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):
train_mat = [\
[0.12, 0.25],\
[3.24, 4.33],\
[0.14, 0.45],\
[7.30, 4.23],\
]
train_label = [[0,1],[1,0],[0,1],[1,0]] # out bit is 1
logistic_reg =\
LogisticRegression(train_mat, train_label)
logistic_reg.fit(lr = 0.001, epoch = 2000, batch_size = 4)
r1 = autotest.eval_predict_one(logistic_reg,[0.10,0.33],[0, 1],self.logging, one_hot=True)
r2 = autotest.eval_predict_one(logistic_reg,[4.40,4.37],[1, 0],self.logging, one_hot=True)
def test_process(self):
dg_mat_train, dg_label_train = dataset.load_mnist("sample_data",
"training",
one_hot=True)
dg_mat_test, dg_label_test = dataset.load_mnist("sample_data",
"testing",
one_hot=True)
logistic_reg = LogisticRegression(dg_mat_train, dg_label_train)
logistic_reg.fit(lr=0.0001, epoch=1000, batch_size=100)
error_rate = autotest.eval_predict(logistic_reg,
dg_mat_test,
dg_label_test,
self.logging,
one_hot=True)
self.tlog("digit 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)
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):
train_mat = [\
[0.12, 0.25],\
[3.24, 4.33],\
[0.14, 0.45],\
[7.30, 4.23],\
]
train_label = [[0, 1], [1, 0], [0, 1], [1, 0]] # out bit is 1
logistic_reg =\
LogisticRegression(train_mat, train_label)
logistic_reg.fit(lr=0.001, epoch=2000, batch_size=4)
r1 = autotest.eval_predict_one(logistic_reg, [0.10, 0.33], [0, 1],
self.logging,
one_hot=True)
r2 = autotest.eval_predict_one(logistic_reg, [4.40, 4.37], [1, 0],
self.logging,
one_hot=True)
