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()
