Mixture

BayesianGaussianMixture¶

Module Sklearn.Mixture.BayesianGaussianMixture wraps Python class sklearn.mixture.BayesianGaussianMixture.

type t

create¶

constructor and attributes create

val create :
  ?n_components:int ->
  ?covariance_type:[`Full | `Tied | `Diag | `Spherical] ->
  ?tol:float ->
  ?reg_covar:float ->
  ?max_iter:int ->
  ?n_init:int ->
  ?init_params:[`Kmeans | `Random] ->
  ?weight_concentration_prior_type:string ->
  ?weight_concentration_prior:float ->
  ?mean_precision_prior:float ->
  ?mean_prior:[>`ArrayLike] Np.Obj.t ->
  ?degrees_of_freedom_prior:float ->
  ?covariance_prior:[>`ArrayLike] Np.Obj.t ->
  ?random_state:int ->
  ?warm_start:bool ->
  ?verbose:int ->
  ?verbose_interval:int ->
  unit ->
  t

Variational Bayesian estimation of a Gaussian mixture.

This class allows to infer an approximate posterior distribution over the parameters of a Gaussian mixture distribution. The effective number of components can be inferred from the data.

This class implements two types of prior for the weights distribution: a finite mixture model with Dirichlet distribution and an infinite mixture model with the Dirichlet Process. In practice Dirichlet Process inference algorithm is approximated and uses a truncated distribution with a fixed maximum number of components (called the Stick-breaking representation). The number of components actually used almost always depends on the data.

.. versionadded:: 0.18

Read more in the :ref:User Guide <bgmm>.

Parameters

n_components : int, defaults to 1. The number of mixture components. Depending on the data and the value of the weight_concentration_prior the model can decide to not use all the components by setting some component weights_ to values very close to zero. The number of effective components is therefore smaller than n_components.

covariance_type : {'full', 'tied', 'diag', 'spherical'}, defaults to 'full' String describing the type of covariance parameters to use. Must be one of::

'full' (each component has its own general covariance matrix),
'tied' (all components share the same general covariance matrix),
'diag' (each component has its own diagonal covariance matrix),
'spherical' (each component has its own single variance).

tol : float, defaults to 1e-3. The convergence threshold. EM iterations will stop when the lower bound average gain on the likelihood (of the training data with respect to the model) is below this threshold.
reg_covar : float, defaults to 1e-6. Non-negative regularization added to the diagonal of covariance. Allows to assure that the covariance matrices are all positive.
max_iter : int, defaults to 100. The number of EM iterations to perform.
n_init : int, defaults to 1. The number of initializations to perform. The result with the highest lower bound value on the likelihood is kept.
init_params : {'kmeans', 'random'}, defaults to 'kmeans'. The method used to initialize the weights, the means and the covariances. Must be one of::
```
'kmeans' : responsibilities are initialized using kmeans.
'random' : responsibilities are initialized randomly.
```
weight_concentration_prior_type : str, defaults to 'dirichlet_process'. String describing the type of the weight concentration prior. Must be one of::
```
'dirichlet_process' (using the Stick-breaking representation),
'dirichlet_distribution' (can favor more uniform weights).
```
weight_concentration_prior : float | None, optional. The dirichlet concentration of each component on the weight distribution (Dirichlet). This is commonly called gamma in the literature. The higher concentration puts more mass in the center and will lead to more components being active, while a lower concentration parameter will lead to more mass at the edge of the mixture weights simplex. The value of the parameter must be greater than 0. If it is None, it's set to 1. / n_components.
mean_precision_prior : float | None, optional. The precision prior on the mean distribution (Gaussian). Controls the extent of where means can be placed. Larger values concentrate the cluster means around mean_prior. The value of the parameter must be greater than 0. If it is None, it is set to 1.
mean_prior : array-like, shape (n_features,), optional The prior on the mean distribution (Gaussian). If it is None, it is set to the mean of X.
degrees_of_freedom_prior : float | None, optional. The prior of the number of degrees of freedom on the covariance distributions (Wishart). If it is None, it's set to n_features.
covariance_prior : float or array-like, optional The prior on the covariance distribution (Wishart). If it is None, the emiprical covariance prior is initialized using the covariance of X. The shape depends on covariance_type::
```
    (n_features, n_features) if 'full',
    (n_features, n_features) if 'tied',
    (n_features)             if 'diag',
    float                    if 'spherical'
```
random_state : int, RandomState instance or None, optional (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 :term:Glossary <random_state>.
warm_start : bool, default to 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.
See :term:the Glossary <warm_start>.
verbose : int, default to 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 to 10. Number of iteration done before the next print.

Attributes

weights_ : array-like, shape (n_components,) The weights of each mixture components.
means_ : array-like, shape (n_components, n_features) The mean of each mixture component.

covariances_ : array-like The covariance of each mixture component. The shape depends on covariance_type::

(n_components,)                        if 'spherical',
(n_features, n_features)               if 'tied',
(n_components, n_features)             if 'diag',
(n_components, n_features, n_features) if 'full'

precisions_ : array-like The precision matrices for each component in the mixture. A precision matrix is the inverse of a covariance matrix. A covariance matrix is symmetric positive definite so the mixture of Gaussian can be equivalently parameterized by the precision matrices. Storing the precision matrices instead of the covariance matrices makes it more efficient to compute the log-likelihood of new samples at test time. The shape depends on covariance_type::
```
(n_components,)                        if 'spherical',
(n_features, n_features)               if 'tied',
(n_components, n_features)             if 'diag',
(n_components, n_features, n_features) if 'full'
```
precisions_cholesky_ : array-like The cholesky decomposition of the precision matrices of each mixture component. A precision matrix is the inverse of a covariance matrix. A covariance matrix is symmetric positive definite so the mixture of Gaussian can be equivalently parameterized by the precision matrices. Storing the precision matrices instead of the covariance matrices makes it more efficient to compute the log-likelihood of new samples at test time. The shape depends on covariance_type::
```
(n_components,)                        if 'spherical',
(n_features, n_features)               if 'tied',
(n_components, n_features)             if 'diag',
(n_components, n_features, n_features) if 'full'
```
converged_ : bool True when convergence was reached in fit(), False otherwise.
n_iter_ : int Number of step used by the best fit of inference to reach the convergence.
lower_bound_ : float Lower bound value on the likelihood (of the training data with respect to the model) of the best fit of inference.
weight_concentration_prior_ : tuple or float The dirichlet concentration of each component on the weight distribution (Dirichlet). The type depends on weight_concentration_prior_type::
```
(float, float) if 'dirichlet_process' (Beta parameters),
float          if 'dirichlet_distribution' (Dirichlet parameters).
```
The higher concentration puts more mass in the center and will lead to more components being active, while a lower concentration parameter will lead to more mass at the edge of the simplex.
weight_concentration_ : array-like, shape (n_components,) The dirichlet concentration of each component on the weight distribution (Dirichlet).
mean_precision_prior_ : float The precision prior on the mean distribution (Gaussian). Controls the extent of where means can be placed. Larger values concentrate the cluster means around mean_prior. If mean_precision_prior is set to None, mean_precision_prior_ is set to 1.
mean_precision_ : array-like, shape (n_components,) The precision of each components on the mean distribution (Gaussian).
mean_prior_ : array-like, shape (n_features,) The prior on the mean distribution (Gaussian).
degrees_of_freedom_prior_ : float The prior of the number of degrees of freedom on the covariance distributions (Wishart).
degrees_of_freedom_ : array-like, shape (n_components,) The number of degrees of freedom of each components in the model.

covariance_prior_ : float or array-like The prior on the covariance distribution (Wishart). The shape depends on covariance_type::

(n_features, n_features) if 'full',
(n_features, n_features) if 'tied',
(n_features)             if 'diag',
float                    if 'spherical'

References

.. [1] Bishop, Christopher M. (2006). 'Pattern recognition and machine learning'. Vol. 4 No. 4. New York: Springer. <https://www.springer.com/kr/book/9780387310732>_

.. [2] Hagai Attias. (2000). 'A Variational Bayesian Framework for Graphical Models'. In Advances in Neural Information Processing Systems 12. <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.36.2841&rep=rep1&type=pdf>_

.. [3] Blei, David M. and Michael I. Jordan. (2006). 'Variational inference for Dirichlet process mixtures'. Bayesian analysis 1.1 <https://www.cs.princeton.edu/courses/archive/fall11/cos597C/reading/BleiJordan2005.pdf>_

fit¶

method fit

val fit :
  ?y:Py.Object.t ->
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  t

Estimate model parameters with the EM algorithm.

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. If warm_start is True, then n_init is ignored and a single initialization is performed upon the first call. Upon consecutive calls, training starts where it left off.

Parameters

X : array-like, shape (n_samples, n_features) List of n_features-dimensional data points. Each row corresponds to a single data point.

Returns

self

fit_predict¶

method fit_predict

val fit_predict :
  ?y:Py.Object.t ->
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [>`ArrayLike] Np.Obj.t

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 :class:~sklearn.exceptions.ConvergenceWarning is raised. After fitting, it predicts the most probable label for the input data points.

.. versionadded:: 0.20

Parameters

X : array-like, shape (n_samples, n_features) List of n_features-dimensional data points. Each row corresponds to a single data point.

Returns

labels : array, shape (n_samples,) Component labels.

get_params¶

method get_params

val get_params :
  ?deep:bool ->
  [> tag] Obj.t ->
  Dict.t

Get parameters for this estimator.

Parameters

deep : bool, default=True 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¶

method predict

val predict :
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [>`ArrayLike] Np.Obj.t

Predict the labels for the data samples in X using trained model.

Parameters

X : array-like, shape (n_samples, n_features) List of n_features-dimensional data points. Each row corresponds to a single data point.

Returns

labels : array, shape (n_samples,) Component labels.

predict_proba¶

method predict_proba

val predict_proba :
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [>`ArrayLike] Np.Obj.t

Predict posterior probability of each component given the data.

Parameters

X : array-like, shape (n_samples, n_features) List of n_features-dimensional data points. Each row corresponds to a single data point.

Returns

resp : array, shape (n_samples, n_components) Returns the probability each Gaussian (state) in the model given each sample.

sample¶

method sample

val sample :
  ?n_samples:int ->
  [> tag] Obj.t ->
  ([>`ArrayLike] Np.Obj.t * [>`ArrayLike] Np.Obj.t)

Generate random samples from the fitted Gaussian distribution.

Parameters

n_samples : int, optional Number of samples to generate. Defaults to 1.

Returns

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

score¶

method score

val score :
  ?y:Py.Object.t ->
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  float

Compute the per-sample average log-likelihood of the given data X.

Parameters

X : array-like, shape (n_samples, n_dimensions) List of n_features-dimensional data points. Each row corresponds to a single data point.

Returns

log_likelihood : float Log likelihood of the Gaussian mixture given X.

score_samples¶

method score_samples

val score_samples :
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [>`ArrayLike] Np.Obj.t

Compute the weighted log probabilities for each sample.

Parameters

X : array-like, shape (n_samples, n_features) List of n_features-dimensional data points. Each row corresponds to a single data point.

Returns

log_prob : array, shape (n_samples,) Log probabilities of each data point in X.

set_params¶

method set_params

val set_params :
  ?params:(string * Py.Object.t) list ->
  [> tag] Obj.t ->
  t

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.

Parameters

**params : dict Estimator parameters.

Returns

self : object Estimator instance.

weights_¶

attribute weights_

val weights_ : t -> [>`ArrayLike] Np.Obj.t
val weights_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

means_¶

attribute means_

val means_ : t -> [>`ArrayLike] Np.Obj.t
val means_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

covariances_¶

attribute covariances_

val covariances_ : t -> [>`ArrayLike] Np.Obj.t
val covariances_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

precisions_¶

attribute precisions_

val precisions_ : t -> [>`ArrayLike] Np.Obj.t
val precisions_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

precisions_cholesky_¶

attribute precisions_cholesky_

val precisions_cholesky_ : t -> [>`ArrayLike] Np.Obj.t
val precisions_cholesky_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

converged_¶

attribute converged_

val converged_ : t -> bool
val converged_opt : t -> (bool) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

n_iter_¶

attribute n_iter_

val n_iter_ : t -> int
val n_iter_opt : t -> (int) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

lower_bound_¶

attribute lower_bound_

val lower_bound_ : t -> float
val lower_bound_opt : t -> (float) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

weight_concentration_prior_¶

attribute weight_concentration_prior_

val weight_concentration_prior_ : t -> Py.Object.t
val weight_concentration_prior_opt : t -> (Py.Object.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

weight_concentration_¶

attribute weight_concentration_

val weight_concentration_ : t -> [>`ArrayLike] Np.Obj.t
val weight_concentration_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

mean_precision_prior_¶

attribute mean_precision_prior_

val mean_precision_prior_ : t -> float
val mean_precision_prior_opt : t -> (float) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

mean_precision_¶

attribute mean_precision_

val mean_precision_ : t -> [>`ArrayLike] Np.Obj.t
val mean_precision_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

mean_prior_¶

attribute mean_prior_

val mean_prior_ : t -> [>`ArrayLike] Np.Obj.t
val mean_prior_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

degrees_of_freedom_prior_¶

attribute degrees_of_freedom_prior_

val degrees_of_freedom_prior_ : t -> float
val degrees_of_freedom_prior_opt : t -> (float) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

degrees_of_freedom_¶

attribute degrees_of_freedom_

val degrees_of_freedom_ : t -> [>`ArrayLike] Np.Obj.t
val degrees_of_freedom_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

covariance_prior_¶

attribute covariance_prior_

val covariance_prior_ : t -> [>`ArrayLike] Np.Obj.t
val covariance_prior_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

to_string¶

method to_string

val to_string: t -> string

Print the object to a human-readable representation.

show¶

method show

val show: t -> string

Print the object to a human-readable representation.

pp¶

method pp

val pp: Format.formatter -> t -> unit

Pretty-print the object to a formatter.

GaussianMixture¶

Module Sklearn.Mixture.GaussianMixture wraps Python class sklearn.mixture.GaussianMixture.

type t

create¶

constructor and attributes create

val create :
  ?n_components:int ->
  ?covariance_type:[`Full | `Tied | `Diag | `Spherical] ->
  ?tol:float ->
  ?reg_covar:float ->
  ?max_iter:int ->
  ?n_init:int ->
  ?init_params:[`Kmeans | `Random] ->
  ?weights_init:[>`ArrayLike] Np.Obj.t ->
  ?means_init:[>`ArrayLike] Np.Obj.t ->
  ?precisions_init:[>`ArrayLike] Np.Obj.t ->
  ?random_state:int ->
  ?warm_start:bool ->
  ?verbose:int ->
  ?verbose_interval:int ->
  unit ->
  t

Gaussian Mixture.

Representation of a Gaussian mixture model probability distribution. This class allows to estimate the parameters of a Gaussian mixture distribution.

Read more in the :ref:User Guide <gmm>.

.. versionadded:: 0.18

Parameters

n_components : int, defaults to 1. The number of mixture components.
covariance_type : {'full' (default), 'tied', 'diag', 'spherical'} String describing the type of covariance parameters to use. Must be one of:

'full' each component has its own general covariance matrix 'tied' all components share the same general covariance matrix 'diag' each component has its own diagonal covariance matrix 'spherical' each component has its own single variance
tol : float, defaults to 1e-3. The convergence threshold. EM iterations will stop when the lower bound average gain is below this threshold.
reg_covar : float, defaults to 1e-6. Non-negative regularization added to the diagonal of covariance. Allows to assure that the covariance matrices are all positive.
max_iter : int, defaults to 100. The number of EM iterations to perform.
n_init : int, defaults to 1. The number of initializations to perform. The best results are kept.
init_params : {'kmeans', 'random'}, defaults to 'kmeans'. The method used to initialize the weights, the means and the precisions. Must be one of::
```
'kmeans' : responsibilities are initialized using kmeans.
'random' : responsibilities are initialized randomly.
```
weights_init : array-like, shape (n_components, ), optional The user-provided initial weights, defaults to None. If it None, weights are initialized using the init_params method.
means_init : array-like, shape (n_components, n_features), optional The user-provided initial means, defaults to None, If it None, means are initialized using the init_params method.
precisions_init : array-like, optional. The user-provided initial precisions (inverse of the covariance matrices), defaults to None. If it None, precisions are initialized using the 'init_params' method. The shape depends on 'covariance_type'::
```
(n_components,)                        if 'spherical',
(n_features, n_features)               if 'tied',
(n_components, n_features)             if 'diag',
(n_components, n_features, n_features) if 'full'
```
random_state : int, RandomState instance or None, optional (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 :term:Glossary <random_state>.
warm_start : bool, default to 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 :term:the Glossary <warm_start>.
verbose : int, default to 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 to 10. Number of iteration done before the next print.

Attributes

weights_ : array-like, shape (n_components,) The weights of each mixture components.
means_ : array-like, shape (n_components, n_features) The mean of each mixture component.

covariances_ : array-like The covariance of each mixture component. The shape depends on covariance_type::

(n_components,)                        if 'spherical',
(n_features, n_features)               if 'tied',
(n_components, n_features)             if 'diag',
(n_components, n_features, n_features) if 'full'

precisions_ : array-like The precision matrices for each component in the mixture. A precision matrix is the inverse of a covariance matrix. A covariance matrix is symmetric positive definite so the mixture of Gaussian can be equivalently parameterized by the precision matrices. Storing the precision matrices instead of the covariance matrices makes it more efficient to compute the log-likelihood of new samples at test time. The shape depends on covariance_type::
```
(n_components,)                        if 'spherical',
(n_features, n_features)               if 'tied',
(n_components, n_features)             if 'diag',
(n_components, n_features, n_features) if 'full'
```
precisions_cholesky_ : array-like The cholesky decomposition of the precision matrices of each mixture component. A precision matrix is the inverse of a covariance matrix. A covariance matrix is symmetric positive definite so the mixture of Gaussian can be equivalently parameterized by the precision matrices. Storing the precision matrices instead of the covariance matrices makes it more efficient to compute the log-likelihood of new samples at test time. The shape depends on covariance_type::
```
(n_components,)                        if 'spherical',
(n_features, n_features)               if 'tied',
(n_components, n_features)             if 'diag',
(n_components, n_features, n_features) if 'full'
```
converged_ : bool True when convergence was reached in fit(), False otherwise.
n_iter_ : int Number of step used by the best fit of EM to reach the convergence.
lower_bound_ : float Lower bound value on the log-likelihood (of the training data with respect to the model) of the best fit of EM.

aic¶

method aic

val aic :
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  float

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

Parameters

X : array of shape (n_samples, n_dimensions)

Returns

aic : float The lower the better.

bic¶

method bic

val bic :
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  float

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

Parameters

X : array of shape (n_samples, n_dimensions)

Returns

bic : float The lower the better.

fit¶

method fit

val fit :
  ?y:Py.Object.t ->
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  t

Estimate model parameters with the EM algorithm.

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. If warm_start is True, then n_init is ignored and a single initialization is performed upon the first call. Upon consecutive calls, training starts where it left off.

Parameters

X : array-like, shape (n_samples, n_features) List of n_features-dimensional data points. Each row corresponds to a single data point.

Returns

self

fit_predict¶

method fit_predict

val fit_predict :
  ?y:Py.Object.t ->
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [>`ArrayLike] Np.Obj.t

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 :class:~sklearn.exceptions.ConvergenceWarning is raised. After fitting, it predicts the most probable label for the input data points.

.. versionadded:: 0.20

Parameters

X : array-like, shape (n_samples, n_features) List of n_features-dimensional data points. Each row corresponds to a single data point.

Returns

labels : array, shape (n_samples,) Component labels.

get_params¶

method get_params

val get_params :
  ?deep:bool ->
  [> tag] Obj.t ->
  Dict.t

Get parameters for this estimator.

Parameters

deep : bool, default=True 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¶

method predict

val predict :
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [>`ArrayLike] Np.Obj.t

Predict the labels for the data samples in X using trained model.

Parameters

X : array-like, shape (n_samples, n_features) List of n_features-dimensional data points. Each row corresponds to a single data point.

Returns

labels : array, shape (n_samples,) Component labels.

predict_proba¶

method predict_proba

val predict_proba :
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [>`ArrayLike] Np.Obj.t

Predict posterior probability of each component given the data.

Parameters

X : array-like, shape (n_samples, n_features) List of n_features-dimensional data points. Each row corresponds to a single data point.

Returns

resp : array, shape (n_samples, n_components) Returns the probability each Gaussian (state) in the model given each sample.

sample¶

method sample

val sample :
  ?n_samples:int ->
  [> tag] Obj.t ->
  ([>`ArrayLike] Np.Obj.t * [>`ArrayLike] Np.Obj.t)

Generate random samples from the fitted Gaussian distribution.

Parameters

n_samples : int, optional Number of samples to generate. Defaults to 1.

Returns

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

score¶

method score

val score :
  ?y:Py.Object.t ->
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  float

Compute the per-sample average log-likelihood of the given data X.

Parameters

X : array-like, shape (n_samples, n_dimensions) List of n_features-dimensional data points. Each row corresponds to a single data point.

Returns

log_likelihood : float Log likelihood of the Gaussian mixture given X.

score_samples¶

method score_samples

val score_samples :
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [>`ArrayLike] Np.Obj.t

Compute the weighted log probabilities for each sample.

Parameters

X : array-like, shape (n_samples, n_features) List of n_features-dimensional data points. Each row corresponds to a single data point.

Returns

log_prob : array, shape (n_samples,) Log probabilities of each data point in X.

set_params¶

method set_params

val set_params :
  ?params:(string * Py.Object.t) list ->
  [> tag] Obj.t ->
  t

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.

Parameters

**params : dict Estimator parameters.

Returns

self : object Estimator instance.

weights_¶

attribute weights_

val weights_ : t -> [>`ArrayLike] Np.Obj.t
val weights_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

means_¶

attribute means_

val means_ : t -> [>`ArrayLike] Np.Obj.t
val means_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

covariances_¶

attribute covariances_

val covariances_ : t -> [>`ArrayLike] Np.Obj.t
val covariances_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

precisions_¶

attribute precisions_

val precisions_ : t -> [>`ArrayLike] Np.Obj.t
val precisions_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

precisions_cholesky_¶

attribute precisions_cholesky_

val precisions_cholesky_ : t -> [>`ArrayLike] Np.Obj.t
val precisions_cholesky_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

converged_¶

attribute converged_

val converged_ : t -> bool
val converged_opt : t -> (bool) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

n_iter_¶

attribute n_iter_

val n_iter_ : t -> int
val n_iter_opt : t -> (int) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

lower_bound_¶

attribute lower_bound_

val lower_bound_ : t -> float
val lower_bound_opt : t -> (float) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

to_string¶

method to_string

val to_string: t -> string

Print the object to a human-readable representation.

show¶

method show

val show: t -> string

Print the object to a human-readable representation.

pp¶

method pp

val pp: Format.formatter -> t -> unit

Pretty-print the object to a formatter.

Mixture

BayesianGaussianMixture¶

create¶

Parameters

Attributes

See Also

References

fit¶

Parameters

Returns

fit_predict¶

Parameters

Returns

get_params¶

Parameters

Returns

predict¶

Parameters

Returns

predict_proba¶

Parameters

Returns

sample¶

Parameters

Returns

score¶

Parameters

Returns

score_samples¶

Parameters

Returns

set_params¶

Parameters

Returns

weights_¶

means_¶

covariances_¶

precisions_¶

precisions_cholesky_¶

converged_¶

n_iter_¶

lower_bound_¶

weight_concentration_prior_¶

weight_concentration_¶

mean_precision_prior_¶

mean_precision_¶

mean_prior_¶

degrees_of_freedom_prior_¶

degrees_of_freedom_¶

covariance_prior_¶

to_string¶

show¶

pp¶

GaussianMixture¶

create¶

Parameters

Attributes

See Also

aic¶

Parameters

Returns

bic¶

Parameters

Returns

fit¶

Parameters

Returns

fit_predict¶

Parameters

Returns

get_params¶

Parameters

Returns

predict¶

Parameters

Returns

predict_proba¶

Parameters

Returns

sample¶