Class: MeanShift

Mean shift clustering using a flat kernel.

Mean shift clustering aims to discover “blobs” in a smooth density of samples. It is a centroid-based algorithm, which works by updating candidates for centroids to be the mean of the points within a given region. These candidates are then filtered in a post-processing stage to eliminate near-duplicates to form the final set of centroids.

Seeding is performed using a binning technique for scalability.

For an example of how to use MeanShift clustering, refer to: A demo of the mean-shift clustering algorithm.

Constructors

new MeanShift()

new MeanShift(opts?): MeanShift

Parameters

Parameter	Type	Description
`opts`?	`object`	-
`opts.bandwidth`?	`number`	Bandwidth used in the flat kernel. If not given, the bandwidth is estimated using sklearn.cluster.estimate_bandwidth; see the documentation for that function for hints on scalability (see also the Notes, below).
`opts.bin_seeding`?	`boolean`	If true, initial kernel locations are not locations of all points, but rather the location of the discretized version of points, where points are binned onto a grid whose coarseness corresponds to the bandwidth. Setting this option to `true` will speed up the algorithm because fewer seeds will be initialized. The default value is `false`. Ignored if seeds argument is not `undefined`.
`opts.cluster_all`?	`boolean`	If true, then all points are clustered, even those orphans that are not within any kernel. Orphans are assigned to the nearest kernel. If false, then orphans are given cluster label -1.
`opts.max_iter`?	`number`	Maximum number of iterations, per seed point before the clustering operation terminates (for that seed point), if has not converged yet.
`opts.min_bin_freq`?	`number`	To speed up the algorithm, accept only those bins with at least min_bin_freq points as seeds.
`opts.n_jobs`?	`number`	The number of jobs to use for the computation. The following tasks benefit from the parallelization:
`opts.seeds`?	`ArrayLike`[]	Seeds used to initialize kernels. If not set, the seeds are calculated by clustering.get_bin_seeds with bandwidth as the grid size and default values for other parameters.

Returns MeanShift

Defined in generated/cluster/MeanShift.ts:29

Properties

Property	Type	Default value	Defined in
`_isDisposed`	`boolean`	`false`	generated/cluster/MeanShift.ts:27
`_isInitialized`	`boolean`	`false`	generated/cluster/MeanShift.ts:26
`_py`	`PythonBridge`	`undefined`	generated/cluster/MeanShift.ts:25
`id`	`string`	`undefined`	generated/cluster/MeanShift.ts:22
`opts`	`any`	`undefined`	generated/cluster/MeanShift.ts:23

Accessors

cluster_centers_

Get Signature

get cluster_centers_(): Promise<ArrayLike[]>

Coordinates of cluster centers.

Returns Promise<ArrayLike[]>

Defined in generated/cluster/MeanShift.ts:294

feature_names_in_

Get Signature

get feature_names_in_(): Promise<ArrayLike>

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

Returns Promise<ArrayLike>

Defined in generated/cluster/MeanShift.ts:390

labels_

Get Signature

get labels_(): Promise<ArrayLike>

Labels of each point.

Returns Promise<ArrayLike>

Defined in generated/cluster/MeanShift.ts:319

n_features_in_

Get Signature

get n_features_in_(): Promise<number>

Number of features seen during fit.

Returns Promise<number>

Defined in generated/cluster/MeanShift.ts:365

n_iter_

Get Signature

get n_iter_(): Promise<number>

Maximum number of iterations performed on each seed.

Returns Promise<number>

Defined in generated/cluster/MeanShift.ts:342

py

Get Signature

get py(): PythonBridge

Returns PythonBridge

Set Signature

set py(pythonBridge): void

Parameters

Parameter	Type
`pythonBridge`	`PythonBridge`

Returns void

Defined in generated/cluster/MeanShift.ts:79

Methods

dispose()

dispose(): Promise<void>

Disposes of the underlying Python resources.

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

Returns Promise<void>

Defined in generated/cluster/MeanShift.ts:130

fit()

fit(opts): Promise<any>

Perform clustering.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`ArrayLike`[]	Samples to cluster.
`opts.y`?	`any`	Not used, present for API consistency by convention.

Returns Promise<any>

Defined in generated/cluster/MeanShift.ts:147

fit_predict()

fit_predict(opts): Promise<ArrayLike>

Perform clustering on X and returns cluster labels.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.kwargs`?	`any`	Arguments to be passed to `fit`.
`opts.X`?	`ArrayLike`[]	Input data.
`opts.y`?	`any`	Not used, present for API consistency by convention.