def test_you_can_train_the_snd_on_the_synthetic_dataset():
X, y = synthetic.generate_dataset(seed=1686, n_classes=32)
X_train, X_test, y_train, y_test = model_selection.train_test_split(
X, y, train_size=0.8)
model = gdec.SuperNeuronDecoder()
model.fit(X_train, y_train)
score = model.score(X_test, y_test)
assert score > 1 / 32 # Better than random guessing?
def test_you_can_train_logistic_regression_on_the_synthetic_dataset():
X, y = synthetic.generate_dataset(seed=1634, n_classes=32)
X_train, X_test, y_train, y_test = model_selection.train_test_split(
X, y, train_size=0.8)
model = linear_model.LogisticRegression(solver="newton-cg")
model.fit(X_train, y_train)
score = model.score(X_test, y_test)
assert score > 1 / 32 # Better than random guessing?
def dataset() -> Tuple[np.ndarray, np.ndarray]:
return synthetic.generate_dataset(
seed=1634, examples_per_class=8, n_classes=8, n_features=8
)
def dataset() -> Tuple[np.ndarray, np.ndarray]:
return synthetic.generate_dataset(seed=1634, n_classes=32)
