def test_iris_embedding(): iris = datasets.load_iris() x = iris.data y = iris.target ivis_iris = Ivis(n_epochs_without_progress=5) ivis_iris.k = 15 ivis_iris.batch_size = 16 y_pred_iris = ivis_iris.fit_transform(x)
def test_1d_supervied_iris_embedding(): iris = datasets.load_iris() x = iris.data y = iris.target ivis_iris = Ivis(epochs=2, embedding_dims=1) ivis_iris.k = 15 ivis_iris.batch_size = 16 y_pred_iris = ivis_iris.fit_transform(x, y)
def test_iris_embedding(): iris = datasets.load_iris() x = iris.data y = iris.target mask = np.random.choice(range(len(y)), size=len(y) // 2, replace=False) y[mask] = -1 ivis_iris = Ivis(epochs=5) ivis_iris.k = 15 ivis_iris.batch_size = 16 y_pred_iris = ivis_iris.fit_transform(x, y)
def test_custom_ndarray_neighbour_matrix(): iris = datasets.load_iris() x = iris.data y = iris.target class_indicies = {label: np.argwhere(y == label).ravel() for label in np.unique(y)} neighbour_matrix = np.array([class_indicies[label] for label in y]) ivis_iris = Ivis(epochs=5, neighbour_matrix=neighbour_matrix) ivis_iris.k = 15 ivis_iris.batch_size = 16 y_pred_iris = ivis_iris.fit_transform(x)