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        "\n# Orthogonal Matching Pursuit\n\nUsing orthogonal matching pursuit for recovering a sparse signal from a noisy\nmeasurement encoded with a dictionary\n"
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        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.datasets import make_sparse_coded_signal\nfrom sklearn.linear_model import OrthogonalMatchingPursuit, OrthogonalMatchingPursuitCV\n\nn_components, n_features = 512, 100\nn_nonzero_coefs = 17\n\n# generate the data\n\n# y = Xw\n# |x|_0 = n_nonzero_coefs\n\ny, X, w = make_sparse_coded_signal(\n    n_samples=1,\n    n_components=n_components,\n    n_features=n_features,\n    n_nonzero_coefs=n_nonzero_coefs,\n    random_state=0,\n)\nX = X.T\n\n(idx,) = w.nonzero()\n\n# distort the clean signal\ny_noisy = y + 0.05 * np.random.randn(len(y))\n\n# plot the sparse signal\nplt.figure(figsize=(7, 7))\nplt.subplot(4, 1, 1)\nplt.xlim(0, 512)\nplt.title(\"Sparse signal\")\nplt.stem(idx, w[idx])\n\n# plot the noise-free reconstruction\nomp = OrthogonalMatchingPursuit(n_nonzero_coefs=n_nonzero_coefs)\nomp.fit(X, y)\ncoef = omp.coef_\n(idx_r,) = coef.nonzero()\nplt.subplot(4, 1, 2)\nplt.xlim(0, 512)\nplt.title(\"Recovered signal from noise-free measurements\")\nplt.stem(idx_r, coef[idx_r])\n\n# plot the noisy reconstruction\nomp.fit(X, y_noisy)\ncoef = omp.coef_\n(idx_r,) = coef.nonzero()\nplt.subplot(4, 1, 3)\nplt.xlim(0, 512)\nplt.title(\"Recovered signal from noisy measurements\")\nplt.stem(idx_r, coef[idx_r])\n\n# plot the noisy reconstruction with number of non-zeros set by CV\nomp_cv = OrthogonalMatchingPursuitCV()\nomp_cv.fit(X, y_noisy)\ncoef = omp_cv.coef_\n(idx_r,) = coef.nonzero()\nplt.subplot(4, 1, 4)\nplt.xlim(0, 512)\nplt.title(\"Recovered signal from noisy measurements with CV\")\nplt.stem(idx_r, coef[idx_r])\n\nplt.subplots_adjust(0.06, 0.04, 0.94, 0.90, 0.20, 0.38)\nplt.suptitle(\"Sparse signal recovery with Orthogonal Matching Pursuit\", fontsize=16)\nplt.show()"
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