Warning
DEPRECATED
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class sklearn.mixture.DPGMM(*args, **kwargs)
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Dirichlet Process Gaussian Mixture Models
Deprecated since version 0.18: This class will be removed in 0.20. Use
sklearn.mixture.BayesianGaussianMixture
with parameterweight_concentration_prior_type='dirichlet_process'
instead.Methods
aic
(X)Akaike information criterion for the current model fit and the proposed data. bic
(X)Bayesian information criterion for the current model fit and the proposed data. fit
(X[, y])Estimate model parameters with the EM algorithm. fit_predict
(X[, y])Fit and then predict labels for data. get_params
([deep])Get parameters for this estimator. lower_bound
(X, z)returns a lower bound on model evidence based on X and membership predict
(X)Predict label for data. predict_proba
(X)Predict posterior probability of data under each Gaussian in the model. sample
([n_samples, random_state])Generate random samples from the model. score
(X[, y])Compute the log probability under the model. score_samples
(X)Return the likelihood of the data under the model. set_params
(\*\*params)Set the parameters of this estimator. -
__init__(*args, **kwargs)
[source] -
DEPRECATED: The
DPGMM
class is not working correctly and it?s better to usesklearn.mixture.BayesianGaussianMixture
class with parameterweight_concentration_prior_type=?dirichlet_process?
instead. DPGMM is deprecated in 0.18 and will be removed in 0.20.
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aic(X)
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Akaike information criterion for the current model fit and the proposed data.
Parameters: X : array of shape(n_samples, n_dimensions) Returns: aic: float (the lower the better) :
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bic(X)
[source] -
Bayesian information criterion for the current model fit and the proposed data.
Parameters: X : array of shape(n_samples, n_dimensions) Returns: bic: float (the lower the better) :
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fit(X, y=None)
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Estimate model parameters with the EM algorithm.
A initialization step is performed before entering the expectation-maximization (EM) algorithm. If you want to avoid this step, set the keyword argument init_params to the empty string ?? when creating the GMM object. Likewise, if you would like just to do an initialization, set n_iter=0.
Parameters: X : array_like, shape (n, n_features)
List of n_features-dimensional data points. Each row corresponds to a single data point.
Returns: self :
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fit_predict(X, y=None)
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Fit and then predict labels for data.
Warning: Due to the final maximization step in the EM algorithm, with low iterations the prediction may not be 100% accurate.
New in version 0.17: fit_predict method in Gaussian Mixture Model.
Parameters: X : array-like, shape = [n_samples, n_features] Returns: C : array, shape = (n_samples,) component memberships
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get_params(deep=True)
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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.
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lower_bound(X, z)
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returns a lower bound on model evidence based on X and membership
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predict(X)
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Predict label for data.
Parameters: X : array-like, shape = [n_samples, n_features] Returns: C : array, shape = (n_samples,) component memberships
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predict_proba(X)
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Predict posterior probability of data under each Gaussian in the model.
Parameters: X : array-like, shape = [n_samples, n_features]
Returns: responsibilities : array-like, shape = (n_samples, n_components)
Returns the probability of the sample for each Gaussian (state) in the model.
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sample(n_samples=1, random_state=None)
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Generate random samples from the model.
Parameters: n_samples : int, optional
Number of samples to generate. Defaults to 1.
Returns: X : array_like, shape (n_samples, n_features)
List of samples
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score(X, y=None)
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Compute the log probability under the 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: logprob : array_like, shape (n_samples,)
Log probabilities of each data point in X
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score_samples(X)
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Return the likelihood of the data under the model.
Compute the bound on log probability of X under the model and return the posterior distribution (responsibilities) of each mixture component for each element of X.
This is done by computing the parameters for the mean-field of z for each observation.
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: logprob : array_like, shape (n_samples,)
Log probabilities of each data point in X
responsibilities : array_like, shape (n_samples, n_components)
Posterior probabilities of each mixture component for each observation
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set_params(**params)
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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 :
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