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

bandwidthfloat

The bandwidth of the kernel.

algorithmstring

The tree algorithm to use. Valid options are [‘kd_tree’|’ball_tree’|’auto’]. Default is ‘auto’.

kernelstring

The kernel to use. Valid kernels are [‘gaussian’|’tophat’|’epanechnikov’|’exponential’|’linear’|’cosine’] Default is ‘gaussian’.

metricstring

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’.

atolfloat

The desired absolute tolerance of the result. A larger tolerance will generally lead to faster execution. Default is 0.

rtolfloat

The desired relative tolerance of the result. A larger tolerance will generally lead to faster execution. Default is 1E-8.

breadth_firstboolean

If true (default), use a breadth-first approach to the problem. Otherwise use a depth-first approach.

leaf_sizeint

Specify the leaf size of the underlying tree. See BallTree or KDTree for details. Default is 40.

metric_paramsdict

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

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

Initialize self. See help(type(self)) for accurate signature.

fit(self, X, y=None, sample_weight=None)[source]¶

Fit the Kernel Density model on the data.

Parameters

Xarray_like, shape (n_samples, n_features)

List of n_features-dimensional data points. Each row corresponds to a single data point.

sample_weightarray_like, shape (n_samples,), optional

List of sample weights attached to the data X.

get_params(self, deep=True)[source]¶

Get parameters for this estimator.

Parameters

deepboolean, optional

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

paramsmapping of string to any

Parameter names mapped to their values.

sample(self, 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_samplesint, optional

Number of samples to generate. Defaults to 1.

random_stateint, RandomState instance or None. default to None

If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

Returns

Xarray_like, shape (n_samples, n_features)

List of samples.

score(self, X, y=None)[source]¶

Compute the total log probability density under the model.

Parameters

Xarray_like, shape (n_samples, n_features)

List of n_features-dimensional data points. Each row corresponds to a single data point.

Returns

logprobfloat

Total log-likelihood of the data in X. This is normalized to be a probability density, so the value will be low for high-dimensional data.

score_samples(self, X)[source]¶

Evaluate the density model on the data.

Parameters

Xarray_like, shape (n_samples, n_features)

An array of points to query. Last dimension should match dimension of training data (n_features).

Returns

densityndarray, shape (n_samples,)

The array of log(density) evaluations. These are normalized to be probability densities, so values will be low for high-dimensional data.

set_params(self, **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