pystruct.learners.
StructuredPerceptron
(model, max_iter=100, verbose=0, batch=False, decay_exponent=0, decay_t0=10, average=False, n_jobs=1, logger=None)[source]¶Structured Perceptron training.
Implements a simple structured perceptron with optional averaging. The structured perceptron approximately minimizes the zeroone loss, therefore the learning does not take
model.loss
into account. It is just shown to illustrate the learning progress.As the perceptron learning is not marginbased, the model does not need to provide loss_augmented_inference.
Parameters:  model : StructuredModel


Attributes:  w : ndarray, shape=(model.size_joint_feature,)
``loss_curve_`` : list of float

Methods
fit (X, Y[, initialize]) 
Learn parameters using structured perceptron. 
get_params ([deep]) 
Get parameters for this estimator. 
predict (X) 
Predict output on examples in X. 
score (X, Y) 
Compute score as 1  loss over whole data set. 
set_params (**params) 
Set the parameters of this estimator. 
__init__
(model, max_iter=100, verbose=0, batch=False, decay_exponent=0, decay_t0=10, average=False, n_jobs=1, logger=None)[source]¶fit
(X, Y, initialize=True)[source]¶Learn parameters using structured perceptron.
Parameters:  X : iterable
Y : iterable
initialize : boolean, default=True


get_params
(deep=True)¶Get parameters for this estimator.
Parameters:  deep: boolean, optional :


Returns:  params : mapping of string to any

predict
(X)¶Predict output on examples in X.
Parameters:  X : iterable


Returns:  Y_pred : list

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
Y : iterable


Returns:  score : float

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.
Returns:  self : 
