SpectralEmbedding

Spectral embedding for non-linear dimensionality reduction.

Forms an affinity matrix given by the specified function and applies spectral decomposition to the corresponding graph laplacian. The resulting transformation is given by the value of the eigenvectors for each data point.

Note : Laplacian Eigenmaps is the actual algorithm implemented here.

Constructors

constructor()

Signature

new SpectralEmbedding(opts?: object): SpectralEmbedding;

Parameters

Name	Type	Description
`opts?`	`object`	-
`opts.affinity?`	`"precomputed"` \| `"rbf"` \| `"nearest_neighbors"` \| `"precomputed_nearest_neighbors"`	‘nearest_neighbors’ : construct the affinity matrix by computing a graph of nearest neighbors. `Default Value` `'nearest_neighbors'`
`opts.eigen_solver?`	`"arpack"` \| `"lobpcg"` \| `"amg"`	The eigenvalue decomposition strategy to use. AMG requires pyamg to be installed. It can be faster on very large, sparse problems. If `undefined`, then `'arpack'` is used.
`opts.eigen_tol?`	`number`	Stopping criterion for eigendecomposition of the Laplacian matrix. If `eigen\_tol="auto"` then the passed tolerance will depend on the `eigen\_solver`: `Default Value` `'auto'`
`opts.gamma?`	`number`	Kernel coefficient for rbf kernel. If `undefined`, gamma will be set to 1/n_features.
`opts.n_components?`	`number`	The dimension of the projected subspace. `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 nearest neighbors for nearest_neighbors graph building. If `undefined`, n_neighbors will be set to max(n_samples/10, 1).
`opts.random_state?`	`number`	A pseudo random number generator used for the initialization of the lobpcg eigen vectors decomposition when `eigen\_solver \== 'amg'`, and for the K-Means initialization. Use an int to make the results deterministic across calls (See Glossary).

Returns

SpectralEmbedding

Defined in: generated/manifold/SpectralEmbedding.ts:27 (opens in a new tab)

Properties

_isDisposed

boolean = false

Defined in: generated/manifold/SpectralEmbedding.ts:25 (opens in a new tab)

_isInitialized

boolean = false

Defined in: generated/manifold/SpectralEmbedding.ts:24 (opens in a new tab)

_py

PythonBridge

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

id

string

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

opts

any

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

Accessors

affinity_matrix_

Affinity_matrix constructed from samples or precomputed.

Signature

affinity_matrix_(): Promise<ArrayLike[]>;

Returns

Promise<ArrayLike[]>

Defined in: generated/manifold/SpectralEmbedding.ts:275 (opens in a new tab)

embedding_

Spectral embedding of the training matrix.

Signature

embedding_(): Promise<ArrayLike[]>;

Returns

Promise<ArrayLike[]>

Defined in: generated/manifold/SpectralEmbedding.ts:248 (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/SpectralEmbedding.ts:329 (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/SpectralEmbedding.ts:302 (opens in a new tab)

n_neighbors_

Number of nearest neighbors effectively used.

Signature

n_neighbors_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/manifold/SpectralEmbedding.ts:356 (opens in a new tab)

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/manifold/SpectralEmbedding.ts:82 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

Name	Type
`pythonBridge`	`PythonBridge`

Returns

void

Defined in: generated/manifold/SpectralEmbedding.ts:86 (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/SpectralEmbedding.ts:145 (opens in a new tab)

fit()

Fit the model from data in X.

Signature

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

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`	Training vector, where `n\_samples` is the number of samples and `n\_features` is the number of features. If affinity is “precomputed” X : {array-like, sparse matrix}, shape (n_samples, n_samples), Interpret X as precomputed adjacency graph computed from samples.
`opts.y?`	`any`	Not used, present for API consistency by convention.

Returns

Promise<any>

Defined in: generated/manifold/SpectralEmbedding.ts:162 (opens in a new tab)

fit_transform()

Fit the model from data in X and transform X.

Signature

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

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`	Training vector, where `n\_samples` is the number of samples and `n\_features` is the number of features. If affinity is “precomputed” X : {array-like, sparse matrix} of shape (n_samples, n_samples), Interpret X as precomputed adjacency graph computed from samples.
`opts.y?`	`any`	Not used, present for API consistency by convention.

Returns

Promise<ArrayLike[]>

Defined in: generated/manifold/SpectralEmbedding.ts:204 (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/SpectralEmbedding.ts:95 (opens in a new tab)

SpectralCoclustering SplineTransformer