sklearn.neighbors.KernelDensity

class sklearn.neighbors.KernelDensity(bandwidth=1.0, algorithm='auto', kernel='gaussian', metric='euclidean', atol=0, rtol=0, breadth_first=True, leaf_size=40, metric_params=None) [source]

Kernel Density Estimation

Read more in the User Guide.

Parameters:

bandwidth : float The bandwidth of the kernel. algorithm : string The tree algorithm to use. Valid options are [‘kd_tree’|’ball_tree’|’auto’]. Default is ‘auto’. kernel : string The kernel to use. Valid kernels are [‘gaussian’|’tophat’|’epanechnikov’|’exponential’|’linear’|’cosine’] Default is ‘gaussian’. metric : string The distance metric to use. Note that not all metrics are valid with all algorithms. Refer to the documentation of BallTree and KDTree for a description of available algorithms. Note that the normalization of the density output is correct only for the Euclidean distance metric. Default is ‘euclidean’. atol : float The desired absolute tolerance of the result. A larger tolerance will generally lead to faster execution. Default is 0. rtol : float The desired relative tolerance of the result. A larger tolerance will generally lead to faster execution. Default is 1E-8. breadth_first : boolean If true (default), use a breadth-first approach to the problem. Otherwise use a depth-first approach. leaf_size : int Specify the leaf size of the underlying tree. See BallTree or KDTree for details. Default is 40. metric_params : dict Additional parameters to be passed to the tree for use with the metric. For more information, see the documentation of BallTree or KDTree.

Methods

fit(X[, y])	Fit the Kernel Density model on the data.
get_params([deep])	Get parameters for this estimator.
sample([n_samples, random_state])	Generate random samples from the model.
score(X[, y])	Compute the total log probability under the model.
score_samples(X)	Evaluate the density model on the data.
set_params(**params)	Set the parameters of this estimator.

__init__(bandwidth=1.0, algorithm='auto', kernel='gaussian', metric='euclidean', atol=0, rtol=0, breadth_first=True, leaf_size=40, metric_params=None) [source]

fit(X, y=None) [source]

Fit the Kernel Density model on 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.

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.
Returns:	params : mapping of string to any Parameter names mapped to their values.

sample(n_samples=1, random_state=None) [source]

Generate random samples from the model.

Currently, this is implemented only for gaussian and tophat kernels.

Parameters:	n_samples : int, optional Number of samples to generate. Defaults to 1. random_state : RandomState or an int seed (0 by default) A random number generator instance.
Returns:	X : array_like, shape (n_samples, n_features) List of samples.

score(X, y=None) [source]

Compute the total 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 : float Total log-likelihood of the data in X.

score_samples(X) [source]

Evaluate the density model on the data.

Parameters:	X : array_like, shape (n_samples, n_features) An array of points to query. Last dimension should match dimension of training data (n_features).
Returns:	density : ndarray, shape (n_samples,) The array of log(density) evaluations.

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 :

Examples using sklearn.neighbors.KernelDensity