def setUpClass(cls):
cls.n, cls.d = 1000, 10
np.random.seed(7)
x = np.random.uniform(0, 50, cls.n * cls.d).reshape(cls.n, cls.d)
eps = np.random.normal(loc=0, scale=2, size=cls.n * cls.d).reshape(cls.n, cls.d)
w = np.random.uniform(1, 5, cls.d).reshape(cls.d, 1)
y = (x + eps).dot(w)
x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=7, test_size=0.25)
x_standardizer = Standardizer(x_train)
cls.x_train_std = x_standardizer.standardize(x_train)
cls.x_test_std = x_standardizer.standardize(x_test)
y_standardizer = Standardizer(y_train)
cls.y_train_std = y_standardizer.standardize(y_train)
cls.y_test_std = y_standardizer.standardize(y_test)
cls.model = LassoRegression(cls.x_train_std, cls.y_train_std, lambd=0.5 / 2)
cls.model.cycliccoorddescent()
cls.model.randcoorddescent()
def setUpClass(cls):
cls.n, cls.d = 500, 10
np.random.seed(7)
x_class1 = np.random.normal(0, 5, cls.n * cls.d).reshape(cls.n, cls.d)
x_class2 = np.random.normal(0, 10, cls.n * cls.d).reshape(cls.n, cls.d)
w = np.random.uniform(1, 5, cls.d).reshape(cls.d, 1)
y_class1 = np.full((cls.n, 1), -1)
y_class2 = np.full((cls.n, 1), 1)
x = np.concatenate((x_class1, x_class2))
y = np.concatenate((y_class1, y_class2))
x_train, x_test, cls.y_train, cls.y_test = train_test_split(
x, y, random_state=7, test_size=0.25)
x_standardizer = Standardizer(x_train)
cls.x_train_std = x_standardizer.standardize(x_train)
cls.x_test_std = x_standardizer.standardize(x_test)
cls.model = LogisticRegression(cls.x_train_std, cls.y_train, lamd=1)
cls.model.train()
def test_label_standardization(self):
y_standardizer = Standardizer(self.labels)
labels_standardized = y_standardizer.standardize(self.labels)
self.assertTrue(
np.array_equal(labels_standardized,
self.labels_standardized_expected))
def test_feature_standardization(self):
x_standardizer = Standardizer(self.features)
features_standardized = x_standardizer.standardize(self.features)
self.assertTrue(
np.array_equal(features_standardized,
self.features_standardized_expected))