spsklearn.mixture

The spsklearn.mixture module implements mixture modeling algorithms.

Classes

vonMisesFisherMixture

von Mises-Fisher Mixture.

Package Contents

class spsklearn.mixture.vonMisesFisherMixture(n_components=1, *, tol=0.001, max_iter=100, n_init=1, init_params='spherical-k-means++', weights_init=None, means_init=None, kappas_init=None, random_state=None, warm_start=False, verbose=0, verbose_interval=10)

Bases: sklearn.mixture._base.BaseMixture

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von Mises-Fisher Mixture.

Representation of a von Mises-Fisher mixture model probability distribution. This class allows to estimate the parameters of a von Mises-Fisher mixture distribution.

Parameters:

n_components (int, default=1) – The number of mixture components.
tol (float, default=1e-3) – The convergence threshold. EM iterations will stop when the lower bound average gain is below this threshold.
max_iter (int, default=100) – The number of EM iterations to perform.
n_init (int, default=1) – The number of initializations to perform. The best results are kept.
init_params ({'spherical-k-means', 'spherical-k-means++', 'random', 'random_from_data'}, default='spherical-k-means++') –
The method used to initialize the weights, the means and the precisions. String must be one of:
- ’spherical-k-means’ : responsibilities are initialized using spherical k-means.
- ’spherical-k-means++’ : use the spherical k-means++ method to initialize.
- ’random’ : responsibilities are initialized randomly.
- ’random_from_data’ : initial means are randomly selected data points.
weights_init (array-like of shape (n_components, ), default=None) – The user-provided initial weights. If it is None, weights are initialized using the init_params method.
means_init (array-like of shape (n_components, n_features), default=None) – The user-provided initial means, If it is None, means are initialized using the init_params method.
kappas_init (array-like, default=None) – The user-provided initial concentration parameter kappas. If it is None, kappas are initialized using the ‘init_params’ method.
random_state (int, RandomState instance or None, default=None) – Controls the random seed given to the method chosen to initialize the parameters (see init_params). In addition, it controls the generation of random samples from the fitted distribution (see the method sample). Pass an int for reproducible output across multiple function calls. See Glossary.
warm_start (bool, default=False) – If ‘warm_start’ is True, the solution of the last fitting is used as initialization for the next call of fit(). This can speed up convergence when fit is called several times on similar problems. In that case, ‘n_init’ is ignored and only a single initialization occurs upon the first call. See the Glossary.
verbose (int, default=0) – Enable verbose output. If 1 then it prints the current initialization and each iteration step. If greater than 1 then it prints also the log probability and the time needed for each step.
verbose_interval (int, default=10) – Number of iteration done before the next print.

weights_

The weights of each mixture components.

Type:: array-like of shape (n_components,)

means_

The mean of each mixture component.

Type:: array-like of shape (n_components, n_features)

kappas_

The concentration parameter kappas of each mixture component.

Type:: array-like

converged_

True when convergence of the best fit of EM was reached, False otherwise.

Type:: bool

n_iter_

Number of step used by the best fit of EM to reach the convergence.

Type:: int

lower_bound_

Lower bound value on the log-likelihood (of the training data with respect to the model) of the best fit of EM.

Type:: float

n_features_in_

Number of features seen during fit.

Type:: int

feature_names_in_

Names of features seen during fit. Defined only when X has feature names that are all strings.

Type:: ndarray of shape (n_features_in_,)

Examples

>>> import numpy as np
>>> from spsklearn.mixture import vonMisesFisherMixture
>>> X = np.array([[1, 2], [1, 4], [1, 0], [10, 2], [10, 4], [10, 0]])
>>> vmf = vonMisesFisherMixture(n_components=2, random_state=0).fit(X)
>>> vmf.means_
array([[10.,  2.],
       [ 1.,  2.]])
>>> vmf.predict([[0, 0], [12, 3]])
array([1, 0])

fit_predict(X, y=None)

Estimate model parameters using X and predict the labels for X.

The method fits the model n_init times and sets the parameters with which the model has the largest likelihood or lower bound. Within each trial, the method iterates between E-step and M-step for max_iter times until the change of likelihood or lower bound is less than tol, otherwise, a ConvergenceWarning is raised. After fitting, it predicts the most probable label for the input data points.

Parameters:

X (array-like of shape (n_samples, n_features)) – List of n_features-dimensional data points. Each row corresponds to a single data point.
y (Ignored) – Not used, present for API consistency by convention.

Returns:

labels – Component labels.

Return type:

array, shape (n_samples,)

bic(X)

Bayesian information criterion for the current model on the input X.

You can refer to this mathematical section for more details regarding the formulation of the BIC used.

Parameters:: X (array of shape (n_samples, n_dimensions)) – The input samples.
Returns:: bic – The lower the better.
Return type:: float

aic(X)

Akaike information criterion for the current model on the input X.

You can refer to this mathematical section for more details regarding the formulation of the AIC used.

Parameters:: X (array of shape (n_samples, n_dimensions)) – The input samples.
Returns:: aic – The lower the better.
Return type:: float

sample(n_samples=1)

Generate random samples from the fitted von Mises-Fisher mixture distribution.

Parameters:

n_samples (int, default=1) – Number of samples to generate.

Returns:

X (array, shape (n_samples, n_features)) – Randomly generated sample.
y (array, shape (nsamples,)) – Component labels.