from sklearn.model_selection import train_test_split
from spn.algorithms.LearningWrappers import learn_parametric
from spn.algorithms.Sampling import sample_instances
from spn.algorithms.Statistics import get_structure_stats, get_structure_stats_dict
# from spn.structure.Base import Context
from new_base import Context
from spn.structure.leaves.parametric.Parametric import CategoricalDictionary, Categorical, Gaussian
from ScikitCSPNClassifier import CSPNClassifier
from experiments.img_tools import get_blocks, stitch_imgs, show_img, save_img
from structure.Conditional.Inference import add_conditional_inference_support
from structure.Conditional.Sampling import add_conditional_sampling_support
from structure.Conditional.utils import concatenate_yx
add_conditional_sampling_support()
add_conditional_inference_support()
def to_ohe(x, n_values):
return np.eye(n_values)[x]
output_path = os.path.dirname(os.path.abspath(__file__)) + '/imgs_pixelcspn_faces/'
faces = fetch_olivetti_faces()
data = fetch_olivetti_faces()
images = np.round(data['images'] * 256).astype(dtype=np.uint8)
target = data['target']
np.random.seed(1)
X_train, X_test, y_train, y_test = train_test_split(images, target)
def setUp(self):
add_conditional_inference_support()
def setUp(self):
add_conditional_inference_support()
add_conditional_mpe_support()
add_conditional_sampling_support()