# Path: pystruct/inference/common.py
# def _validate_params(unary_potentials, pairwise_params, edges):
#     n_states = unary_potentials.shape[-1]
#     if pairwise_params.shape == (n_states, n_states):
#         # only one matrix given
#         pairwise_potentials = np.repeat(pairwise_params[np.newaxis, :, :],
#                                         edges.shape[0], axis=0)
#     else:
#         if pairwise_params.shape != (edges.shape[0], n_states, n_states):
#             raise ValueError("Expected pairwise_params either to "
#                              "be of shape n_states x n_states "
#                              "or n_edges x n_states x n_states, but"
#                              " got shape %s" % repr(pairwise_params.shape))
#         pairwise_potentials = pairwise_params
#     return n_states, pairwise_potentials


import numpy as np
    import opengm
    import ad3
    import ad3
from .linear_programming import lp_general_graph
from .maxprod import inference_max_product
from .common import _validate_params
    from pyqpbo import alpha_expansion_general_graph
    alg : string
        Possible choices currently are:
            * 'bp' for Loopy Belief Propagation.
            * 'dd' for Dual Decomposition via Subgradients.
            * 'trws' for Vladimirs TRWs implementation.
            * 'trw' for OGM  TRW.
            * 'gibbs' for Gibbs sampling.
            * 'lf' for Lazy Flipper
            * 'fm' for Fusion Moves (alpha-expansion fusion)
            * 'dyn' for Dynamic Programming (message passing in trees)
            * 'gc' for Graph Cut
            * 'alphaexp' for Alpha Expansion using Graph Cuts
            * 'mqpbo' for multi-label qpbo

    init : nd-array
        Initial solution for starting inference (ignored by some algorithms).

    reserveNumFactorsPerVariable :
        reserve a certain number of factors for each variable can speed up
        the building of a graphical model.
        ( For a 2d grid with second order factors one should set this to 5
         4 2-factors and 1 unary factor for most pixels )

    Returns
    -------
    labels : nd-array
        Approximate (usually) MAP variable assignment.
    """

    n_states, pairwise_potentials = \

# Path: pystruct/inference/common.py
# def _validate_params(unary_potentials, pairwise_params, edges):
#     n_states = unary_potentials.shape[-1]
#     if pairwise_params.shape == (n_states, n_states):
#         # only one matrix given
#         pairwise_potentials = np.repeat(pairwise_params[np.newaxis, :, :],
#                                         edges.shape[0], axis=0)
#     else:
#         if pairwise_params.shape != (edges.shape[0], n_states, n_states):
#             raise ValueError("Expected pairwise_params either to "
#                              "be of shape n_states x n_states "
#                              "or n_edges x n_states x n_states, but"
#                              " got shape %s" % repr(pairwise_params.shape))
#         pairwise_potentials = pairwise_params
#     return n_states, pairwise_potentials


# Path: pystruct/inference/inference_methods.py
import numpy as np
    import opengm
    import ad3
    import ad3
from .linear_programming import lp_general_graph
from .maxprod import inference_max_product
from .common import _validate_params
    from pyqpbo import alpha_expansion_general_graph

    alg : string
        Possible choices currently are:
            * 'bp' for Loopy Belief Propagation.
            * 'dd' for Dual Decomposition via Subgradients.
            * 'trws' for Vladimirs TRWs implementation.
            * 'trw' for OGM  TRW.
            * 'gibbs' for Gibbs sampling.
            * 'lf' for Lazy Flipper
            * 'fm' for Fusion Moves (alpha-expansion fusion)
            * 'dyn' for Dynamic Programming (message passing in trees)
            * 'gc' for Graph Cut
            * 'alphaexp' for Alpha Expansion using Graph Cuts
            * 'mqpbo' for multi-label qpbo

    init : nd-array
        Initial solution for starting inference (ignored by some algorithms).

    reserveNumFactorsPerVariable :
        reserve a certain number of factors for each variable can speed up
        the building of a graphical model.
        ( For a 2d grid with second order factors one should set this to 5
         4 2-factors and 1 unary factor for most pixels )

    Returns
    -------
    labels : nd-array
        Approximate (usually) MAP variable assignment.
    """

    n_states, pairwise_potentials = \

        _validate_params(unary_potentials, pairwise_potentials, edges)