mvpa2.datasets.sources.skl_data.skl_biclusters(shape, n_clusters, noise=0.0, minval=10, maxval=100, shuffle=True, random_state=None)

Generate an array with constant block diagonal structure for biclustering.


shape : iterable (n_rows, n_cols)

The shape of the result.

n_clusters : integer

The number of biclusters.

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.


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.

See also



This function has been auto-generated by wrapping make_biclusters() from the sklearn package. The documentation of this function has been kept verbatim. Consequently, the actual return value is not as described in the documentation, but the data is returned as a PyMVPA dataset.


[R22]Dhillon, I. S. (2001, August). Co-clustering documents and words using bipartite spectral graph partitioning. In Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 269-274). ACM.