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KernelDensity

Kernel Density Estimation.

Read more in the User Guide.

Python Reference

Constructors

constructor()

Signature

new KernelDensity(opts?: object): KernelDensity;

Parameters

Name Type Description
opts? object -
opts.algorithm? "auto" | "ball_tree" | "kd_tree" The tree algorithm to use. Default Value 'auto'
opts.atol? number The desired absolute tolerance of the result. A larger tolerance will generally lead to faster execution. Default Value 0
opts.bandwidth? number | "scott" | "silverman" The bandwidth of the kernel. If bandwidth is a float, it defines the bandwidth of the kernel. If bandwidth is a string, one of the estimation methods is implemented. Default Value 1
opts.breadth_first? boolean If true (default), use a breadth-first approach to the problem. Otherwise use a depth-first approach. Default Value true
opts.kernel? "linear" | "cosine" | "exponential" | "gaussian" | "tophat" | "epanechnikov" The kernel to use. Default Value 'gaussian'
opts.leaf_size? number Specify the leaf size of the underlying tree. See BallTree or KDTree for details. Default Value 40
opts.metric? string Metric to use for distance computation. See the documentation of scipy.spatial.distance and the metrics listed in distance\_metrics for valid metric values. Not all metrics are valid with all algorithms: refer to the documentation of BallTree and KDTree. Note that the normalization of the density output is correct only for the Euclidean distance metric. Default Value 'euclidean'
opts.metric_params? any Additional parameters to be passed to the tree for use with the metric. For more information, see the documentation of BallTree or KDTree.
opts.rtol? number The desired relative tolerance of the result. A larger tolerance will generally lead to faster execution. Default Value 0

Returns

KernelDensity

Defined in: generated/neighbors/KernelDensity.ts:23

Methods

dispose()

Disposes of the underlying Python resources.

Once dispose() is called, the instance is no longer usable.

Signature

dispose(): Promise<void>;

Returns

Promise<void>

Defined in: generated/neighbors/KernelDensity.ts:158

fit()

Fit the Kernel Density model on the data.

Signature

fit(opts: object): Promise<any>;

Parameters

Name Type Description
opts object -
opts.X? ArrayLike[] List of n_features-dimensional data points. Each row corresponds to a single data point.
opts.sample_weight? ArrayLike List of sample weights attached to the data X.
opts.y? any Ignored. This parameter exists only for compatibility with Pipeline.

Returns

Promise<any>

Defined in: generated/neighbors/KernelDensity.ts:175

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Signature

get_metadata_routing(opts: object): Promise<any>;

Parameters

Name Type Description
opts object -
opts.routing? any A MetadataRequest encapsulating routing information.

Returns

Promise<any>

Defined in: generated/neighbors/KernelDensity.ts:224

init()

Initializes the underlying Python resources.

This instance is not usable until the Promise returned by init() resolves.

Signature

init(py: PythonBridge): Promise<void>;

Parameters

Name Type
py PythonBridge

Returns

Promise<void>

Defined in: generated/neighbors/KernelDensity.ts:110

sample()

Generate random samples from the model.

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

Signature

sample(opts: object): Promise<ArrayLike[]>;

Parameters

Name Type Description
opts object -
opts.n_samples? number Number of samples to generate. Default Value 1
opts.random_state? number Determines random number generation used to generate random samples. Pass an int for reproducible results across multiple function calls. See Glossary.

Returns

Promise<ArrayLike[]>

Defined in: generated/neighbors/KernelDensity.ts:261

score()

Compute the total log-likelihood under the model.

Signature

score(opts: object): Promise<number>;

Parameters

Name Type Description
opts object -
opts.X? ArrayLike[] List of n_features-dimensional data points. Each row corresponds to a single data point.
opts.y? any Ignored. This parameter exists only for compatibility with Pipeline.

Returns

Promise<number>

Defined in: generated/neighbors/KernelDensity.ts:301

score_samples()

Compute the log-likelihood of each sample under the model.

Signature

score_samples(opts: object): Promise<ArrayLike>;

Parameters

Name Type Description
opts object -
opts.X? ArrayLike[] An array of points to query. Last dimension should match dimension of training data (n_features).

Returns

Promise<ArrayLike>

Defined in: generated/neighbors/KernelDensity.ts:339

set_fit_request()

Request metadata passed to the fit method.

Note that this method is only relevant if enable\_metadata\_routing=True (see sklearn.set\_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

Signature

set_fit_request(opts: object): Promise<any>;

Parameters

Name Type Description
opts object -
opts.sample_weight? string | boolean Metadata routing for sample\_weight parameter in fit.

Returns

Promise<any>

Defined in: generated/neighbors/KernelDensity.ts:376

Properties

_isDisposed

boolean = false

Defined in: generated/neighbors/KernelDensity.ts:21

_isInitialized

boolean = false

Defined in: generated/neighbors/KernelDensity.ts:20

_py

PythonBridge

Defined in: generated/neighbors/KernelDensity.ts:19

id

string

Defined in: generated/neighbors/KernelDensity.ts:16

opts

any

Defined in: generated/neighbors/KernelDensity.ts:17

Accessors

bandwidth_

Value of the bandwidth, given directly by the bandwidth parameter or estimated using the ‘scott’ or ‘silverman’ method.

Signature

bandwidth_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/neighbors/KernelDensity.ts:482

feature_names_in_

Names of features seen during fit. Defined only when X has feature names that are all strings.

Signature

feature_names_in_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/neighbors/KernelDensity.ts:457

n_features_in_

Number of features seen during fit.

Signature

n_features_in_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/neighbors/KernelDensity.ts:409

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/neighbors/KernelDensity.ts:97

Signature

py(pythonBridge: PythonBridge): void;

Parameters

Name Type
pythonBridge PythonBridge

Returns

void

Defined in: generated/neighbors/KernelDensity.ts:101

tree_

The tree algorithm for fast generalized N-point problems.

Signature

tree_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/neighbors/KernelDensity.ts:434