Class: SpectralCoclustering

Spectral Co-Clustering algorithm (Dhillon, 2001).

Clusters rows and columns of an array X to solve the relaxed normalized cut of the bipartite graph created from X as follows: the edge between row vertex i and column vertex j has weight X\[i, j\].

The resulting bicluster structure is block-diagonal, since each row and each column belongs to exactly one bicluster.

Supports sparse matrices, as long as they are nonnegative.

Constructors

new SpectralCoclustering()

new SpectralCoclustering(opts?): SpectralCoclustering

Parameters

Parameter	Type	Description
`opts`?	`object`	-
`opts.init`?	`ArrayLike`[]	Method for initialization of k-means algorithm; defaults to ‘k-means++’.
`opts.mini_batch`?	`boolean`	Whether to use mini-batch k-means, which is faster but may get different results.
`opts.n_clusters`?	`number`	The number of biclusters to find.
`opts.n_init`?	`number`	Number of random initializations that are tried with the k-means algorithm. If mini-batch k-means is used, the best initialization is chosen and the algorithm runs once. Otherwise, the algorithm is run for each initialization and the best solution chosen.
`opts.n_svd_vecs`?	`number`	Number of vectors to use in calculating the SVD. Corresponds to `ncv` when `svd_method=arpack` and `n_oversamples` when `svd_method` is ‘randomized`.
`opts.random_state`?	`number`	Used for randomizing the singular value decomposition and the k-means initialization. Use an int to make the randomness deterministic. See Glossary.
`opts.svd_method`?	`"randomized"` \| `"arpack"`	Selects the algorithm for finding singular vectors. May be ‘randomized’ or ‘arpack’. If ‘randomized’, use `sklearn.utils.extmath.randomized_svd`, which may be faster for large matrices. If ‘arpack’, use `scipy.sparse.linalg.svds`, which is more accurate, but possibly slower in some cases.

Returns SpectralCoclustering

Defined in generated/cluster/SpectralCoclustering.ts:29

Properties

Property	Type	Default value	Defined in
`_isDisposed`	`boolean`	`false`	generated/cluster/SpectralCoclustering.ts:27
`_isInitialized`	`boolean`	`false`	generated/cluster/SpectralCoclustering.ts:26
`_py`	`PythonBridge`	`undefined`	generated/cluster/SpectralCoclustering.ts:25
`id`	`string`	`undefined`	generated/cluster/SpectralCoclustering.ts:22
`opts`	`any`	`undefined`	generated/cluster/SpectralCoclustering.ts:23

Accessors

column_labels_

Get Signature

get column_labels_(): Promise<ArrayLike>

The bicluster label of each column.

Returns Promise<ArrayLike>

Defined in generated/cluster/SpectralCoclustering.ts:427

columns_

Get Signature

get columns_(): Promise<ArrayLike[]>

Results of the clustering, like rows.

Returns Promise<ArrayLike[]>

Defined in generated/cluster/SpectralCoclustering.ts:373

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/cluster/SpectralCoclustering.ts:481

n_features_in_

Get Signature

get n_features_in_(): Promise<number>

Number of features seen during fit.

Returns Promise<number>

Defined in generated/cluster/SpectralCoclustering.ts:454

py

Get Signature

get py(): PythonBridge

Returns PythonBridge

Set Signature

set py(pythonBridge): void

Parameters

Parameter	Type
`pythonBridge`	`PythonBridge`

Returns void

Defined in generated/cluster/SpectralCoclustering.ts:81

row_labels_

Get Signature

get row_labels_(): Promise<ArrayLike>

The bicluster label of each row.

Returns Promise<ArrayLike>

Defined in generated/cluster/SpectralCoclustering.ts:400

rows_

Get Signature

get rows_(): Promise<ArrayLike[]>

Results of the clustering. rows\[i, r\] is true if cluster i contains row r. Available only after calling fit.

Returns Promise<ArrayLike[]>

Defined in generated/cluster/SpectralCoclustering.ts:346

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/cluster/SpectralCoclustering.ts:137

fit()

fit(opts): Promise<any>

Create a biclustering for X.

Parameters

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

Returns Promise<any>

Defined in generated/cluster/SpectralCoclustering.ts:154

get_indices()

get_indices(opts): Promise<ArrayLike>

Row and column indices of the i’th bicluster.

Only works if rows_ and columns_ attributes exist.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.i`?	`number`	The index of the cluster.

Returns Promise<ArrayLike>

Defined in generated/cluster/SpectralCoclustering.ts:195

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/cluster/SpectralCoclustering.ts:233

get_shape()

get_shape(opts): Promise<number>

Shape of the i’th bicluster.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.i`?	`number`	The index of the cluster.

Returns Promise<number>

Defined in generated/cluster/SpectralCoclustering.ts:269

get_submatrix()

get_submatrix(opts): Promise<ArrayLike[]>

Return the submatrix corresponding to bicluster i.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.data`?	`ArrayLike`[]	The data.
`opts.i`?	`number`	The index of the cluster.

Returns Promise<ArrayLike[]>

Defined in generated/cluster/SpectralCoclustering.ts:305

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/cluster/SpectralCoclustering.ts:94

SpectralClustering SpectralEmbedding