pystruct.models.
LatentGraphCRF
(n_labels=None, n_features=None, n_states_per_label=2, inference_method=None)[source]¶CRF with latent states for variables.
This is also called “hidden dynamics CRF”. For each output variable there is an additional variable which can encode additional states and interactions.
Parameters:  n_labels : int
n_featues : int or None (default=None).
n_states_per_label : int or list (default=2)
inference_method : string, default=”ad3”


Methods
base_loss (y, y_hat) 

batch_inference (X, w[, relaxed]) 

batch_joint_feature (X, Y[, Y_true]) 

batch_loss (Y, Y_hat) 

batch_loss_augmented_inference (X, Y, w[, ...]) 

continuous_loss (y, y_hat) 

inference (x, w[, relaxed, return_energy]) 
Inference for x using parameters w. 
init_latent (X, Y) 

initialize (X, Y) 

joint_feature (x, y) 
Feature vector associated with instance (x, y). 
label_from_latent (h) 

latent (x, y, w) 

loss (h, h_hat) 

loss_augmented_inference (x, h, w[, relaxed, ...]) 

max_loss (y) 
inference
(x, w, relaxed=False, return_energy=False)¶Inference for x using parameters w.
Finds (approximately) armin_y np.dot(w, joint_feature(x, y)) using self.inference_method.
Parameters:  x : tuple
w : ndarray, shape=(size_joint_feature,)
relaxed : bool, default=False
return_energy : bool, default=False


Returns:  y_pred : ndarray or tuple

joint_feature
(x, y)¶Feature vector associated with instance (x, y).
Feature representation joint_feature, such that the energy of the configuration (x, y) and a weight vector w is given by np.dot(w, joint_feature(x, y)).
Parameters:  x : tuple
y : ndarray or tuple


Returns:  p : ndarray, shape (size_joint_feature,)
