LocallyLinearEmbedding

Locally Linear Embedding.

Constructors

constructor()

Signature

new LocallyLinearEmbedding(opts?: object): LocallyLinearEmbedding;

Parameters

Name	Type	Description
`opts?`	`object`	-
`opts.eigen_solver?`	`"auto"` \| `"arpack"` \| `"dense"`	The solver used to compute the eigenvectors. The available options are: `Default Value` `'auto'`
`opts.hessian_tol?`	`number`	Tolerance for Hessian eigenmapping method. Only used if `method \== 'hessian'`. `Default Value` `0.0001`
`opts.max_iter?`	`number`	Maximum number of iterations for the arpack solver. Not used if eigen_solver==’dense’. `Default Value` `100`
`opts.method?`	`"standard"` \| `"hessian"` \| `"modified"` \| `"ltsa"`	`standard`: use the standard locally linear embedding algorithm. see reference [1] `Default Value` `'standard'`
`opts.modified_tol?`	`number`	Tolerance for modified LLE method. Only used if `method \== 'modified'`. `Default Value` `1e-12`
`opts.n_components?`	`number`	Number of coordinates for the manifold. `Default Value` `2`
`opts.n_jobs?`	`number`	The number of parallel jobs to run. `undefined` means 1 unless in a `joblib.parallel\_backend` (opens in a new tab) context. `\-1` means using all processors. See Glossary for more details.
`opts.n_neighbors?`	`number`	Number of neighbors to consider for each point. `Default Value` `5`
`opts.neighbors_algorithm?`	`"auto"` \| `"ball_tree"` \| `"kd_tree"` \| `"brute"`	Algorithm to use for nearest neighbors search, passed to `NearestNeighbors` instance. `Default Value` `'auto'`
`opts.random_state?`	`number`	Determines the random number generator when `eigen\_solver` == ‘arpack’. Pass an int for reproducible results across multiple function calls. See Glossary.
`opts.reg?`	`number`	Regularization constant, multiplies the trace of the local covariance matrix of the distances. `Default Value` `0.001`
`opts.tol?`	`number`	Tolerance for ‘arpack’ method Not used if eigen_solver==’dense’. `Default Value` `0.000001`

Returns

LocallyLinearEmbedding

Defined in: generated/manifold/LocallyLinearEmbedding.ts:23 (opens in a new tab)

Properties

_isDisposed

boolean = false

Defined in: generated/manifold/LocallyLinearEmbedding.ts:21 (opens in a new tab)

_isInitialized

boolean = false

Defined in: generated/manifold/LocallyLinearEmbedding.ts:20 (opens in a new tab)

_py

PythonBridge

Defined in: generated/manifold/LocallyLinearEmbedding.ts:19 (opens in a new tab)

id

string

Defined in: generated/manifold/LocallyLinearEmbedding.ts:16 (opens in a new tab)

opts

any

Defined in: generated/manifold/LocallyLinearEmbedding.ts:17 (opens in a new tab)

Accessors

embedding_

Stores the embedding vectors

Signature

embedding_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/manifold/LocallyLinearEmbedding.ts:395 (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/manifold/LocallyLinearEmbedding.ts:476 (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/manifold/LocallyLinearEmbedding.ts:449 (opens in a new tab)

nbrs_

Stores nearest neighbors instance, including BallTree or KDtree if applicable.

Signature

nbrs_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/manifold/LocallyLinearEmbedding.ts:503 (opens in a new tab)

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/manifold/LocallyLinearEmbedding.ts:108 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

Name	Type
`pythonBridge`	`PythonBridge`

Returns

void

Defined in: generated/manifold/LocallyLinearEmbedding.ts:112 (opens in a new tab)

reconstruction_error_

Reconstruction error associated with embedding\_

Signature

reconstruction_error_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/manifold/LocallyLinearEmbedding.ts:422 (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/manifold/LocallyLinearEmbedding.ts:179 (opens in a new tab)

fit()

Compute the embedding vectors for data X.

Signature

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

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`[]	Training set.
`opts.y?`	`any`	Not used, present here for API consistency by convention.

Returns

Promise<any>

Defined in: generated/manifold/LocallyLinearEmbedding.ts:196 (opens in a new tab)

fit_transform()

Compute the embedding vectors for data X and transform X.

Signature

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

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`[]	Training set.
`opts.y?`	`any`	Not used, present here for API consistency by convention.

Returns

Promise<ArrayLike>

Defined in: generated/manifold/LocallyLinearEmbedding.ts:236 (opens in a new tab)

get_feature_names_out()

Get output feature names for transformation.

The feature names out will prefixed by the lowercased class name. For example, if the transformer outputs 3 features, then the feature names out are: \["class\_name0", "class\_name1", "class\_name2"\].

Signature

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

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.input_features?`	`any`	Only used to validate feature names with the names seen in `fit`.

Returns

Promise<any>

Defined in: generated/manifold/LocallyLinearEmbedding.ts:281 (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/manifold/LocallyLinearEmbedding.ts:121 (opens in a new tab)

set_output()

Set output container.

See Introducing the set_output API for an example on how to use the API.

Signature

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

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.transform?`	`"default"` \| `"pandas"`	Configure output of `transform` and `fit\_transform`.

Returns

Promise<any>

Defined in: generated/manifold/LocallyLinearEmbedding.ts:321 (opens in a new tab)

transform()

Transform new points into embedding space.

Signature

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

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`[]	Training set.

Returns

Promise<ArrayLike[]>

Defined in: generated/manifold/LocallyLinearEmbedding.ts:358 (opens in a new tab)

LinearSVR LocalOutlierFactor