val of_pyobject : Py.Object.t -> tval to_pyobject : t -> Py.Object.tval 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 of int | `None ] ->
?code_init:Ndarray.t ->
?dict_init:Ndarray.t ->
?verbose:bool ->
?split_sign:bool ->
?random_state:[ `Int of int | `RandomState of Py.Object.t | `None ] ->
?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
val fit : ?y:Py.Object.t -> x:Ndarray.t -> t -> tFit 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
val fit_transform :
?y:Ndarray.t ->
?fit_params:(string * Py.Object.t) list ->
x:Ndarray.t ->
t ->
Ndarray.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.
val get_params : ?deep:bool -> t -> Py.Object.tGet 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.
val set_params : ?params:(string * Py.Object.t) list -> t -> tSet 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
val n_iter_ : t -> intAttribute n_iter_: see constructor for documentation
val to_string : t -> stringPrint the object to a human-readable representation.
val show : t -> stringPrint the object to a human-readable representation.
val pp : Format.formatter -> t -> unitPretty-print the object to a formatter.