model : StructuredModel
Object containing the model structure. Has to implement loss, inference and loss_augmented_inference.
max_iter : int, default=1000
Maximum number of passes over dataset to find constraints.
C : float, default=1
Regularization parameter. Corresponds to 1 / (lambda * n_samples).
verbose : int
Verbosity.
n_jobs : int, default=1
Number of parallel processes. Currently only n_jobs=1 is supported.
show_loss_every : int, default=0
How often the training set loss should be computed. Zero corresponds to never.
tol : float, default=1e-3
Convergence tolerance on the duality gap.
logger : logger object, default=None
Pystruct logger for storing the model or extracting additional information.
batch_mode : boolean, default=False
Whether to use batch updates. Will slow down learning enormously.
line_search : boolean, default=True
Whether to compute the optimum step size in each step. The line-search is done in closed form and cheap. There is usually no reason to turn this off.
check_dual_every : int, default=10
How often the stopping criterion should be checked. Computing the stopping criterion is as costly as doing one pass over the dataset, so check_dual_every=1 will make learning twice as slow.
do_averaging : bool, default=True
Whether to use weight averaging as described in the reference paper. Currently this is only supported in the block-coordinate version.
random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.
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