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reconstruct_from_patches_2d — scikit-learn 1.7....
patch_size = ( 10 , 10 )) >>> print ( 'Patches shape: {} ' . format (...( image_patches . shape )) Patches shape: (263758, 10, 10, 3)...scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.image.reconstruct_from_patch... -
make_sparse_coded_signal — scikit-learn 1.7.2 d...
= 50 , ... n_components = 100 , ... n_features = 10 , ... n_nonzero_coefs...= 4 , ... random_state = 0 ... ) >>> data . shape (50, 10) >>>...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_sparse_coded_signal.html -
plot_kmeans_digits.py
{n_features}") # %% # Define our evaluation benchmark # ---------- # #...# %% # Run the benchmark # ---------- # # We will compare three...scikit-learn.org/stable/_downloads/5a87b25ba023ee709595b8d02049f021/plot_kmeans_digits.py -
load_digits — scikit-learn 1.7.2 documentation
cmap = "gray" ) <...> >>> plt . show () Gallery examples # Recognizing...load_digits () >>> print ( digits . data . shape ) (1797, 64) >>> import...scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html -
locally_linear_embedding — scikit-learn 1.7.2 d...
solver. method {‘standard’, ‘hessian’, ‘modified’, ‘ltsa’}, default=’standard’...distances. eigen_solver {‘auto’, ‘arpack’, ‘dense’}, default=’auto’...scikit-learn.org/stable/modules/generated/sklearn.manifold.locally_linear_embedding.html -
cosine_similarity — scikit-learn 1.7.2 document...
= [[ 0 , 0 , 0 ], [ 1 , 1 , 1 ]] >>> Y = [[ 1 , 0 , 0 ], [ 1 ,..., 1 , 0 ]] >>> cosine_similarity ( X , Y ) array([[0. , 0. ],...scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.cosine_similarity.html -
config_context — scikit-learn 1.7.2 documentation
print ‘SVC()’, but would print ‘SVC(C=1.0, cache_size=200, …)’...float ( 'nan' )]) Traceback (most recent call last): ... ValueError...scikit-learn.org/stable/modules/generated/sklearn.config_context.html -
MetaEstimatorMixin — scikit-learn 1.7.2 documen...
, X , y = None ): ... if self . estimator is None : ... self...self . estimator_ = LogisticRegression () ... else : ... self . estimator_...scikit-learn.org/stable/modules/generated/sklearn.base.MetaEstimatorMixin.html -
MinCovDet — scikit-learn 1.7.2 documentation
real_cov = np . array ([[ .8 , .3 ], ... [ .3 , .4 ]]) >>> rng =...>>> cov . covariance_ array([[0.7411, 0.2535], [0.2535, 0.3053]])...scikit-learn.org/stable/modules/generated/sklearn.covariance.MinCovDet.html -
get_data_home — scikit-learn 1.7.2 documentation
get_data_home () >>> os . path . exists ( data_home_path ) True Gallery...examples # Out-of-core classification of text documents Out-of-core...scikit-learn.org/stable/modules/generated/sklearn.datasets.get_data_home.html