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        "\n# A demo of the Spectral Co-Clustering algorithm\n\nThis example demonstrates how to generate a dataset and bicluster it\nusing the Spectral Co-Clustering algorithm.\n\nThe dataset is generated using the ``make_biclusters`` function, which\ncreates a matrix of small values and implants bicluster with large\nvalues. The rows and columns are then shuffled and passed to the\nSpectral Co-Clustering algorithm. Rearranging the shuffled matrix to\nmake biclusters contiguous shows how accurately the algorithm found\nthe biclusters.\n"
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        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport numpy as np\nfrom matplotlib import pyplot as plt\n\nfrom sklearn.cluster import SpectralCoclustering\nfrom sklearn.datasets import make_biclusters\nfrom sklearn.metrics import consensus_score\n\ndata, rows, columns = make_biclusters(\n    shape=(300, 300), n_clusters=5, noise=5, shuffle=False, random_state=0\n)\n\nplt.matshow(data, cmap=plt.cm.Blues)\nplt.title(\"Original dataset\")\n\n# shuffle clusters\nrng = np.random.RandomState(0)\nrow_idx = rng.permutation(data.shape[0])\ncol_idx = rng.permutation(data.shape[1])\ndata = data[row_idx][:, col_idx]\n\nplt.matshow(data, cmap=plt.cm.Blues)\nplt.title(\"Shuffled dataset\")\n\nmodel = SpectralCoclustering(n_clusters=5, random_state=0)\nmodel.fit(data)\nscore = consensus_score(model.biclusters_, (rows[:, row_idx], columns[:, col_idx]))\n\nprint(\"consensus score: {:.3f}\".format(score))\n\nfit_data = data[np.argsort(model.row_labels_)]\nfit_data = fit_data[:, np.argsort(model.column_labels_)]\n\nplt.matshow(fit_data, cmap=plt.cm.Blues)\nplt.title(\"After biclustering; rearranged to show biclusters\")\n\nplt.show()"
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