from sklearn.datasets import fetch_mldata from sklearn.preprocessing import StandardScaler from sklearn.utils import check_random_state def create_leaf(data, ds_context, scope): return create_piecewise_leaf(data, ds_context, scope, isotonic=False, prior_weight=None) #return create_histogram_leaf(data, ds_context, scope, alpha=0.1) add_piecewise_inference_support() add_histogram_inference_support() add_parametric_inference_support() memory = Memory(cachedir="cache", verbose=0, compress=9) data = [] for x in range(10): for y in range(10): for z in range(10): data.append([x, y, z, int(((x + y + z) / 5))]) data = np.array(data).astype(np.float) types = [ MetaType.DISCRETE, MetaType.DISCRETE, MetaType.DISCRETE, MetaType.DISCRETE ] ds_context = Context(meta_types=types) ds_context.parametric_types = [Gaussian, Gaussian, Gaussian, Categorical]
def setUp(self): add_histogram_inference_support() add_piecewise_inference_support()
def setUp(self): add_histogram_inference_support()
def setUp(self): add_parametric_inference_support() add_histogram_inference_support()