sklearn.datasets.make_checkerboard

sklearn.datasets.make_checkerboard(shape, n_clusters, noise=0.0, minval=10, maxval=100, shuffle=True, random_state=None)

Generate an array with block checkerboard structure for biclustering.

Parameters:

shape : iterable (n_rows, n_cols)

The shape of the result.

n_clusters : integer or iterable (n_row_clusters, n_column_clusters)

The number of row and column clusters.

noise : float, optional (default=0.0)

The standard deviation of the gaussian noise.

minval : int, optional (default=10)

Minimum value of a bicluster.

maxval : int, optional (default=100)

Maximum value of a bicluster.

shuffle : boolean, optional (default=True)

Shuffle the samples.

random_state : int, RandomState instance or None, optional (default=None)

If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

Returns:

X : array of shape shape

The generated array.

rows : array of shape (n_clusters, X.shape[0],)

The indicators for cluster membership of each row.

cols : array of shape (n_clusters, X.shape[1],)

The indicators for cluster membership of each column.

References

[R104]

Kluger, Y., Basri, R., Chang, J. T., & Gerstein, M. (2003). Spectral biclustering of microarray data: coclustering genes and conditions. Genome research, 13(4), 703-716.