val of_pyobject : Py.Object.t -> tval to_pyobject : t -> Py.Object.tval create :
?n_components:int ->
?alpha:float ->
?n_iter:int ->
?fit_algorithm:[ `Lars | `Cd ] ->
?n_jobs:[ `Int of int | `None ] ->
?batch_size:int ->
?shuffle:bool ->
?dict_init:Ndarray.t ->
?transform_algorithm:[ `Lasso_lars | `Lasso_cd | `Lars | `Omp | `Threshold ] ->
?transform_n_nonzero_coefs:[ `Int of int | `PyObject of Py.Object.t ] ->
?transform_alpha:float ->
?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 ->
tMini-batch dictionary 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, number of dictionary elements to extract
alpha : float, sparsity controlling parameter
n_iter : int, total number of iterations to perform
fit_algorithm : 'lars', 'cd' 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 or None, optional (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.
batch_size : int, number of samples in each mini-batch
shuffle : bool, whether to shuffle the samples before forming batches
dict_init : array of shape (n_components, n_features), initial value of the dictionary for warm restart scenarios
transform_algorithm : 'lasso_lars', 'lasso_cd', 'lars', 'omp', 'threshold' 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, ``0.1 * n_features`` by default 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, 1. by default 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`.
verbose : bool, optional (default: False) To control the verbosity of the procedure.
split_sign : bool, False by default 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, optional (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 Whether to enforce positivity when finding the code.
.. versionadded:: 0.20
positive_dict : bool Whether to enforce positivity when finding the dictionary.
.. versionadded:: 0.20
transform_max_iter : int, optional (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 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
n_iter_ : int Number of iterations run.
iter_offset_ : int The number of iteration on data batches that has been performed before.
random_state_ : RandomState RandomState instance that is generated either from a seed, the random number generattor or by `np.random`.
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 DictionaryLearning 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 instance 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 partial_fit : ?y:Py.Object.t -> ?iter_offset:int -> x:Ndarray.t -> t -> tUpdates the model using the data in X as a mini-batch.
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
iter_offset : integer, optional 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.
Returns ------- self : object Returns the instance itself.
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 inner_stats_ : t -> Py.Object.tAttribute inner_stats_: see constructor for documentation
val n_iter_ : t -> intAttribute n_iter_: see constructor for documentation
val iter_offset_ : t -> intAttribute iter_offset_: see constructor for documentation
val random_state_ : t -> Py.Object.tAttribute random_state_: 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.