlog("iris train data size : " + str(len(iris_mat_train)))
self.tlog("iris test data size : " + str(len(iris_mat_test)))
