Class: MDS

Multidimensional scaling.

Constructors

new MDS()

new MDS(opts?): MDS

Parameters

Parameter	Type	Description
`opts`?	`object`	-
`opts.dissimilarity`?	`"euclidean"` \| `"precomputed"`	Dissimilarity measure to use:
`opts.eps`?	`number`	Relative tolerance with respect to stress at which to declare convergence. The value of `eps` should be tuned separately depending on whether or not `normalized_stress` is being used.
`opts.max_iter`?	`number`	Maximum number of iterations of the SMACOF algorithm for a single run.
`opts.metric`?	`boolean`	If `true`, perform metric MDS; otherwise, perform nonmetric MDS. When `false` (i.e. non-metric MDS), dissimilarities with 0 are considered as missing values.
`opts.n_components`?	`number`	Number of dimensions in which to immerse the dissimilarities.
`opts.n_init`?	`number`	Number of times the SMACOF algorithm will be run with different initializations. The final results will be the best output of the runs, determined by the run with the smallest final stress.
`opts.n_jobs`?	`number`	The number of jobs to use for the computation. If multiple initializations are used (`n_init`), each run of the algorithm is computed in parallel. `undefined` means 1 unless in a `joblib.parallel_backend` context. `\-1` means using all processors. See Glossary for more details.
`opts.normalized_stress`?	`boolean` \| `"auto” default=”auto"`	Whether use and return normed stress value (Stress-1) instead of raw stress calculated by default. Only supported in non-metric MDS.
`opts.random_state`?	`number`	Determines the random number generator used to initialize the centers. Pass an int for reproducible results across multiple function calls. See Glossary.
`opts.verbose`?	`number`	Level of verbosity.

Returns MDS

Defined in generated/manifold/MDS.ts:23

Properties

Property	Type	Default value	Defined in
`_isDisposed`	`boolean`	`false`	generated/manifold/MDS.ts:21
`_isInitialized`	`boolean`	`false`	generated/manifold/MDS.ts:20
`_py`	`PythonBridge`	`undefined`	generated/manifold/MDS.ts:19
`id`	`string`	`undefined`	generated/manifold/MDS.ts:16
`opts`	`any`	`undefined`	generated/manifold/MDS.ts:17

Accessors

dissimilarity_matrix_

Get Signature

get dissimilarity_matrix_(): Promise<ArrayLike[]>

Pairwise dissimilarities between the points. Symmetric matrix that:

Returns Promise<ArrayLike[]>

Defined in generated/manifold/MDS.ts:359

embedding_

Get Signature

get embedding_(): Promise<ArrayLike[]>

Stores the position of the dataset in the embedding space.

Returns Promise<ArrayLike[]>

Defined in generated/manifold/MDS.ts:315

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/manifold/MDS.ts:407

n_features_in_

Get Signature

get n_features_in_(): Promise<number>

Number of features seen during fit.

Returns Promise<number>

Defined in generated/manifold/MDS.ts:384

n_iter_

Get Signature

get n_iter_(): Promise<number>

The number of iterations corresponding to the best stress.

Returns Promise<number>

Defined in generated/manifold/MDS.ts:430

py

Get Signature

get py(): PythonBridge

Returns PythonBridge

Set Signature

set py(pythonBridge): void

Parameters

Parameter	Type
`pythonBridge`	`PythonBridge`

Returns void

Defined in generated/manifold/MDS.ts:94

stress_

Get Signature

get stress_(): Promise<number>

The final value of the stress (sum of squared distance of the disparities and the distances for all constrained points). If normalized_stress=True, and metric=False returns Stress-1. A value of 0 indicates “perfect” fit, 0.025 excellent, 0.05 good, 0.1 fair, and 0.2 poor [1].

Returns Promise<number>

Defined in generated/manifold/MDS.ts:337

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/manifold/MDS.ts:145

fit()

fit(opts): Promise<any>

Compute the position of the points in the embedding space.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.init`?	`ArrayLike`[]	Starting configuration of the embedding to initialize the SMACOF algorithm. By default, the algorithm is initialized with a randomly chosen array.
`opts.X`?	`ArrayLike`[]	Input data. If `dissimilarity=='precomputed'`, the input should be the dissimilarity matrix.
`opts.y`?	`any`	Not used, present for API consistency by convention.

Returns Promise<any>

Defined in generated/manifold/MDS.ts:162

fit_transform()

fit_transform(opts): Promise<ArrayLike[]>

Fit the data from X, and returns the embedded coordinates.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.init`?	`ArrayLike`[]	Starting configuration of the embedding to initialize the SMACOF algorithm. By default, the algorithm is initialized with a randomly chosen array.
`opts.X`?	`ArrayLike`[]	Input data. If `dissimilarity=='precomputed'`, the input should be the dissimilarity matrix.
`opts.y`?	`any`	Not used, present for API consistency by convention.

Returns Promise<ArrayLike[]>

Defined in generated/manifold/MDS.ts:203

get_metadata_routing()

get_metadata_routing(opts): Promise<any>

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Parameters

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

Returns Promise<any>

Defined in generated/manifold/MDS.ts:247

init()

init(py): Promise<void>

Initializes the underlying Python resources.

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

Parameters

Parameter	Type
`py`	`PythonBridge`

Returns Promise<void>

Defined in generated/manifold/MDS.ts:107

set_fit_request()

set_fit_request(opts): Promise<any>

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:

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.init`?	`string` \| `boolean`	Metadata routing for `init` parameter in `fit`.

Returns Promise<any>

Defined in generated/manifold/MDS.ts:283

MaxAbsScaler MeanShift