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)

Kernel Density Estimation

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)	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)	Compute the 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)

fit(X)

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)

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)

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)

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.

score_samples(X)

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

The array of log(density) evaluations. This has shape X.shape[:-1]

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns :self :