66 lines
1.7 KiB
Python
66 lines
1.7 KiB
Python
"""
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===========================
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Orthogonal Matching Pursuit
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===========================
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Using orthogonal matching pursuit for recovering a sparse signal from a noisy
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measurement encoded with a dictionary
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"""
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print __doc__
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import pylab as pl
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import numpy as np
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from sklearn.linear_model import orthogonal_mp
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from sklearn.datasets import make_sparse_coded_signal
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n_components, n_features = 512, 100
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n_nonzero_coefs = 17
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# generate the data
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###################
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# y = Dx
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# |x|_0 = n_nonzero_coefs
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y, D, x = make_sparse_coded_signal(n_samples=1,
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n_components=n_components,
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n_features=n_features,
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n_nonzero_coefs=n_nonzero_coefs,
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random_state=0)
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idx, = x.nonzero()
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# distort the clean signal
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##########################
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y_noisy = y + 0.05 * np.random.randn(len(y))
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# plot the sparse signal
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########################
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pl.subplot(3, 1, 1)
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pl.xlim(0, 512)
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pl.title("Sparse signal")
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pl.stem(idx, x[idx])
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# plot the noise-free reconstruction
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####################################
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x_r = orthogonal_mp(D, y, n_nonzero_coefs)
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idx_r, = x_r.nonzero()
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pl.subplot(3, 1, 2)
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pl.xlim(0, 512)
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pl.title("Recovered signal from noise-free measurements")
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pl.stem(idx_r, x_r[idx_r])
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# plot the noisy reconstruction
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###############################
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x_r = orthogonal_mp(D, y_noisy, n_nonzero_coefs)
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idx_r, = x_r.nonzero()
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pl.subplot(3, 1, 3)
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pl.xlim(0, 512)
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pl.title("Recovered signal from noisy measurements")
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pl.stem(idx_r, x_r[idx_r])
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pl.subplots_adjust(0.06, 0.04, 0.94, 0.90, 0.20, 0.38)
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pl.suptitle('Sparse signal recovery with Orthogonal Matching Pursuit',
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fontsize=16)
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pl.show()
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