package sklearn
Module Decomposition.DictionaryLearningSource
Source
val create :
?n_components:int ->
?alpha:float ->
?max_iter:int ->
?tol:float ->
?fit_algorithm:[ `Lars | `Cd ] ->
?transform_algorithm:[ `Lasso_lars | `Lasso_cd | `Lars | `Omp | `Threshold ] ->
?transform_n_nonzero_coefs:int ->
?transform_alpha:float ->
?n_jobs:int ->
?code_init:Arr.t ->
?dict_init:Arr.t ->
?verbose:int ->
?split_sign:bool ->
?random_state:int ->
?positive_code:bool ->
?positive_dict:bool ->
?transform_max_iter:int ->
unit ->
tDictionary learning
Finds a dictionary (a set of atoms) that can best be used to represent data using a sparse code.
Solves the optimization problem::
(U^*,V^* ) = argmin 0.5 || Y - U V ||_2^2 + alpha * || U ||_1 (U,V) with || V_k ||_2 = 1 for all 0 <= k < n_components
Read more in the :ref:`User Guide <DictionaryLearning>`.
Parameters ---------- n_components : int, default=n_features number of dictionary elements to extract
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 (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.
.. 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 (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'``
.. versionadded:: 0.17 *lasso_cd* coordinate descent method to improve speed.
transform_n_nonzero_coefs : int, default=0.1*n_features 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.
transform_alpha : float, default=1.0 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`.
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 <n_jobs>` for more details.
code_init : array of shape (n_samples, n_components), default=None initial value for the code, for warm restart
dict_init : array of shape (n_components, n_features), default=None initial values for the dictionary, for warm restart
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 If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`.
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_ : array, n_components, n_features dictionary atoms extracted from the data
error_ : array vector of errors at each iteration
n_iter_ : int Number of iterations run.
Notes ----- **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)
See also -------- SparseCoder MiniBatchDictionaryLearning SparsePCA MiniBatchSparsePCA
Fit the model from data in X.
Parameters ---------- X : array-like, shape (n_samples, n_features) Training vector, where n_samples in the number of samples and n_features is the number of features.
y : Ignored
Returns ------- self : object Returns the object itself
Source
val fit_transform :
?y:Arr.t ->
?fit_params:(string * Py.Object.t) list ->
x:Arr.t ->
t ->
Arr.tFit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
Parameters ---------- X : numpy array of shape n_samples, n_features Training set.
y : numpy array of shape n_samples Target values.
**fit_params : dict Additional fit parameters.
Returns ------- X_new : numpy array of shape n_samples, n_features_new Transformed array.
Get parameters for this estimator.
Parameters ---------- deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns ------- params : mapping of string to any Parameter names mapped to their values.
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form ``<component>__<parameter>`` so that it's possible to update each component of a nested object.
Parameters ---------- **params : dict Estimator parameters.
Returns ------- self : object Estimator instance.
Encode the data as a sparse combination of the dictionary atoms.
Coding method is determined by the object parameter `transform_algorithm`.
Parameters ---------- X : array 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 : array, shape (n_samples, n_components) Transformed data
Pretty-print the object to a formatter.