class sklearn.mixture.DPGMM(*args, **kwargs)
[source]
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 use sklearn.mixture.BayesianGaussianMixture
class with parameter weight_concentration_prior_type=’dirichlet_process’
instead. DPGMM is deprecated in 0.18 and will be removed in 0.20.
aic(X)
[source]
Akaike information criterion for the current model fit and the proposed data.
Parameters: | X : array of shape(n_samples, n_dimensions) |
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Returns: | aic: float (the lower the better) : |
bic(X)
[source]
Bayesian information criterion for the current model fit and the proposed data.
Parameters: | X : array of shape(n_samples, n_dimensions) |
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Returns: | bic: float (the lower the better) : |
fit(X, y=None)
[source]
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. |
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Returns: |
self : |
fit_predict(X, y=None)
[source]
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] |
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Returns: | C : array, shape = (n_samples,) component memberships |
get_params(deep=True)
[source]
Get parameters for this estimator.
Parameters: |
deep: boolean, optional : If True, will return the parameters for this estimator and contained subobjects that are estimators. |
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Returns: |
params : mapping of string to any Parameter names mapped to their values. |
lower_bound(X, z)
[source]
returns a lower bound on model evidence based on X and membership
predict(X)
[source]
Predict label for data.
Parameters: | X : array-like, shape = [n_samples, n_features] |
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Returns: | C : array, shape = (n_samples,) component memberships |
predict_proba(X)
[source]
Predict posterior probability of data under each Gaussian in the model.
Parameters: |
X : array-like, shape = [n_samples, n_features] |
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Returns: |
responsibilities : array-like, shape = (n_samples, n_components) Returns the probability of the sample for each Gaussian (state) in the model. |
sample(n_samples=1, random_state=None)
[source]
Generate random samples from the model.
Parameters: |
n_samples : int, optional Number of samples to generate. Defaults to 1. |
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Returns: |
X : array_like, shape (n_samples, n_features) List of samples |
score(X, y=None)
[source]
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. |
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Returns: |
logprob : array_like, shape (n_samples,) Log probabilities of each data point in X |
score_samples(X)
[source]
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. |
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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 |
set_params(**params)
[source]
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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© 2007–2016 The scikit-learn developers
Licensed under the 3-clause BSD License.
http://scikit-learn.org/stable/modules/generated/sklearn.mixture.DPGMM.html