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pystruct.learners.PrimalDSStructuredSVM — pystruct 0.2.4 documentation

Uses downhill simplex for optimizing an unconstraint primal.

This is basically a sanity check on all other implementations, as this is easier to check for correctness.

Methods

__init__(model, max_iter=100, C=1.0, verbose=0, n_jobs=1, show_loss_every=0, logger=None)¶

get_params(deep=True)¶

Get parameters for this estimator.

Parameters:

deep: boolean, optional :

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params : mapping of string to any

Parameter names mapped to their values.

predict(X)¶

Predict output on examples in X.

Parameters:

X : iterable

Traing instances. Contains the structured input objects.

Returns:

Y_pred : list

List of inference results for X using the learned parameters.

score(X, Y)¶

Compute score as 1 - loss over whole data set.

Returns the average accuracy (in terms of model.loss) over X and Y.

Parameters:

X : iterable

Evaluation data.

Y : iterable

True labels.

Returns:

score : float

Average of 1 - loss over training examples.

set_params(**params)¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

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