SpectralCoclustering: Performs clustering according to the spectral coclustering...

In rgudhi: An Interface to the GUDHI Library for Topological Data Analysis

Performs clustering according to the spectral coclustering algorithm

Description

This is a wrapper around the Python class sklearn.cluster.SpectralCoclustering.

Super classes

rgudhi::PythonClass -> rgudhi::SKLearnClass -> rgudhi::BaseClustering -> SpectralCoclustering

Methods

Public methods

• SpectralCoclustering$new()

• SpectralCoclustering$clone()

Inherited methods

• rgudhi::PythonClass$get_python_class()
• rgudhi::PythonClass$set_python_class()
• rgudhi::SKLearnClass$apply()
• rgudhi::SKLearnClass$fit()
• rgudhi::SKLearnClass$fit_transform()
• rgudhi::SKLearnClass$get_params()
• rgudhi::SKLearnClass$set_params()
• rgudhi::SKLearnClass$transform()

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Method new()

The SpectralCoclustering class constructor.

Usage

SpectralCoclustering$new(
  n_clusters = 3L,
  svd_method = c("randomized", "arpack"),
  n_svd_vecs = NULL,
  mini_batch = FALSE,
  init = c("k-means++", "random"),
  n_init = 10L,
  random_state = NULL
)

Arguments

n_clusters

An integer value specifying the number of biclusters to find. Defaults to 3L.

svd_method

A string specifying the algorithm for finding singular vectors. May be "randomized" or "arpack". If "randomized", uses sklearn.utils.extmath.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. Defaults to "randomized".

n_svd_vecs

An integer value specifying the number of vectors to use in calculating the SVD. Corresponds to ncv when svd_method == "arpack" and n_oversamples when svd_method == "randomized". Defaults to NULL.

mini_batch

A boolean value specifying whether to use mini-batch k-means, which is faster but may get different results. Defaults to FALSE.

init

A string specifying the method for initialization of k-means algorithm. Choices are "k-means++" or "random". Defaults to "k-means++".

n_init

An integer value specifying the 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. Defaults to 10L.

random_state

An integer value specifying a pseudo random number generator used for the initialization of the lobpcg eigenvectors decomposition when eigen_solver == "amg", and for the k-means initialization. Defaults to NULL which uses clock time.

Returns

An object of class SpectralCoclustering.

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Method clone()

The objects of this class are cloneable with this method.

Usage

SpectralCoclustering$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

cl <- SpectralCoclustering$new()

rgudhi documentation built on March 31, 2023, 11:38 p.m.