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)