# Path: label.py
# class Label(object):
#     def __init__(self, full, weak, weights, full_labeled, relaxed=False):
#         self.full = full
#         self.weak = weak
#         self.weights = weights
#         self.full_labeled = full_labeled
#         self.relaxed = relaxed
#         if not self.relaxed:
#             if self.full is None and self.weak is None:
#                 raise ValueError("You should specify full or weak labels")
#             if self.weak is None:
#                 self.weak = np.unique(self.full).astype(np.int32)
#             if self.full is None:
#                 self.full = np.random.choice(self.weak, self.weights.size).astype(np.int32)
# 
#     def __eq__(self, other):
#         if self.relaxed or other.relaxed:
#             return np.all(self.full[0] == other.full[0]) \
#                 and np.all(self.full[1] == other.full[1])
#         return np.all(self.full == other.full)


import numpy as np
    import ad3
from pystruct.models.base import StructuredModel
from sklearn.utils.extmath import safe_sparse_dot
from label import Label
    from pygco import cut_from_graph_gen_potts
                from trw import trw
            from trw import trw

        w : ndarray, shape=(size_joint_feature,)
            Parameters for the CRF energy function.

        relaxed : bool, default=False
            We do not support it yet.

        return_energy : bool, default=False
            Whether to return the energy of the solution (x, y) that was found.

        Returns
        -------
        y_pred : ndarray
            By default an integer ndarray of shape=(width, height)
            of variable assignments for x is returned.

        """

        self._check_size_w(w)
        self.inference_calls += 1
        unary_potentials = self._get_unary_potentials(x, w)
        pairwise_potentials = self._get_pairwise_potentials(x, w)
        edges = self._get_edges(x)
        if invert:
            unary_potentials = -unary_potentials
            pairwise_potentials = -pairwise_potentials

        if self.inference_method == 'gco':
            h = inference_gco(unary_potentials, pairwise_potentials,
                              edges, n_iter=self.n_iter)

# Path: label.py
# class Label(object):
#     def __init__(self, full, weak, weights, full_labeled, relaxed=False):
#         self.full = full
#         self.weak = weak
#         self.weights = weights
#         self.full_labeled = full_labeled
#         self.relaxed = relaxed
#         if not self.relaxed:
#             if self.full is None and self.weak is None:
#                 raise ValueError("You should specify full or weak labels")
#             if self.weak is None:
#                 self.weak = np.unique(self.full).astype(np.int32)
#             if self.full is None:
#                 self.full = np.random.choice(self.weak, self.weights.size).astype(np.int32)
# 
#     def __eq__(self, other):
#         if self.relaxed or other.relaxed:
#             return np.all(self.full[0] == other.full[0]) \
#                 and np.all(self.full[1] == other.full[1])
#         return np.all(self.full == other.full)


# Path: heterogenous_crf.py
import numpy as np
    import ad3
from pystruct.models.base import StructuredModel
from sklearn.utils.extmath import safe_sparse_dot
from label import Label
    from pygco import cut_from_graph_gen_potts
                from trw import trw
            from trw import trw


        w : ndarray, shape=(size_joint_feature,)
            Parameters for the CRF energy function.

        relaxed : bool, default=False
            We do not support it yet.

        return_energy : bool, default=False
            Whether to return the energy of the solution (x, y) that was found.

        Returns
        -------
        y_pred : ndarray
            By default an integer ndarray of shape=(width, height)
            of variable assignments for x is returned.

        """

        self._check_size_w(w)
        self.inference_calls += 1
        unary_potentials = self._get_unary_potentials(x, w)
        pairwise_potentials = self._get_pairwise_potentials(x, w)
        edges = self._get_edges(x)
        if invert:
            unary_potentials = -unary_potentials
            pairwise_potentials = -pairwise_potentials

        if self.inference_method == 'gco':
            h = inference_gco(unary_potentials, pairwise_potentials,
                              edges, n_iter=self.n_iter)

            y_ret = Label(h, None, None, True)