# Path: pystruct/models/grid_crf.py
# class GridCRF(GraphCRF):
#     """Pairwise CRF on a 2d grid.
# 
#     Pairwise potentials are symmetric and the same for all edges.
#     This leads to n_classes parameters for unary potentials and
#     n_classes * (n_classes + 1) / 2 parameters for edge potentials.
# 
#     Unary evidence ``x`` is given as array of shape (width, height, n_features),
#     labels ``y`` are given as array of shape (width, height). Grid sizes do not
#     need to be constant over the dataset.
# 
#     Parameters
#     ----------
#     n_states : int, default=2
#         Number of states for all variables.
# 
#     inference_method : string, default="ad3"
#         Function to call do do inference and loss-augmented inference.
#         Possible values are:
# 
#             - 'max-product' for max-product belief propagation.
#                 Recommended for chains an trees. Loopy belief propagation in
#                 case of a general graph.
#             - 'lp' for Linear Programming relaxation using cvxopt.
#             - 'ad3' for AD3 dual decomposition.
#             - 'qpbo' for QPBO + alpha expansion.
#             - 'ogm' for OpenGM inference algorithms.
# 
#     neighborhood : int, default=4
#         Neighborhood defining connection for each variable in the grid.
#         Possible choices are 4 and 8.
#     """
#     def __init__(self, n_states=None, n_features=None, inference_method=None,
#                  neighborhood=4):
#         self.neighborhood = neighborhood
#         GraphCRF.__init__(self, n_states=n_states, n_features=n_features,
#                           inference_method=inference_method)
# 
#     def _get_edges(self, x):
#         return make_grid_edges(x, neighborhood=self.neighborhood)
# 
#     def _get_features(self, x):
#         return x.reshape(-1, self.n_features)
# 
#     def _reshape_y(self, y, shape_x, return_energy):
#         if return_energy:
#             y, energy = y
# 
#         if isinstance(y, tuple):
#             y = (y[0].reshape(shape_x[0], shape_x[1], y[0].shape[1]), y[1])
#         else:
#             y = y.reshape(shape_x[:-1])
# 
#         if return_energy:
#             return y, energy
#         return y
# 
#     def inference(self, x, w, relaxed=False, return_energy=False):
#         y = GraphCRF.inference(self, x, w, relaxed=relaxed,
#                                return_energy=return_energy)
#         return self._reshape_y(y, x.shape, return_energy)
# 
#     def loss_augmented_inference(self, x, y, w, relaxed=False,
#                                  return_energy=False):
#         y_hat = GraphCRF.loss_augmented_inference(self, x, y.ravel(), w,
#                                                   relaxed=relaxed,
#                                                   return_energy=return_energy)
#         return self._reshape_y(y_hat, x.shape, return_energy)
# 
#     def continuous_loss(self, y, y_hat):
#         # continuous version of the loss
#         # y_hat is the result of linear programming
#         return GraphCRF.continuous_loss(
#             self, y.ravel(), y_hat.reshape(-1, y_hat.shape[-1]))
# 
# Path: pystruct/inference/inference_methods.py
# def get_installed(method_filter=None):
#     if method_filter is None:
#         method_filter = ["max-product", 'ad3', 'ad3+', 'qpbo', 'ogm', 'lp']
# 
#     installed = []
#     unary = np.zeros((1, 1))
#     pw = np.zeros((1, 1))
#     edges = np.empty((0, 2), dtype=np.int)
#     for method in method_filter:
#         try:
#             if method != 'ad3+':
#                 inference_dispatch(unary, pw, edges, inference_method=method)
#             else:
#                 inference_dispatch(unary, np.zeros((0,1,1))
#                                    , np.zeros((0,2), dtype=np.int)
#                                    , inference_method=method)
#             installed.append(method)
#         except ImportError:
#             pass
#     return installed


import numpy as np
from numpy.testing import (assert_array_equal, assert_array_almost_equal,
                           assert_almost_equal)
from pystruct.datasets import (generate_blocks, generate_blocks_multinomial,
                               binary, multinomial)
from pystruct.models import GridCRF
from pystruct.utils import (find_constraint, exhaustive_inference,
                            exhaustive_loss_augmented_inference)
from pystruct.inference import get_installed



def test_continuous_y():
    for inference_method in get_installed(["lp", "ad3"]):
        X, Y = generate_blocks(n_samples=1)
        x, y = X[0], Y[0]
        w = np.array([1, 0,  # unary
                      0, 1,
                      0,     # pairwise
                      -4, 0])


# Path: pystruct/models/grid_crf.py
# class GridCRF(GraphCRF):
#     """Pairwise CRF on a 2d grid.
# 
#     Pairwise potentials are symmetric and the same for all edges.
#     This leads to n_classes parameters for unary potentials and
#     n_classes * (n_classes + 1) / 2 parameters for edge potentials.
# 
#     Unary evidence ``x`` is given as array of shape (width, height, n_features),
#     labels ``y`` are given as array of shape (width, height). Grid sizes do not
#     need to be constant over the dataset.
# 
#     Parameters
#     ----------
#     n_states : int, default=2
#         Number of states for all variables.
# 
#     inference_method : string, default="ad3"
#         Function to call do do inference and loss-augmented inference.
#         Possible values are:
# 
#             - 'max-product' for max-product belief propagation.
#                 Recommended for chains an trees. Loopy belief propagation in
#                 case of a general graph.
#             - 'lp' for Linear Programming relaxation using cvxopt.
#             - 'ad3' for AD3 dual decomposition.
#             - 'qpbo' for QPBO + alpha expansion.
#             - 'ogm' for OpenGM inference algorithms.
# 
#     neighborhood : int, default=4
#         Neighborhood defining connection for each variable in the grid.
#         Possible choices are 4 and 8.
#     """
#     def __init__(self, n_states=None, n_features=None, inference_method=None,
#                  neighborhood=4):
#         self.neighborhood = neighborhood
#         GraphCRF.__init__(self, n_states=n_states, n_features=n_features,
#                           inference_method=inference_method)
# 
#     def _get_edges(self, x):
#         return make_grid_edges(x, neighborhood=self.neighborhood)
# 
#     def _get_features(self, x):
#         return x.reshape(-1, self.n_features)
# 
#     def _reshape_y(self, y, shape_x, return_energy):
#         if return_energy:
#             y, energy = y
# 
#         if isinstance(y, tuple):
#             y = (y[0].reshape(shape_x[0], shape_x[1], y[0].shape[1]), y[1])
#         else:
#             y = y.reshape(shape_x[:-1])
# 
#         if return_energy:
#             return y, energy
#         return y
# 
#     def inference(self, x, w, relaxed=False, return_energy=False):
#         y = GraphCRF.inference(self, x, w, relaxed=relaxed,
#                                return_energy=return_energy)
#         return self._reshape_y(y, x.shape, return_energy)
# 
#     def loss_augmented_inference(self, x, y, w, relaxed=False,
#                                  return_energy=False):
#         y_hat = GraphCRF.loss_augmented_inference(self, x, y.ravel(), w,
#                                                   relaxed=relaxed,
#                                                   return_energy=return_energy)
#         return self._reshape_y(y_hat, x.shape, return_energy)
# 
#     def continuous_loss(self, y, y_hat):
#         # continuous version of the loss
#         # y_hat is the result of linear programming
#         return GraphCRF.continuous_loss(
#             self, y.ravel(), y_hat.reshape(-1, y_hat.shape[-1]))
# 
# Path: pystruct/inference/inference_methods.py
# def get_installed(method_filter=None):
#     if method_filter is None:
#         method_filter = ["max-product", 'ad3', 'ad3+', 'qpbo', 'ogm', 'lp']
# 
#     installed = []
#     unary = np.zeros((1, 1))
#     pw = np.zeros((1, 1))
#     edges = np.empty((0, 2), dtype=np.int)
#     for method in method_filter:
#         try:
#             if method != 'ad3+':
#                 inference_dispatch(unary, pw, edges, inference_method=method)
#             else:
#                 inference_dispatch(unary, np.zeros((0,1,1))
#                                    , np.zeros((0,2), dtype=np.int)
#                                    , inference_method=method)
#             installed.append(method)
#         except ImportError:
#             pass
#     return installed


# Path: pystruct/tests/test_models/test_grid_crf.py
import numpy as np
from numpy.testing import (assert_array_equal, assert_array_almost_equal,
                           assert_almost_equal)
from pystruct.datasets import (generate_blocks, generate_blocks_multinomial,
                               binary, multinomial)
from pystruct.models import GridCRF
from pystruct.utils import (find_constraint, exhaustive_inference,
                            exhaustive_loss_augmented_inference)
from pystruct.inference import get_installed




def test_continuous_y():
    for inference_method in get_installed(["lp", "ad3"]):
        X, Y = generate_blocks(n_samples=1)
        x, y = X[0], Y[0]
        w = np.array([1, 0,  # unary
                      0, 1,
                      0,     # pairwise
                      -4, 0])


        crf = GridCRF(inference_method=inference_method)