Introduction to Sparse Modeling for Software Engineers

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1ZUIPODPEFFYBNQMF # Soft thresholding operator def soft_threshold(X, thresh): return np.where(np.abs(X)
<= thresh, 0, X - thresh * np.sign(X)) # Coordinate descent w_cd = np.zeros(n_features) for _ in range(n_iter): for j in range(n_features): w_cd[j] = 0.0 r_j = y - np.dot(X, w_cd) w_cd[j] = soft_threshold(np.dot(X[:, j], r_j) / n_samples, alpha)

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&Y-BTTPBOE-BTTP"%.. # Lasso from scikit-learn from sklearn.linear_model import Lasso model
= Lasso(alpha=0.1) model.fit(X_train, Y_train) model.score(X_test, y_test) # Lasso from spm-image from spmimage.linear_model import LassoADMM model = LassoADMM(alpha=0.1) model.fit(X_train, Y_train) model.score(X_test, y_test)

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1ZUIPODPEFFYBNQMF # extract patches patches = extract_simple_patches_2d(img, patch_size) # normalize
patches patches = patches.reshape(patches.shape[0], -1).astype(np.float64) intercept = np.mean(patches, axis=0) patches -= intercept patches /= np.std(patches, axis=0) # dictionary learning model = MiniBatchDictionaryLearning(n_components=n_basis, alpha=1, n_iter=n_iter, n_jobs=1) model.fit(patches) # reconstruction reconstructed_patches = np.dot(code, model.components_) reconstructed_patches = reconstructed_patches.reshape(len(patches), *patch_size) reconstructed = reconstruct_from_simple_patches_2d(reconstructed_patches, img.shape)

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Introduction to Sparse Modeling for Software Engineers

Introduction to Sparse Modeling for Software Engineers

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