coclust.coclustering.CoclustSpecMod
¶
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class
coclust.coclustering.
CoclustSpecMod
(n_clusters=2, max_iter=20, tol=1e-09, n_init=1, random_state=None)[source]¶ Co-clustering by spectral approximation of the modularity matrix.
Parameters: - n_clusters (int, optional, default: 2) – Number of co-clusters to form
- max_iter (int, optional, default: 20) – Maximum number of iterations
- n_init (int, optional, default: 10) – Number of time the k-means algorithm will be run with different centroid seeds. The final results will be the best output of n_init consecutive runs in terms of inertia.
- random_state (integer or numpy.RandomState, optional) – The generator used to initialize the centers. If an integer is given, it fixes the seed. Defaults to the global numpy random number generator.
- tol (float, default: 1e-9) – Relative tolerance with regards to criterion to declare convergence
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row_labels_
¶ array-like, shape (n_rows,) – Bicluster label of each row
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column_labels_
¶ array-like, shape (n_cols,) – Bicluster label of each column
References
- Labiod L., Nadif M., ICONIP‘11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II Pages 700-708
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fit
(X, y=None)[source]¶ Perform co-clustering by spectral approximation of the modularity matrix
Parameters: X (numpy array or scipy sparse matrix, shape=(n_samples, n_features)) – Matrix to be analyzed
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get_indices
(i)¶ Give the row and column indices of the i’th co-cluster.
Parameters: i (integer) – Index of the co-cluster Returns: (row indices, column indices) Return type: (list, list)
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get_params
(deep=True)¶ Get parameters for this estimator.
Parameters: deep (boolean, optional) – If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns: params – Parameter names mapped to their values. Return type: mapping of string to any
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get_shape
(i)¶ Give the shape of the i’th co-cluster.
Parameters: i (integer) – Index of the co-cluster Returns: (number of rows, number of columns) Return type: (int, int)
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get_submatrix
(m, i)¶ Give the submatrix corresponding to co-cluster i.
Parameters: - m (X : numpy array or scipy sparse matrix) – Matrix from which the block has to be extracted
- i (integer) – index of the co-cluster
Returns: Submatrix corresponding to co-cluster i
Return type: numpy array or scipy sparse matrix
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set_params
(**params)¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form
<component>__<parameter>
so that it’s possible to update each component of a nested object.Returns: Return type: self