File Content: plot_omp.py """ =========================== Orthogonal Matching Pursuit =========================== Using orthogonal matching pursuit for recovering a sparse signal from a noisy measurement encoded with a dictionary """ print(__doc__) import matplotlib.pyplot as plt import numpy as np from sklearn.linear_model import OrthogonalMatchingPursuit from sklearn.linear_model import OrthogonalMatchingPursuitCV from sklearn.datasets import make_sparse_coded_signal n_components, n_features = 512, 100 n_nonzero_coefs = 17 # generate the data ################### # y = Xw # |x|_0 = n_nonzero_coefs y, X, w = make_sparse_coded_signal(n_samples=1, n_components=n_components, n_features=n_features, n_nonzero_coefs=n_nonzero_coefs, random_state=0) idx, = w.nonzero() # distort the clean signal ########################## y_noisy = y + 0.05 * np.random.randn(len(y)) # plot the sparse signal ######################## plt.figure(figsize=(7, 7)) plt.subplot(4, 1, 1) plt.xlim(0, 512) plt.title("Sparse signal") plt.stem(idx, w[idx]) # plot the noise-free reconstruction #################################### omp = OrthogonalMatchingPursuit(n_nonzero_coefs=n_nonzero_coefs) omp.fit(X, y) coef = omp.coef_ idx_r, = coef.nonzero() plt.subplot(4, 1, 2) plt.xlim(0, 512) plt.title("Recovered signal from noise-free measurements") plt.stem(idx_r, coef[idx_r]) # plot the noisy reconstruction ############################### omp.fit(X, y_noisy) coef = omp.coef_ idx_r, = coef.nonzero() plt.subplot(4, 1, 3) plt.xlim(0, 512) plt.title("Recovered signal from noisy measurements") plt.stem(idx_r, coef[idx_r]) # plot the noisy reconstruction with number of non-zeros set by CV ################################################################## omp_cv = OrthogonalMatchingPursuitCV() omp_cv.fit(X, y_noisy) coef = omp_cv.coef_ idx_r, = coef.nonzero() plt.subplot(4, 1, 4) plt.xlim(0, 512) plt.title("Recovered signal from noisy measurements with CV") plt.stem(idx_r, coef[idx_r]) plt.subplots_adjust(0.06, 0.04, 0.94, 0.90, 0.20, 0.38) plt.suptitle('Sparse signal recovery with Orthogonal Matching Pursuit', fontsize=16) plt.show() Submit