sklearn_lvq.LmrslvqModel¶
-
class
sklearn_lvq.LmrslvqModel(prototypes_per_class=1, initial_prototypes=None, initial_matrices=None, regularization=0.0, initialdim=None, classwise=False, sigma=1, max_iter=2500, gtol=1e-05, display=False, random_state=None)[source]¶ Localized Matrix Robust Soft Learning Vector Quantization
- Parameters
- prototypes_per_classint or list of int, optional (default=1)
Number of prototypes per class. Use list to specify different numbers per class.
- initial_prototypesarray-like, shape = [n_prototypes, n_features + 1],
- optional
Prototypes to start with. If not given initialization near the class means. Class label must be placed as last entry of each prototype.
- initial_matriceslist of array-like, optional
Matrices to start with. If not given random initialization
- regularizationfloat or array-like, shape = [n_classes/n_prototypes],
- optional (default=0.0)
Values between 0 and 1. Regularization is done by the log determinant of the relevance matrix. Without regularization relevances may degenerate to zero.
- initialdimint, optional
Maximum rank or projection dimensions
- classwiseboolean, optional
If true, each class has one relevance matrix. If false, each prototype has one relevance matrix.
- sigmafloat, optional (default=0.5)
Variance for the distribution.
- max_iterint, optional (default=2500)
The maximum number of iterations.
- gtolfloat, optional (default=1e-5)
Gradient norm must be less than gtol before successful termination of bfgs.
- displayboolean, optional (default=False)
Print information about the bfgs steps.
- random_stateint, RandomState instance or None, optional
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.
- Attributes
- w_array-like, shape = [n_prototypes, n_features]
Prototype vector, where n_prototypes in the number of prototypes and n_features is the number of features
- c_w_array-like, shape = [n_prototypes]
Prototype classes
- classes_array-like, shape = [n_classes]
Array containing labels.
- omegas_list of array-like
Relevance Matrices
- dim_list of int
Maximum rank of projection
- regularization_array-like, shape = [n_classes/n_prototypes]
Values between 0 and 1
- See also
- ——–
- RslvqModel, MrslvqModel
Methods
fit(x, y)Fit the LVQ model to the given training data and parameters using l-bfgs-b.
get_params([deep])Get parameters for this estimator.
posterior(y, x)calculate the posterior for x:
predict(x)Predict class membership index for each input sample.
project(x, prototype_idx, dims[, …])Projects the data input data X using the relevance matrix of the prototype specified by prototype_idx to dimension dim
score(X, y[, sample_weight])Return the mean accuracy on the given test data and labels.
set_params(**params)Set the parameters of this estimator.