glvq.GmlvqModel

class glvq.GmlvqModel(prototypes_per_class=1, initial_prototypes=None, initial_matrix=None, regularization=0.0, dim=None, max_iter=2500, gtol=1e-05, display=False, random_state=None)

Generalized Matrix Learning Vector Quantization

Parameters:

prototypes_per_class : int or list of int, optional (default=1)

Number of prototypes per class. Use list to specify different numbers per class.

initial_prototypes : array-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_matrix : array-like, shape = [dim, n_features], optional

Relevance matrix to start with. If not given random initialization for rectangular matrix and unity for squared matrix.

regularization : float, optional (default=0.0)

Value between 0 and 1. Regularization is done by the log determinant of the relevance matrix. Without regularization relevances may degenerate to zero.

dim : int, optional (default=nb_features)

Maximum rank or projection dimensions

max_iter : int, optional (default=2500)

The maximum number of iterations.

gtol : float, optional (default=1e-5)

Gradient norm must be less than gtol before successful termination of l-bfgs-b.

display : boolean, optional (default=False)

Print information about the bfgs steps.

random_state : int, RandomState instance or None, optional (default=None)

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.
dim_	(int) Maximum rank or projection dimensions
omega_	(array-like, shape = [dim, n_features]) Relevance matrix

See also

GlvqModel, GrlvqModel, LgmlvqModel

Methods

fit(x, y)	Fit the GLVQ model to the given training data and parameters using l-bfgs-b.
get_params([deep])	Get parameters for this estimator.
predict(x)	Predict class membership index for each input sample.
project(x, dims[, print_variance_covered])	Projects the data input data X using the relevance matrix of trained
score(X, y[, sample_weight])	Returns the mean accuracy on the given test data and labels.
set_params(**params)	Set the parameters of this estimator.

__init__(prototypes_per_class=1, initial_prototypes=None, initial_matrix=None, regularization=0.0, dim=None, max_iter=2500, gtol=1e-05, display=False, random_state=None)

x.__init__(…) initializes x; see help(type(x)) for signature

fit(x, y)

Fit the GLVQ model to the given training data and parameters using l-bfgs-b.

Parameters:

x : array-like, shape = [n_samples, n_features]

Training vector, where n_samples in the number of samples and n_features is the number of features.

y : array, shape = [n_samples]

Target values (integers in classification, real numbers in regression)

Returns:

self

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 class membership index for each input sample.

This function does classification on an array of test vectors X.

Parameters:

x : array-like, shape = [n_samples, n_features]

Returns:

C : array, shape = (n_samples,)

Returns predicted values.

project(x, dims, print_variance_covered=False)

Projects the data input data X using the relevance matrix of trained model to dimension dim

Parameters:

x : array-like, shape = [n,n_features]

input data for project

dims : int

dimension to project to

print_variance_covered : boolean

flag to print the covered variance of the projection

Returns:

C : array, shape = [n,n_features]

Returns predicted values.

score(X, y, sample_weight=None)

Returns the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters:

X : array-like, shape = (n_samples, n_features)

Test samples.

y : array-like, shape = (n_samples) or (n_samples, n_outputs)

True labels for X.

sample_weight : array-like, shape = [n_samples], optional

Sample weights.

Returns:

score : float

Mean accuracy of self.predict(X) wrt. y.

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 latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:self