Class: 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

new SpectralEmbedding()

new SpectralEmbedding(opts?): SpectralEmbedding

Parameters

Parameter	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.
`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`:
`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.
`opts.n_jobs`?	`number`	The number of parallel jobs to run. `undefined` means 1 unless in a `joblib.parallel_backend` 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

Properties

Property	Type	Default value	Defined in
`_isDisposed`	`boolean`	`false`	generated/manifold/SpectralEmbedding.ts:25
`_isInitialized`	`boolean`	`false`	generated/manifold/SpectralEmbedding.ts:24
`_py`	`PythonBridge`	`undefined`	generated/manifold/SpectralEmbedding.ts:23
`id`	`string`	`undefined`	generated/manifold/SpectralEmbedding.ts:20
`opts`	`any`	`undefined`	generated/manifold/SpectralEmbedding.ts:21

Accessors

affinity_matrix_

Get Signature

get affinity_matrix_(): Promise<ArrayLike[]>

Affinity_matrix constructed from samples or precomputed.

Returns Promise<ArrayLike[]>

Defined in generated/manifold/SpectralEmbedding.ts:302

embedding_

Get Signature

get embedding_(): Promise<ArrayLike[]>

Spectral embedding of the training matrix.

Returns Promise<ArrayLike[]>

Defined in generated/manifold/SpectralEmbedding.ts:275

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/SpectralEmbedding.ts:356

n_features_in_

Get Signature

get n_features_in_(): Promise<number>

Number of features seen during fit.

Returns Promise<number>

Defined in generated/manifold/SpectralEmbedding.ts:329

n_neighbors_

Get Signature

get n_neighbors_(): Promise<number>

Number of nearest neighbors effectively used.

Returns Promise<number>

Defined in generated/manifold/SpectralEmbedding.ts:383

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/SpectralEmbedding.ts:82

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/SpectralEmbedding.ts:136

fit()

fit(opts): Promise<any>

Fit the model from data in X.

Parameters

Parameter	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:153

fit_transform()

fit_transform(opts): Promise<ArrayLike[]>

Fit the model from data in X and transform X.

Parameters

Parameter	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.