sklearn.datasets.make_checkerboard()

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

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

[R135]

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.