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Classes
SpectralBiclustering

SpectralBiclustering

Spectral biclustering (Kluger, 2003).

Partitions rows and columns under the assumption that the data has an underlying checkerboard structure. For instance, if there are two row partitions and three column partitions, each row will belong to three biclusters, and each column will belong to two biclusters. The outer product of the corresponding row and column label vectors gives this checkerboard structure.

Read more in the User Guide.

Python Reference (opens in a new tab)

Constructors

constructor()

Signature

new SpectralBiclustering(opts?: object): SpectralBiclustering;

Parameters

NameTypeDescription
opts?object-
opts.init?ArrayLike[] | "k-means++" | "random"Method for initialization of k-means algorithm; defaults to ‘k-means++’. Default Value 'k-means++'
opts.method?"bistochastic" | "scale" | "log"Method of normalizing and converting singular vectors into biclusters. May be one of ‘scale’, ‘bistochastic’, or ‘log’. The authors recommend using ‘log’. If the data is sparse, however, log normalization will not work, which is why the default is ‘bistochastic’. Default Value 'bistochastic'
opts.mini_batch?booleanWhether to use mini-batch k-means, which is faster but may get different results. Default Value false
opts.n_best?numberNumber of best singular vectors to which to project the data for clustering. Default Value 3
opts.n_clusters?numberThe number of row and column clusters in the checkerboard structure. Default Value 3
opts.n_components?numberNumber of singular vectors to check. Default Value 6
opts.n_init?numberNumber 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. Default Value 10
opts.n_svd_vecs?numberNumber 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?numberUsed 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’, uses randomized\_svd, which may be faster for large matrices. If ‘arpack’, uses scipy.sparse.linalg.svds, which is more accurate, but possibly slower in some cases. Default Value 'randomized'

Returns

SpectralBiclustering

Defined in: generated/cluster/SpectralBiclustering.ts:25 (opens in a new tab)

Properties

_isDisposed

boolean = false

Defined in: generated/cluster/SpectralBiclustering.ts:23 (opens in a new tab)

_isInitialized

boolean = false

Defined in: generated/cluster/SpectralBiclustering.ts:22 (opens in a new tab)

_py

PythonBridge

Defined in: generated/cluster/SpectralBiclustering.ts:21 (opens in a new tab)

id

string

Defined in: generated/cluster/SpectralBiclustering.ts:18 (opens in a new tab)

opts

any

Defined in: generated/cluster/SpectralBiclustering.ts:19 (opens in a new tab)

Accessors

column_labels_

Column partition labels.

Signature

column_labels_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/cluster/SpectralBiclustering.ts:423 (opens in a new tab)

columns_

Results of the clustering, like rows.

Signature

columns_(): Promise<ArrayLike[]>;

Returns

Promise<ArrayLike[]>

Defined in: generated/cluster/SpectralBiclustering.ts:369 (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/cluster/SpectralBiclustering.ts:477 (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/cluster/SpectralBiclustering.ts:450 (opens in a new tab)

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/cluster/SpectralBiclustering.ts:98 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

NameType
pythonBridgePythonBridge

Returns

void

Defined in: generated/cluster/SpectralBiclustering.ts:102 (opens in a new tab)

row_labels_

Row partition labels.

Signature

row_labels_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/cluster/SpectralBiclustering.ts:396 (opens in a new tab)

rows_

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

Signature

rows_(): Promise<ArrayLike[]>;

Returns

Promise<ArrayLike[]>

Defined in: generated/cluster/SpectralBiclustering.ts:342 (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/cluster/SpectralBiclustering.ts:165 (opens in a new tab)

fit()

Create a biclustering for X.

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLike[]Training data.
opts.y?anyNot used, present for API consistency by convention.

Returns

Promise<any>

Defined in: generated/cluster/SpectralBiclustering.ts:182 (opens in a new tab)

get_indices()

Row and column indices of the i’th bicluster.

Only works if rows\_ and columns\_ attributes exist.

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.i?numberThe index of the cluster.

Returns

Promise<ArrayLike>

Defined in: generated/cluster/SpectralBiclustering.ts:224 (opens in a new tab)

get_shape()

Shape of the i’th bicluster.

Signature

get_shape(opts: object): Promise<number>;

Parameters

NameTypeDescription
optsobject-
opts.i?numberThe index of the cluster.

Returns

Promise<number>

Defined in: generated/cluster/SpectralBiclustering.ts:261 (opens in a new tab)

get_submatrix()

Return the submatrix corresponding to bicluster i.

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.data?ArrayLike[]The data.
opts.i?numberThe index of the cluster.

Returns

Promise<ArrayLike[]>

Defined in: generated/cluster/SpectralBiclustering.ts:298 (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

NameType
pyPythonBridge

Returns

Promise<void>

Defined in: generated/cluster/SpectralBiclustering.ts:111 (opens in a new tab)