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.
Read more in the User Guide.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new MeanShift(opts?: object): MeanShift;Parameters
| Name | 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. Default Value false |
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. Default Value true |
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. Default Value 300 |
opts.min_bin_freq? | number | To speed up the algorithm, accept only those bins with at least min_bin_freq points as seeds. Default Value 1 |
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
Defined in: generated/cluster/MeanShift.ts:27 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/cluster/MeanShift.ts:25 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/cluster/MeanShift.ts:24 (opens in a new tab)
_py
PythonBridge
Defined in: generated/cluster/MeanShift.ts:23 (opens in a new tab)
id
string
Defined in: generated/cluster/MeanShift.ts:20 (opens in a new tab)
opts
any
Defined in: generated/cluster/MeanShift.ts:21 (opens in a new tab)
Accessors
cluster_centers_
Coordinates of cluster centers.
Signature
cluster_centers_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/cluster/MeanShift.ts:263 (opens in a new tab)
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/cluster/MeanShift.ts:359 (opens in a new tab)
labels_
Labels of each point.
Signature
labels_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/cluster/MeanShift.ts:288 (opens in a new tab)
n_features_in_
Number of features seen during fit.
Signature
n_features_in_(): Promise<number>;Returns
Promise<number>
Defined in: generated/cluster/MeanShift.ts:334 (opens in a new tab)
n_iter_
Maximum number of iterations performed on each seed.
Signature
n_iter_(): Promise<number>;Returns
Promise<number>
Defined in: generated/cluster/MeanShift.ts:311 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/cluster/MeanShift.ts:77 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/cluster/MeanShift.ts:81 (opens in a new tab)
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/cluster/MeanShift.ts:137 (opens in a new tab)
fit()
Perform clustering.
Signature
fit(opts: object): Promise<any>;Parameters
| Name | 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:154 (opens in a new tab)
fit_predict()
Perform clustering on X and returns cluster labels.
Signature
fit_predict(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Input data. |
opts.y? | any | Not used, present for API consistency by convention. |
Returns
Promise<ArrayLike>
Defined in: generated/cluster/MeanShift.ts:192 (opens in a new tab)
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/cluster/MeanShift.ts:90 (opens in a new tab)
predict()
Predict the closest cluster each sample in X belongs to.
Signature
predict(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | New data to predict. |
Returns
Promise<ArrayLike>
Defined in: generated/cluster/MeanShift.ts:230 (opens in a new tab)