pystruct.models.
LatentNodeCRF
(n_labels=None, n_features=None, n_hidden_states=2, inference_method=None, class_weight=None, latent_node_features=False)[source]¶CRF with latent variables.
Input x is tuple (features, edges, n_hidden) First features.shape[0] nodes are observed, then n_hidden unobserved nodes.
Currently unobserved nodes don’t have features.
Parameters:  n_labels : int, default=2
n_hidden_states : int, default=2
n_features : int, default=None
inference_method : string, default=None
class_weight : None, or arraylike
latent_node_features : bool, default=False


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 (h) 
__init__
(n_labels=None, n_features=None, n_hidden_states=2, inference_method=None, class_weight=None, latent_node_features=False)[source]¶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)[source]¶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,)
