U 3d>F@sdZddlZddlZddlZddlmZmZddlZddlm Z ddl Z ddl m Z ddlmZddlmZmZmZdd lmZmZmZmZdd lmZdd lmZmZmZdd lmZm Z m!Z!dd l"m#Z#ddl$m%Z%m&Z&ddl'm(Z(m)Z)m*Z*m+Z+ddZ,d5ddZ-ddddddddddddd ddZ.d6ddZ/dddddddddddddd d!d"Z0d7d#d$Z1d8d%d&ddddd'ddddd&dd&d&d&dddd(d)d*d+d,Z2Gd-d.d.eeZ3Gd/d0d0e3eZ4Gd1d2d2e3eZ5Gd3d4d4e3eZ6dS)9z Dictionary learning. N)IntegralReal)ceil)linalg)effective_n_jobs) BaseEstimatorTransformerMixinClassNamePrefixFeaturesOutMixin) check_arraycheck_random_stategen_even_slices gen_batches) deprecated)HiddenInterval StrOptions)randomized_svd row_normssvd_flip)check_is_fitted)delayedParallel)Lassoorthogonal_mp_gram LassoLarsLarscCs|r|dkrtd|dS)N)omplarsz9Positive constraint not supported for '{}' coding method.) ValueErrorformat)methodpositiver#H/tmp/pip-unpacked-wheel-zrfo1fqw/sklearn/decomposition/_dict_learning.py_check_positive_codings r% lasso_larsTFc  CsP|jdkr|ddtjf}|j\} } |jd}|jd|jdkrXtd|j|j|dkrz|dkrzd}t||j}t|| |dkrt || }z@tj dd }t |d| |d| |d }|j |j|j|d |j }W5tj f|XnH|dkrXt || }t|d||d | d }|dk r<|jds6t|}||_ |j |j|j| d|j }n|dkrz@tj dd }td| |t|dd}|j |j|j|d |j }W5tj f|Xn|dkrt|tt||dj}| r4tj|dd|dn:|dkr(t||t|dt|d d|dj}n td||jdkrL|| |S|S)a Generic sparse coding. Each column of the result is the solution to a Lasso problem. Parameters ---------- X : ndarray of shape (n_samples, n_features) Data matrix. dictionary : ndarray of shape (n_components, n_features) The dictionary matrix against which to solve the sparse coding of the data. Some of the algorithms assume normalized rows. gram : ndarray of shape (n_components, n_components) or None Precomputed Gram matrix, `dictionary * dictionary'` gram can be `None` if method is 'threshold'. cov : ndarray of shape (n_components, n_samples), default=None Precomputed covariance, `dictionary * X'`. algorithm : {'lasso_lars', 'lasso_cd', 'lars', 'omp', 'threshold'}, default='lasso_lars' The algorithm used: * `'lars'`: uses the least angle regression method (`linear_model.lars_path`); * `'lasso_lars'`: uses Lars to compute the Lasso solution; * `'lasso_cd'`: uses the coordinate descent method to compute the Lasso solution (`linear_model.Lasso`). lasso_lars will be faster if the estimated components are sparse; * `'omp'`: uses orthogonal matching pursuit to estimate the sparse solution; * `'threshold'`: squashes to zero all coefficients less than regularization from the projection `dictionary * data'`. regularization : int or float, default=None The regularization parameter. It corresponds to alpha when algorithm is `'lasso_lars'`, `'lasso_cd'` or `'threshold'`. Otherwise it corresponds to `n_nonzero_coefs`. init : ndarray of shape (n_samples, n_components), default=None Initialization value of the sparse code. Only used if `algorithm='lasso_cd'`. max_iter : int, default=1000 Maximum number of iterations to perform if `algorithm='lasso_cd'` or `'lasso_lars'`. copy_cov : bool, default=True Whether to copy the precomputed covariance matrix; if `False`, it may be overwritten. check_input : bool, default=True If `False`, the input arrays `X` and dictionary will not be checked. verbose : int, default=0 Controls the verbosity; the higher, the more messages. positive: bool, default=False Whether to enforce a positivity constraint on the sparse code. .. versionadded:: 0.20 Returns ------- code : ndarray of shape (n_components, n_features) The sparse codes. See Also -------- sklearn.linear_model.lars_path sklearn.linear_model.orthogonal_mp sklearn.linear_model.Lasso SparseCoder NrzRDictionary and X have different numbers of features:dictionary.shape: {} X.shape{}lasso_cdFr&ignore)all)alpha fit_interceptverbose precomputefit_pathr"max_iter)XyT)r,r-r/r1Z warm_startr"Z WRITEABLE) check_inputr)r-r.r/n_nonzero_coefsr0 thresholdoutr)Zsquared)ZGramr2r4tolZ norms_squaredZcopy_Xyz\Sparse coding method must be "lasso_lars" "lasso_cd", "lasso", "threshold" or "omp", got %s.r)ndimnpnewaxisshaperr dotTr%floatZseterrrfitZcoef_rflagsarrayrintsignmaximumabscliprrZreshape)X dictionarygramcov algorithmregularizationcopy_covinitr1r3r.r" n_samples n_features n_componentsr,Zerr_mgtr&Znew_codeZclfrr#r#r$_sparse_encode#sY                 "     rS) rJrKrLr4r,rNrOr1n_jobsr3r.r"c s| rJdkr:tdtjtjgdtdtjtjgdnttj\}}jd}dkr|dkr|tjdkrdkrdtjdkr| dkrtt|d d | n| dkrd t | d ksdkrt  d d }|St ||f}t t |t | }t| d  f dd|D}t||D]\}}|||<qn|S)a:Sparse coding. Each row of the result is the solution to a sparse coding problem. The goal is to find a sparse array `code` such that:: X ~= code * dictionary Read more in the :ref:`User Guide `. Parameters ---------- X : ndarray of shape (n_samples, n_features) Data matrix. dictionary : ndarray of shape (n_components, n_features) The dictionary matrix against which to solve the sparse coding of the data. Some of the algorithms assume normalized rows for meaningful output. gram : ndarray of shape (n_components, n_components), default=None Precomputed Gram matrix, `dictionary * dictionary'`. cov : ndarray of shape (n_components, n_samples), default=None Precomputed covariance, `dictionary' * X`. algorithm : {'lasso_lars', 'lasso_cd', 'lars', 'omp', 'threshold'}, default='lasso_lars' The algorithm used: * `'lars'`: uses the least angle regression method (`linear_model.lars_path`); * `'lasso_lars'`: uses Lars to compute the Lasso solution; * `'lasso_cd'`: uses the coordinate descent method to compute the Lasso solution (`linear_model.Lasso`). lasso_lars will be faster if the estimated components are sparse; * `'omp'`: uses orthogonal matching pursuit to estimate the sparse solution; * `'threshold'`: squashes to zero all coefficients less than regularization from the projection `dictionary * data'`. n_nonzero_coefs : int, default=None Number of nonzero coefficients to target in each column of the solution. This is only used by `algorithm='lars'` and `algorithm='omp'` and is overridden by `alpha` in the `omp` case. If `None`, then `n_nonzero_coefs=int(n_features / 10)`. alpha : float, default=None If `algorithm='lasso_lars'` or `algorithm='lasso_cd'`, `alpha` is the penalty applied to the L1 norm. If `algorithm='threshold'`, `alpha` is the absolute value of the threshold below which coefficients will be squashed to zero. If `algorithm='omp'`, `alpha` is the tolerance parameter: the value of the reconstruction error targeted. In this case, it overrides `n_nonzero_coefs`. If `None`, default to 1. copy_cov : bool, default=True Whether to copy the precomputed covariance matrix; if `False`, it may be overwritten. init : ndarray of shape (n_samples, n_components), default=None Initialization value of the sparse codes. Only used if `algorithm='lasso_cd'`. max_iter : int, default=1000 Maximum number of iterations to perform if `algorithm='lasso_cd'` or `'lasso_lars'`. n_jobs : int, default=None Number of parallel jobs to run. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary ` for more details. check_input : bool, default=True If `False`, the input arrays X and dictionary will not be checked. verbose : int, default=0 Controls the verbosity; the higher, the more messages. positive : bool, default=False Whether to enforce positivity when finding the encoding. .. versionadded:: 0.20 Returns ------- code : ndarray of shape (n_samples, n_components) The sparse codes. See Also -------- sklearn.linear_model.lars_path : Compute Least Angle Regression or Lasso path using LARS algorithm. sklearn.linear_model.orthogonal_mp : Solves Orthogonal Matching Pursuit problems. sklearn.linear_model.Lasso : Train Linear Model with L1 prior as regularizer. SparseCoder : Find a sparse representation of data from a fixed precomputed dictionary. r)C)orderdtyperNr5F)rr r(g?) rKrLrMrNrOr1r3r.r")rTr.c3s^|]V}tt|dk r.dd|fnd dk rF|ndd d VqdS)NF)rMrNrOr1r3r.r")rrS).0 this_slice rHrLrNrKrIrJrOr1r"rMr.r#r$ sz sparse_encode..)r r:float64float32r<r=r>minmaxrrSemptylistr rzip)rHrIrJrKrLr4r,rNrOr1rTr3r.r"rPrQrRcodeZslicesZ code_viewsrZZ this_viewr#r[r$ sparse_encodes`t   ( rec CsF|j\}} t|}|dkr$|j|}|dkr6|j|}d} t| D]} || | fdkr|| |dd| f|| ||| | f7<nV|||} d| pd} |jd| t| d}| ||| <d|dd| f<| d7} |rtj || dd|| d|| t t || d<qB|rB| dkrBt | ddS) aUpdate the dense dictionary factor in place. Parameters ---------- dictionary : ndarray of shape (n_components, n_features) Value of the dictionary at the previous iteration. Y : ndarray of shape (n_samples, n_features) Data matrix. code : ndarray of shape (n_samples, n_components) Sparse coding of the data against which to optimize the dictionary. A : ndarray of shape (n_components, n_components), default=None Together with `B`, sufficient stats of the online model to update the dictionary. B : ndarray of shape (n_features, n_components), default=None Together with `A`, sufficient stats of the online model to update the dictionary. verbose: bool, default=False Degree of output the procedure will print. random_state : int, RandomState instance or None, default=None Used for randomly initializing the dictionary. Pass an int for reproducible results across multiple function calls. See :term:`Glossary `. positive : bool, default=False Whether to enforce positivity when finding the dictionary. .. versionadded:: 0.20 Nrgư>g{Gz?r()sizer6z unused atoms resampled.)r<r r>rangechoiceZstdnormallenr:rGr`rnormprint)rIYrdABr. random_stater"rPrRZn_unusedknewdZ noise_levelZnoiser#r#r$ _update_dicts*,    6 "rsd:0yE>r) r1r8r!rT dict_init code_initcallbackr.rp return_n_iter positive_dict positive_codemethod_max_iterc Cs|dkrtd|t||d|}t}t|}t| } |dk rb|dk rbtj|dd}|}n8tj|dd\}}}t ||\}}|ddtj f|}t |}||kr|ddd|f}|d|ddf}nBtj |t t |||ff}tj|t |||jd ff}t|}g}tj}| d kr@td d d d }t|D]2}t|}| d krtjdtjn| rtd|||d|ft||||||||| d }t|||| | | ddt|||d|tt|}|||dkr\|d|d }|||d kr\| d krDtdn| rVtd|q|ddkrL| dk rL| tqL| r||||d fS|||fSdS)a$Solve a dictionary learning matrix factorization problem. Finds the best dictionary and the corresponding sparse code for approximating the data matrix X by solving:: (U^*, V^*) = argmin 0.5 || X - U V ||_Fro^2 + alpha * || U ||_1,1 (U,V) with || V_k ||_2 = 1 for all 0 <= k < n_components where V is the dictionary and U is the sparse code. ||.||_Fro stands for the Frobenius norm and ||.||_1,1 stands for the entry-wise matrix norm which is the sum of the absolute values of all the entries in the matrix. Read more in the :ref:`User Guide `. Parameters ---------- X : ndarray of shape (n_samples, n_features) Data matrix. n_components : int Number of dictionary atoms to extract. alpha : int Sparsity controlling parameter. max_iter : int, default=100 Maximum number of iterations to perform. tol : float, default=1e-8 Tolerance for the stopping condition. method : {'lars', 'cd'}, default='lars' The method used: * `'lars'`: uses the least angle regression method to solve the lasso problem (`linear_model.lars_path`); * `'cd'`: uses the coordinate descent method to compute the Lasso solution (`linear_model.Lasso`). Lars will be faster if the estimated components are sparse. n_jobs : int, default=None Number of parallel jobs to run. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary ` for more details. dict_init : ndarray of shape (n_components, n_features), default=None Initial value for the dictionary for warm restart scenarios. Only used if `code_init` and `dict_init` are not None. code_init : ndarray of shape (n_samples, n_components), default=None Initial value for the sparse code for warm restart scenarios. Only used if `code_init` and `dict_init` are not None. callback : callable, default=None Callable that gets invoked every five iterations. verbose : bool, default=False To control the verbosity of the procedure. random_state : int, RandomState instance or None, default=None Used for randomly initializing the dictionary. Pass an int for reproducible results across multiple function calls. See :term:`Glossary `. return_n_iter : bool, default=False Whether or not to return the number of iterations. positive_dict : bool, default=False Whether to enforce positivity when finding the dictionary. .. versionadded:: 0.20 positive_code : bool, default=False Whether to enforce positivity when finding the code. .. versionadded:: 0.20 method_max_iter : int, default=1000 Maximum number of iterations to perform. .. versionadded:: 0.22 Returns ------- code : ndarray of shape (n_samples, n_components) The sparse code factor in the matrix factorization. dictionary : ndarray of shape (n_components, n_features), The dictionary factor in the matrix factorization. errors : array Vector of errors at each iteration. n_iter : int Number of iterations run. Returned only if `return_n_iter` is set to True. See Also -------- dict_learning_online : Solve a dictionary learning matrix factorization problem online. DictionaryLearning : Find a dictionary that sparsely encodes data. MiniBatchDictionaryLearning : A faster, less accurate version of the dictionary learning algorithm. SparsePCA : Sparse Principal Components Analysis. MiniBatchSparsePCA : Mini-batch Sparse Principal Components Analysis. rcdz2Coding method %r not supported as a fit algorithm.lasso_NF)rVF)Z full_matricesr([dict_learning] end.zCIteration % 3i (elapsed time: % 3is, % 4.1fmn, current cost % 7.3f)<)rLr,rOrTr"r1r.r.rpr"?rrz+--- Convergence reached after %d iterations)rr%timer?r r:rBrZsvdrr;rjZc_zerosr_r<ZasfortranarraynanrlrgsysstdoutwriteflushrerssumrFappendlocals)rHrRr,r1r8r!rTrvrwrxr.rpryrzr{r|t0rdrISrerrorsZ current_costiidtZdEr#r#r$ dict_learnings                 rcCs>|dkr6d|d}|r&|d|7}t|t|S|SdS)Nr'zB' is deprecated in version 1.1 and will be removed in version 1.3.r)warningswarn FutureWarning)paramnamedefaultadditional_messagemsgr#r#r$_check_warn_deprecateds  rr(rrMbP?rX)r,n_iterr1 return_codervrx batch_sizer.shufflerTr! iter_offsetrpreturn_inner_stats inner_statsryrzr{r|r8max_no_improvementc-Cs||| |f}|dk r.tdd|Ds.tdt| ddd} t|dd d d }t|d dd}t|d d dd }|dk rd| }t|||| | || |||||||| |||d|}|s|jS||}||jfSt|dddd }|dkrtdt d}|dkr|j d}| dkrtdt | |d| } t }|j \}}t |}t|}|dk r\|}n*t|||d\}} }| ddtjf|}t|}!||!kr|d|ddf}n(tj|tj||!|j df|jdf}| dkrtddd| r|}"||"n|}"t|"dtjtjgd d }"t|d!|"jd d }tj|d"d#}t||}#t|#}#|dkrtj||f|"jd}$tj||f|"jd}%n|d}$|d}%| d}&t t!| | ||#D]B\}&}'|"|'}(t |})| dkrt"j#$d$t"j#%n<| r:| d%ks$|&t&d&| dkr:td'|&|)|)d(ft'|(|| || d ||| d) }*|&|dkrtt |&d|}+nt |d*|&d|}+|+d||+d},|$|,9}$|$t(|*j)|*7}$|%|,9}%|%t(|(j)|*7}%t*||(|*|$|%| ||d+|dk r|t+q|r2|r&||$|%f|&| dfS||$|%ffS|r| dkrPtd,ddn| dkrftd-ddt'||| || d ||| d) }| dkrt |})td.|)|)d(f|r|||&| dfS||fS|r||&| dfS|SdS)/aNSolve a dictionary learning matrix factorization problem online. Finds the best dictionary and the corresponding sparse code for approximating the data matrix X by solving:: (U^*, V^*) = argmin 0.5 || X - U V ||_Fro^2 + alpha * || U ||_1,1 (U,V) with || V_k ||_2 = 1 for all 0 <= k < n_components where V is the dictionary and U is the sparse code. ||.||_Fro stands for the Frobenius norm and ||.||_1,1 stands for the entry-wise matrix norm which is the sum of the absolute values of all the entries in the matrix. This is accomplished by repeatedly iterating over mini-batches by slicing the input data. Read more in the :ref:`User Guide `. Parameters ---------- X : ndarray of shape (n_samples, n_features) Data matrix. n_components : int or None, default=2 Number of dictionary atoms to extract. If None, then ``n_components`` is set to ``n_features``. alpha : float, default=1 Sparsity controlling parameter. n_iter : int, default=100 Number of mini-batch iterations to perform. .. deprecated:: 1.1 `n_iter` is deprecated in 1.1 and will be removed in 1.4. Use `max_iter` instead. max_iter : int, default=None Maximum number of iterations over the complete dataset before stopping independently of any early stopping criterion heuristics. If ``max_iter`` is not None, ``n_iter`` is ignored. .. versionadded:: 1.1 return_code : bool, default=True Whether to also return the code U or just the dictionary `V`. dict_init : ndarray of shape (n_components, n_features), default=None Initial values for the dictionary for warm restart scenarios. If `None`, the initial values for the dictionary are created with an SVD decomposition of the data via :func:`~sklearn.utils.randomized_svd`. callback : callable, default=None A callable that gets invoked at the end of each iteration. batch_size : int, default=3 The number of samples to take in each batch. .. versionchanged:: 1.3 The default value of `batch_size` will change from 3 to 256 in version 1.3. verbose : bool, default=False To control the verbosity of the procedure. shuffle : bool, default=True Whether to shuffle the data before splitting it in batches. n_jobs : int, default=None Number of parallel jobs to run. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary ` for more details. method : {'lars', 'cd'}, default='lars' * `'lars'`: uses the least angle regression method to solve the lasso problem (`linear_model.lars_path`); * `'cd'`: uses the coordinate descent method to compute the Lasso solution (`linear_model.Lasso`). Lars will be faster if the estimated components are sparse. iter_offset : int, default=0 Number of previous iterations completed on the dictionary used for initialization. .. deprecated:: 1.1 `iter_offset` serves internal purpose only and will be removed in 1.3. random_state : int, RandomState instance or None, default=None Used for initializing the dictionary when ``dict_init`` is not specified, randomly shuffling the data when ``shuffle`` is set to ``True``, and updating the dictionary. Pass an int for reproducible results across multiple function calls. See :term:`Glossary `. return_inner_stats : bool, default=False Return the inner statistics A (dictionary covariance) and B (data approximation). Useful to restart the algorithm in an online setting. If `return_inner_stats` is `True`, `return_code` is ignored. .. deprecated:: 1.1 `return_inner_stats` serves internal purpose only and will be removed in 1.3. inner_stats : tuple of (A, B) ndarrays, default=None Inner sufficient statistics that are kept by the algorithm. Passing them at initialization is useful in online settings, to avoid losing the history of the evolution. `A` `(n_components, n_components)` is the dictionary covariance matrix. `B` `(n_features, n_components)` is the data approximation matrix. .. deprecated:: 1.1 `inner_stats` serves internal purpose only and will be removed in 1.3. return_n_iter : bool, default=False Whether or not to return the number of iterations. .. deprecated:: 1.1 `return_n_iter` will be removed in 1.3 and n_iter will always be returned. positive_dict : bool, default=False Whether to enforce positivity when finding the dictionary. .. versionadded:: 0.20 positive_code : bool, default=False Whether to enforce positivity when finding the code. .. versionadded:: 0.20 method_max_iter : int, default=1000 Maximum number of iterations to perform when solving the lasso problem. .. versionadded:: 0.22 tol : float, default=1e-3 Control early stopping based on the norm of the differences in the dictionary between 2 steps. Used only if `max_iter` is not None. To disable early stopping based on changes in the dictionary, set `tol` to 0.0. .. versionadded:: 1.1 max_no_improvement : int, default=10 Control early stopping based on the consecutive number of mini batches that does not yield an improvement on the smoothed cost function. Used only if `max_iter` is not None. To disable convergence detection based on cost function, set `max_no_improvement` to None. .. versionadded:: 1.1 Returns ------- code : ndarray of shape (n_samples, n_components), The sparse code (only returned if `return_code=True`). dictionary : ndarray of shape (n_components, n_features), The solutions to the dictionary learning problem. n_iter : int Number of iterations run. Returned only if `return_n_iter` is set to `True`. See Also -------- dict_learning : Solve a dictionary learning matrix factorization problem. DictionaryLearning : Find a dictionary that sparsely encodes data. MiniBatchDictionaryLearning : A faster, less accurate, version of the dictionary learning algorithm. SparsePCA : Sparse Principal Components Analysis. MiniBatchSparsePCA : Mini-batch Sparse Principal Components Analysis. Ncss|]}|dkVqdS)rNr#)rYargr#r#r$r\sz'dict_learning_online..zuThe following arguments are incompatible with 'max_iter': return_n_iter, return_inner_stats, iter_offset, inner_statsrr)rrFz,From 1.3 inner_stats will never be returned.)rrrryzFrom 1.3 'n_iter' will never be returned. Refer to the 'n_iter_' and 'n_steps_' attributes of the MiniBatchDictionaryLearning object instead.r)rRr,rrT fit_algorithmrrrvrptransform_algorithmtransform_alphar{rztransform_max_iterr.rxr8rrrtzUse 'max_iter' instead.rAThe default value of batch_size will change from 3 to 256 in 1.3.r(r}z/Coding method not supported as a fit algorithm.rprWrrrrUrVrWcopyrW requirementsrrXY@z.Iteration % 3i (elapsed time: % 3is, % 4.1fmn)rrLr,rTr3r"r1r.rrzLearning code...|z"done (total time: % 3is, % 4.1fmn)),r+rrMiniBatchDictionaryLearningr@ components_ transformrrrr<r%rr?r rr:r;rjrrrWrlrrr r]r^requirer itertoolscyclercrgrrrrrrer=r>rsr)-rHrRr,rr1rrvrxrr.rrTr!rrprrryrzr{r|r8rdepsrZestrdrrPrQrI_rrX_trainbatchesrnrorbatchZthis_XrZ this_codethetabetar#r#r$dict_learning_onlines^H                                 rc@s(eZdZdZddZddZddZdS) _BaseSparseCodingz>Base class from SparseCoder and DictionaryLearning algorithms.cCs.||_||_||_||_||_||_||_dSN)rtransform_n_nonzero_coefsrr split_signrTr{)selfrrrrrTr{rr#r#r$__init__s z_BaseSparseCoding.__init__c Cs|j|dd}t|dr*|jdkr*|j}n|j}t|||j|j||j|j|j d}|j r|j \}}t |d|f}t |d|ddd|f<t |d |dd|df<|}|S)zWPrivate method allowing to accommodate both DictionaryLearning and SparseCoder.F)resetr,N)rLr4r,r1rTr"rr)_validate_datahasattrrr,rerrrrTr{rr<r:rarEZminimum)rrHrIrrdrPrQZ split_coder#r#r$ _transforms*  z_BaseSparseCoding._transformcCst||||jS)aEncode the data as a sparse combination of the dictionary atoms. Coding method is determined by the object parameter `transform_algorithm`. Parameters ---------- X : ndarray of shape (n_samples, n_features) Test data to be transformed, must have the same number of features as the data used to train the model. Returns ------- X_new : ndarray of shape (n_samples, n_components) Transformed data. )rrrrrHr#r#r$rsz_BaseSparseCoding.transformN)__name__ __module__ __qualname____doc__rrrr#r#r#r$rsrcs|eZdZdZdgZddddddddfdd Zdd d Zdfd d ZddZe ddZ e ddZ e ddZ Z S) SparseCoderaSparse coding. Finds a sparse representation of data against a fixed, precomputed dictionary. Each row of the result is the solution to a sparse coding problem. The goal is to find a sparse array `code` such that:: X ~= code * dictionary Read more in the :ref:`User Guide `. Parameters ---------- dictionary : ndarray of shape (n_components, n_features) The dictionary atoms used for sparse coding. Lines are assumed to be normalized to unit norm. transform_algorithm : {'lasso_lars', 'lasso_cd', 'lars', 'omp', 'threshold'}, default='omp' Algorithm used to transform the data: - `'lars'`: uses the least angle regression method (`linear_model.lars_path`); - `'lasso_lars'`: uses Lars to compute the Lasso solution; - `'lasso_cd'`: uses the coordinate descent method to compute the Lasso solution (linear_model.Lasso). `'lasso_lars'` will be faster if the estimated components are sparse; - `'omp'`: uses orthogonal matching pursuit to estimate the sparse solution; - `'threshold'`: squashes to zero all coefficients less than alpha from the projection ``dictionary * X'``. transform_n_nonzero_coefs : int, default=None Number of nonzero coefficients to target in each column of the solution. This is only used by `algorithm='lars'` and `algorithm='omp'` and is overridden by `alpha` in the `omp` case. If `None`, then `transform_n_nonzero_coefs=int(n_features / 10)`. transform_alpha : float, default=None If `algorithm='lasso_lars'` or `algorithm='lasso_cd'`, `alpha` is the penalty applied to the L1 norm. If `algorithm='threshold'`, `alpha` is the absolute value of the threshold below which coefficients will be squashed to zero. If `algorithm='omp'`, `alpha` is the tolerance parameter: the value of the reconstruction error targeted. In this case, it overrides `n_nonzero_coefs`. If `None`, default to 1. split_sign : bool, default=False Whether to split the sparse feature vector into the concatenation of its negative part and its positive part. This can improve the performance of downstream classifiers. n_jobs : int, default=None Number of parallel jobs to run. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary ` for more details. positive_code : bool, default=False Whether to enforce positivity when finding the code. .. versionadded:: 0.20 transform_max_iter : int, default=1000 Maximum number of iterations to perform if `algorithm='lasso_cd'` or `lasso_lars`. .. versionadded:: 0.22 Attributes ---------- n_components_ : int Number of atoms. n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 See Also -------- DictionaryLearning : Find a dictionary that sparsely encodes data. MiniBatchDictionaryLearning : A faster, less accurate, version of the dictionary learning algorithm. MiniBatchSparsePCA : Mini-batch Sparse Principal Components Analysis. SparsePCA : Sparse Principal Components Analysis. sparse_encode : Sparse coding where each row of the result is the solution to a sparse coding problem. Examples -------- >>> import numpy as np >>> from sklearn.decomposition import SparseCoder >>> X = np.array([[-1, -1, -1], [0, 0, 3]]) >>> dictionary = np.array( ... [[0, 1, 0], ... [-1, -1, 2], ... [1, 1, 1], ... [0, 1, 1], ... [0, 2, 1]], ... dtype=np.float64 ... ) >>> coder = SparseCoder( ... dictionary=dictionary, transform_algorithm='lasso_lars', ... transform_alpha=1e-10, ... ) >>> coder.transform(X) array([[ 0., 0., -1., 0., 0.], [ 0., 1., 1., 0., 0.]]) rIrNFr')rrrrrTr{rc s"t|||||||||_dSr)superrrI) rrIrrrrrTr{r __class__r#r$rPs  zSparseCoder.__init__cCs|S)aDo nothing and return the estimator unchanged. This method is just there to implement the usual API and hence work in pipelines. Parameters ---------- X : Ignored Not used, present for API consistency by convention. y : Ignored Not used, present for API consistency by convention. Returns ------- self : object Returns the instance itself. r#rrHyr#r#r$r@gszSparseCoder.fitcst||jS)aQEncode the data as a sparse combination of the dictionary atoms. Coding method is determined by the object parameter `transform_algorithm`. Parameters ---------- X : ndarray of shape (n_samples, n_features) Training vector, where `n_samples` is the number of samples and `n_features` is the number of features. y : Ignored Not used, present for API consistency by convention. Returns ------- X_new : ndarray of shape (n_samples, n_components) Transformed data. )rrrIrrr#r$r|szSparseCoder.transformcCsdtjtjgdS)NF)Z requires_fitpreserves_dtyper:r]r^rr#r#r$ _more_tagss zSparseCoder._more_tagscCs |jjdS)zNumber of atoms.rrIr<rr#r#r$ n_components_szSparseCoder.n_components_cCs |jjdS)z%Number of features seen during `fit`.r(rrr#r#r$n_features_in_szSparseCoder.n_features_in_cCs|jS)&Number of transformed output features.)rrr#r#r$_n_features_outszSparseCoder._n_features_out)N)N)rrrrZ_required_parametersrr@rrpropertyrrr __classcell__r#r#rr$rs&w   rcs2eZdZUdZeedddddgeeddddgeeddddgeeddddgeddhged d dd d hgeedddddgeedddddgedgej dgej dgd gdgdgdgdgeeddddgdZ e e d<dddddd ddddddddddddfdd Z dddZeddZddZZS) DictionaryLearningatDictionary learning. Finds a dictionary (a set of atoms) that performs well at sparsely encoding the fitted data. Solves the optimization problem:: (U^*,V^*) = argmin 0.5 || X - U V ||_Fro^2 + alpha * || U ||_1,1 (U,V) with || V_k ||_2 <= 1 for all 0 <= k < n_components ||.||_Fro stands for the Frobenius norm and ||.||_1,1 stands for the entry-wise matrix norm which is the sum of the absolute values of all the entries in the matrix. Read more in the :ref:`User Guide `. Parameters ---------- n_components : int, default=None Number of dictionary elements to extract. If None, then ``n_components`` is set to ``n_features``. alpha : float, default=1.0 Sparsity controlling parameter. max_iter : int, default=1000 Maximum number of iterations to perform. tol : float, default=1e-8 Tolerance for numerical error. fit_algorithm : {'lars', 'cd'}, default='lars' * `'lars'`: uses the least angle regression method to solve the lasso problem (:func:`~sklearn.linear_model.lars_path`); * `'cd'`: uses the coordinate descent method to compute the Lasso solution (:class:`~sklearn.linear_model.Lasso`). Lars will be faster if the estimated components are sparse. .. versionadded:: 0.17 *cd* coordinate descent method to improve speed. transform_algorithm : {'lasso_lars', 'lasso_cd', 'lars', 'omp', 'threshold'}, default='omp' Algorithm used to transform the data: - `'lars'`: uses the least angle regression method (:func:`~sklearn.linear_model.lars_path`); - `'lasso_lars'`: uses Lars to compute the Lasso solution. - `'lasso_cd'`: uses the coordinate descent method to compute the Lasso solution (:class:`~sklearn.linear_model.Lasso`). `'lasso_lars'` will be faster if the estimated components are sparse. - `'omp'`: uses orthogonal matching pursuit to estimate the sparse solution. - `'threshold'`: squashes to zero all coefficients less than alpha from the projection ``dictionary * X'``. .. versionadded:: 0.17 *lasso_cd* coordinate descent method to improve speed. transform_n_nonzero_coefs : int, default=None Number of nonzero coefficients to target in each column of the solution. This is only used by `algorithm='lars'` and `algorithm='omp'`. If `None`, then `transform_n_nonzero_coefs=int(n_features / 10)`. transform_alpha : float, default=None If `algorithm='lasso_lars'` or `algorithm='lasso_cd'`, `alpha` is the penalty applied to the L1 norm. If `algorithm='threshold'`, `alpha` is the absolute value of the threshold below which coefficients will be squashed to zero. If `None`, defaults to `alpha`. .. versionchanged:: 1.2 When None, default value changed from 1.0 to `alpha`. n_jobs : int or None, default=None Number of parallel jobs to run. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary ` for more details. code_init : ndarray of shape (n_samples, n_components), default=None Initial value for the code, for warm restart. Only used if `code_init` and `dict_init` are not None. dict_init : ndarray of shape (n_components, n_features), default=None Initial values for the dictionary, for warm restart. Only used if `code_init` and `dict_init` are not None. verbose : bool, default=False To control the verbosity of the procedure. split_sign : bool, default=False Whether to split the sparse feature vector into the concatenation of its negative part and its positive part. This can improve the performance of downstream classifiers. random_state : int, RandomState instance or None, default=None Used for initializing the dictionary when ``dict_init`` is not specified, randomly shuffling the data when ``shuffle`` is set to ``True``, and updating the dictionary. Pass an int for reproducible results across multiple function calls. See :term:`Glossary `. positive_code : bool, default=False Whether to enforce positivity when finding the code. .. versionadded:: 0.20 positive_dict : bool, default=False Whether to enforce positivity when finding the dictionary. .. versionadded:: 0.20 transform_max_iter : int, default=1000 Maximum number of iterations to perform if `algorithm='lasso_cd'` or `'lasso_lars'`. .. versionadded:: 0.22 Attributes ---------- components_ : ndarray of shape (n_components, n_features) dictionary atoms extracted from the data error_ : array vector of errors at each iteration n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 n_iter_ : int Number of iterations run. See Also -------- MiniBatchDictionaryLearning: A faster, less accurate, version of the dictionary learning algorithm. MiniBatchSparsePCA : Mini-batch Sparse Principal Components Analysis. SparseCoder : Find a sparse representation of data from a fixed, precomputed dictionary. SparsePCA : Sparse Principal Components Analysis. References ---------- J. Mairal, F. Bach, J. Ponce, G. Sapiro, 2009: Online dictionary learning for sparse coding (https://www.di.ens.fr/sierra/pdfs/icml09.pdf) Examples -------- >>> import numpy as np >>> from sklearn.datasets import make_sparse_coded_signal >>> from sklearn.decomposition import DictionaryLearning >>> X, dictionary, code = make_sparse_coded_signal( ... n_samples=100, n_components=15, n_features=20, n_nonzero_coefs=10, ... random_state=42, data_transposed=False ... ) >>> dict_learner = DictionaryLearning( ... n_components=15, transform_algorithm='lasso_lars', transform_alpha=0.1, ... random_state=42, ... ) >>> X_transformed = dict_learner.fit_transform(X) We can check the level of sparsity of `X_transformed`: >>> np.mean(X_transformed == 0) 0.41... We can compare the average squared euclidean norm of the reconstruction error of the sparse coded signal relative to the squared euclidean norm of the original signal: >>> X_hat = X_transformed @ dict_learner.components_ >>> np.mean(np.sum((X_hat - X) ** 2, axis=1) / np.sum(X ** 2, axis=1)) 0.07... r(Nleftclosedrrr~r&r)rr5r.booleanrp)rRr,r1r8rrrrrTrwrvr.rrpr{rzr_parameter_constraintsr'ruF)r,r1r8rrrrrTrwrvr.rrpr{rzrc sXt|||| | ||||_||_||_||_||_| |_| |_| |_ ||_ ||_ dSr) rrrRr,r1r8rrwrvr.rprz)rrRr,r1r8rrrrrTrwrvr.rrpr{rzrrr#r$rzs& zDictionaryLearning.__init__cCs|t|j}||}|jdkr2|jd}n|j}t|||j|j|j |j |j |j |j |j|j|d|j|jd\}}}|_||_||_|S)Fit the model from data in X. Parameters ---------- X : array-like of shape (n_samples, n_features) Training vector, where `n_samples` is the number of samples and `n_features` is the number of features. y : Ignored Not used, present for API consistency by convention. Returns ------- self : object Returns the instance itself. Nr(T) r,r8r1r!r|rTrwrvr.rpryrzr{)_validate_paramsr rprrRr<rr,r8r1rrrTrwrvr.rzr{n_iter_rZerror_)rrHrrprRVUEr#r#r$r@s4    zDictionaryLearning.fitcCs |jjdSrrrr<rr#r#r$rsz"DictionaryLearning._n_features_outcCsdtjtjgiSNrrrr#r#r$rs zDictionaryLearning._more_tags)N)N)rrrrrrrrr:ndarrayrdict__annotations__rr@rrrrr#r#rr$rsX = * / rcseZdZUdZeedddddgeeddddgeeddddeedhgeedddddgedd hgdegeeddddeed hgd gde j ged d d ddhgeedddddgeedddddgdgd gdgd gd geeddddgde geeddddgeedddddgdZ e ed<d7dddd dd dddddddddddddddfdd ZededdZeded d!Zed"ed#d$Zd%d&Zd'd(Zd)d*Zd+d,Zd-d.Zd8d/d0Zd9d1d2Zed3d4Zd5d6ZZS):raC#Mini-batch dictionary learning. Finds a dictionary (a set of atoms) that performs well at sparsely encoding the fitted data. Solves the optimization problem:: (U^*,V^*) = argmin 0.5 || X - U V ||_Fro^2 + alpha * || U ||_1,1 (U,V) with || V_k ||_2 <= 1 for all 0 <= k < n_components ||.||_Fro stands for the Frobenius norm and ||.||_1,1 stands for the entry-wise matrix norm which is the sum of the absolute values of all the entries in the matrix. Read more in the :ref:`User Guide `. Parameters ---------- n_components : int, default=None Number of dictionary elements to extract. alpha : float, default=1 Sparsity controlling parameter. n_iter : int, default=1000 Total number of iterations over data batches to perform. .. deprecated:: 1.1 ``n_iter`` is deprecated in 1.1 and will be removed in 1.4. Use ``max_iter`` instead. max_iter : int, default=None Maximum number of iterations over the complete dataset before stopping independently of any early stopping criterion heuristics. If ``max_iter`` is not None, ``n_iter`` is ignored. .. versionadded:: 1.1 fit_algorithm : {'lars', 'cd'}, default='lars' The algorithm used: - `'lars'`: uses the least angle regression method to solve the lasso problem (`linear_model.lars_path`) - `'cd'`: uses the coordinate descent method to compute the Lasso solution (`linear_model.Lasso`). Lars will be faster if the estimated components are sparse. n_jobs : int, default=None Number of parallel jobs to run. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary ` for more details. batch_size : int, default=3 Number of samples in each mini-batch. .. versionchanged:: 1.3 The default value of `batch_size` will change from 3 to 256 in version 1.3. shuffle : bool, default=True Whether to shuffle the samples before forming batches. dict_init : ndarray of shape (n_components, n_features), default=None Initial value of the dictionary for warm restart scenarios. transform_algorithm : {'lasso_lars', 'lasso_cd', 'lars', 'omp', 'threshold'}, default='omp' Algorithm used to transform the data: - `'lars'`: uses the least angle regression method (`linear_model.lars_path`); - `'lasso_lars'`: uses Lars to compute the Lasso solution. - `'lasso_cd'`: uses the coordinate descent method to compute the Lasso solution (`linear_model.Lasso`). `'lasso_lars'` will be faster if the estimated components are sparse. - `'omp'`: uses orthogonal matching pursuit to estimate the sparse solution. - `'threshold'`: squashes to zero all coefficients less than alpha from the projection ``dictionary * X'``. transform_n_nonzero_coefs : int, default=None Number of nonzero coefficients to target in each column of the solution. This is only used by `algorithm='lars'` and `algorithm='omp'`. If `None`, then `transform_n_nonzero_coefs=int(n_features / 10)`. transform_alpha : float, default=None If `algorithm='lasso_lars'` or `algorithm='lasso_cd'`, `alpha` is the penalty applied to the L1 norm. If `algorithm='threshold'`, `alpha` is the absolute value of the threshold below which coefficients will be squashed to zero. If `None`, defaults to `alpha`. .. versionchanged:: 1.2 When None, default value changed from 1.0 to `alpha`. verbose : bool or int, default=False To control the verbosity of the procedure. split_sign : bool, default=False Whether to split the sparse feature vector into the concatenation of its negative part and its positive part. This can improve the performance of downstream classifiers. random_state : int, RandomState instance or None, default=None Used for initializing the dictionary when ``dict_init`` is not specified, randomly shuffling the data when ``shuffle`` is set to ``True``, and updating the dictionary. Pass an int for reproducible results across multiple function calls. See :term:`Glossary `. positive_code : bool, default=False Whether to enforce positivity when finding the code. .. versionadded:: 0.20 positive_dict : bool, default=False Whether to enforce positivity when finding the dictionary. .. versionadded:: 0.20 transform_max_iter : int, default=1000 Maximum number of iterations to perform if `algorithm='lasso_cd'` or `'lasso_lars'`. .. versionadded:: 0.22 callback : callable, default=None A callable that gets invoked at the end of each iteration. .. versionadded:: 1.1 tol : float, default=1e-3 Control early stopping based on the norm of the differences in the dictionary between 2 steps. Used only if `max_iter` is not None. To disable early stopping based on changes in the dictionary, set `tol` to 0.0. .. versionadded:: 1.1 max_no_improvement : int, default=10 Control early stopping based on the consecutive number of mini batches that does not yield an improvement on the smoothed cost function. Used only if `max_iter` is not None. To disable convergence detection based on cost function, set `max_no_improvement` to None. .. versionadded:: 1.1 Attributes ---------- components_ : ndarray of shape (n_components, n_features) Components extracted from the data. inner_stats_ : tuple of (A, B) ndarrays Internal sufficient statistics that are kept by the algorithm. Keeping them is useful in online settings, to avoid losing the history of the evolution, but they shouldn't have any use for the end user. `A` `(n_components, n_components)` is the dictionary covariance matrix. `B` `(n_features, n_components)` is the data approximation matrix. .. deprecated:: 1.1 `inner_stats_` serves internal purpose only and will be removed in 1.3. n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 n_iter_ : int Number of iterations over the full dataset. iter_offset_ : int The number of iteration on data batches that has been performed before. .. deprecated:: 1.1 `iter_offset_` has been renamed `n_steps_` and will be removed in 1.3. random_state_ : RandomState instance RandomState instance that is generated either from a seed, the random number generattor or by `np.random`. .. deprecated:: 1.1 `random_state_` serves internal purpose only and will be removed in 1.3. n_steps_ : int Number of mini-batches processed. .. versionadded:: 1.1 See Also -------- DictionaryLearning : Find a dictionary that sparsely encodes data. MiniBatchSparsePCA : Mini-batch Sparse Principal Components Analysis. SparseCoder : Find a sparse representation of data from a fixed, precomputed dictionary. SparsePCA : Sparse Principal Components Analysis. References ---------- J. Mairal, F. Bach, J. Ponce, G. Sapiro, 2009: Online dictionary learning for sparse coding (https://www.di.ens.fr/sierra/pdfs/icml09.pdf) Examples -------- >>> import numpy as np >>> from sklearn.datasets import make_sparse_coded_signal >>> from sklearn.decomposition import MiniBatchDictionaryLearning >>> X, dictionary, code = make_sparse_coded_signal( ... n_samples=100, n_components=15, n_features=20, n_nonzero_coefs=10, ... random_state=42, data_transposed=False) >>> dict_learner = MiniBatchDictionaryLearning( ... n_components=15, batch_size=3, transform_algorithm='lasso_lars', ... transform_alpha=0.1, random_state=42) >>> X_transformed = dict_learner.fit_transform(X) We can check the level of sparsity of `X_transformed`: >>> np.mean(X_transformed == 0) < 0.5 True We can compare the average squared euclidean norm of the reconstruction error of the sparse coded signal relative to the squared euclidean norm of the original signal: >>> X_hat = X_transformed @ dict_learner.components_ >>> np.mean(np.sum((X_hat - X) ** 2, axis=1) / np.sum(X ** 2, axis=1)) 0.057... r(Nrrrrr~rrrr&r)rr5r.rp)rRr,rr1rrTrrrvrrrr.rrpr{rzrrxr8rrTFr'rrX)r,rr1rrTrrrvrrrr.rrpr{rzrrxr8rc svt| | | ||||||_||_||_||_||_| |_| |_||_ ||_ ||_ ||_ ||_ ||_||_||_dSr)rrrRr,rr1rrvr.rrrrprzrxrr8)rrRr,rr1rrTrrrvrrrr.rrpr{rzrrxr8rrr#r$rs0 z$MiniBatchDictionaryLearning.__init__zMThe attribute `iter_offset_` is deprecated in 1.1 and will be removed in 1.3.cCs|jSr)rrr#r#r$ iter_offset_#sz(MiniBatchDictionaryLearning.iter_offset_zNThe attribute `random_state_` is deprecated in 1.1 and will be removed in 1.3.cCs|jSr) _random_staterr#r#r$ random_state_*sz)MiniBatchDictionaryLearning.random_state_zMThe attribute `inner_stats_` is deprecated in 1.1 and will be removed in 1.3.cCs|jSr) _inner_statsrr#r#r$ inner_stats_1sz(MiniBatchDictionaryLearning.inner_stats_cCsZ|j|_|jdkr|jd|_t|j|jd|j|_t|drVt|j |jd|_ dS)Nr(r _batch_sizer) rR _n_componentsr<r%rr{_fit_algorithmrr_rrr#r#r$ _check_params8s    z)MiniBatchDictionaryLearning._check_paramscCs|jdk r|j}n,t||j|d\}}}|ddtjf|}|jt|krd|d|jddf}n.t|tj|jt||jdf|j df}t |d|j dd}tj |dd }|S) z!Initialization of the dictionary.Nrr(rrFrrr) rvrr r:r;rjZ concatenaterr<rWr r)rrHrprIrrr#r#r$_initialize_dictFs*   z,MiniBatchDictionaryLearning._initialize_dictc Cs||dkr|d|}n|d|d|}|d||d}|j\}}||9}||j||7}||9}||j||7}dS)zUpdate the inner stats inplace.r(rN)rr>) rrHrdrsteprrrnror#r#r$_update_inner_statscs  z/MiniBatchDictionaryLearning._update_inner_statsc Cs|jd}t|||j|j|jd|j|j|jd }d|||d|jt t ||}| |||||j \}} t ||||| |j||jd|S)z7Perform the update on the dictionary for one minibatch.rFrrrr)r<rer r,rTr{rr.rr:rFrrrsrz) rrHrIrpr rrd batch_costrnror#r#r$_minibatch_stepqs<    z+MiniBatchDictionaryLearning._minibatch_stepc Csl|jd}|d}|td||krH|jrDtd|d|d|dS|jdkrZ||_n.||d} t| d} |jd| || |_|jrtd|d|d|d |jt|||j} |jdkr| |jkr|jrtd |d|d S|j dks|j|j kr d|_ |j|_ n|j d7_ |j dk rh|j |j krh|jrdtd |d|d SdS) aHelper function to encapsulate the early stopping logic. Early stopping is based on two factors: - A small change of the dictionary between two minibatch updates. This is controlled by the tol parameter. - No more improvement on a smoothed estimate of the objective function for a a certain number of consecutive minibatch updates. This is controlled by the max_no_improvement parameter. rr(rtzMinibatch step /z: mean batch cost: FNz , ewa cost: z,Converged (small dictionary change) at step Tz>Converged (lack of improvement in objective function) at step ) r<r_r.rl _ewa_costrrkr r8 _ewa_cost_min_no_improvementr) rrHrZnew_dictold_dictrPr n_stepsrr,Z dict_diffr#r#r$_check_convergencesD      z.MiniBatchDictionaryLearning._check_convergencec Cs||j|_|jdkr,tdtd|_|j|tjtj gddd}| ||j dkrltdt|j }t |j |_|||j}|}|jr|}|j|n|}|j\}}|jrtd tj|j|jf|jd tj||jf|jd f|_|jd k rd |_d |_d |_t||j} t| } t t!||j} |j| } d } t"t#| | D]f\} } || }|$|||j| }|%|||||| | rq|j&d k r|&t'||d d <qZ| d|_(t!|j(| |_)n|j dkrdn|j }t||j} t| } t"t#|| D]v\} } |$|| ||j| |joT| t!d|jd k}|jdksh|rvt| d|j&d k r|&t'q||_(t!|t t!||j|_)||_*|S)rrrrrUF)rWrVrrz'n_iter' is deprecated in version 1.1 and will be removed in version 1.4. Use 'max_iter' and let 'n_iter' to its default value instead. 'n_iter' is also ignored if 'max_iter' is specified.rrNrrr(r'rrXz batches processed.)+rrrrrrrr:r]r^r rr rprr rrr<r.rlrr rWrr1rrrrrrrCrrcrgrrrxrn_steps_rr)rrHrrrIrrrPrQrZn_steps_per_iterrirZX_batchrZtrigger_verboser#r#r$r@s                 zMiniBatchDictionaryLearning.fitcCst|d}|s||j|tjtjgd| d}|dkrNtdt||_ nt |dd|_ |s| |t |j |_|||j}tj|j|jf|jdtj|jd |jf|jdf|_n|j}||||j|j ||_|j d 7_ |S) a2Update the model using the data in X as a mini-batch. Parameters ---------- X : array-like of shape (n_samples, n_features) Training vector, where `n_samples` is the number of samples and `n_features` is the number of features. y : Ignored Not used, present for API consistency by convention. iter_offset : int, default=None The number of iteration on data batches that has been performed before this call to `partial_fit`. This is optional: if no number is passed, the memory of the object is used. .. deprecated:: 1.1 ``iter_offset`` will be removed in 1.3. Returns ------- self : object Return the instance itself. rrU)rWrVrrzM'iter_offset' is deprecated in version 1.1 and will be removed in version 1.3rrrr()rrrr:r]r^rrrrgetattrr r rprr rr rWr<rrr)rrHrrZhas_componentsrIr#r#r$ partial_fitY s8    z'MiniBatchDictionaryLearning.partial_fitcCs |jjdSrrrr#r#r$r sz+MiniBatchDictionaryLearning._n_features_outcCsdtjtjgiSrrrr#r#r$r s z&MiniBatchDictionaryLearning._more_tags)N)N)Nr)rrrrrrrrrr:rcallablerrrrrrrrrr r rrrr@rrrrr#r#rr$rs s   "3   (D | B r) Nr&NTNr'TrF)NNFNF)N)r)7rrrrZnumbersrrrmathrZnumpyr:ZscipyrZjoblibrbaserr r utilsr r r rrZutils._param_validationrrrZ utils.extmathrrrZutils.validationrZutils.parallelrrZ linear_modelrrrrr%rSrersrrrrrrrr#r#r#r$s       J A U i  'IS8