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MiniBatchDictionaryLearning

Mini-batch dictionary learning.

Finds a dictionary (a set of atoms) that performs well at sparsely encoding the fitted data.

Solves the optimization problem:

Python Reference

Constructors

constructor()

Signature

new MiniBatchDictionaryLearning(opts?: object): MiniBatchDictionaryLearning;

Parameters

Name Type Description
opts? object -
opts.alpha? number Sparsity controlling parameter. Default Value 1
opts.batch_size? number Number of samples in each mini-batch. Default Value 256
opts.callback? any A callable that gets invoked at the end of each iteration.
opts.dict_init? ArrayLike[] Initial value of the dictionary for warm restart scenarios.
opts.fit_algorithm? "cd" | "lars" The algorithm used: Default Value 'lars'
opts.max_iter? number Maximum number of iterations over the complete dataset before stopping independently of any early stopping criterion heuristics. If max\_iter is not undefined, n\_iter is ignored.
opts.max_no_improvement? number 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 undefined. To disable convergence detection based on cost function, set max\_no\_improvement to undefined. Default Value 10
opts.n_components? number Number of dictionary elements to extract.
opts.n_iter? number Total number of iterations over data batches to perform. Default Value 1000
opts.n_jobs? number Number of parallel jobs to run. undefined means 1 unless in a joblib.parallel\_backend context. \-1 means using all processors. See Glossary for more details.
opts.positive_code? boolean Whether to enforce positivity when finding the code. Default Value false
opts.positive_dict? boolean Whether to enforce positivity when finding the dictionary. Default Value false
opts.random_state? number 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 Glossary.
opts.shuffle? boolean Whether to shuffle the samples before forming batches. Default Value true
opts.split_sign? boolean 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. Default Value false
opts.tol? number Control early stopping based on the norm of the differences in the dictionary between 2 steps. Used only if max\_iter is not undefined. To disable early stopping based on changes in the dictionary, set tol to 0.0. Default Value 0.001
opts.transform_algorithm? "threshold" | "lars" | "lasso_lars" | "lasso_cd" | "omp" Algorithm used to transform the data: Default Value 'omp'
opts.transform_alpha? number 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 undefined, defaults to alpha.
opts.transform_max_iter? number Maximum number of iterations to perform if algorithm='lasso\_cd' or 'lasso\_lars'. Default Value 1000
opts.transform_n_nonzero_coefs? number Number of nonzero coefficients to target in each column of the solution. This is only used by algorithm='lars' and algorithm='omp'. If undefined, then transform\_n\_nonzero\_coefs=int(n\_features / 10).
opts.verbose? number | boolean To control the verbosity of the procedure. Default Value false

Returns

MiniBatchDictionaryLearning

Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:25

Methods

dispose()

Disposes of the underlying Python resources.

Once dispose() is called, the instance is no longer usable.

Signature

dispose(): Promise<void>;

Returns

Promise<void>

Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:255

fit()

Fit the model from data in X.

Signature

fit(opts: object): Promise<any>;

Parameters

Name Type Description
opts object -
opts.X? ArrayLike[] Training vector, where n\_samples is the number of samples and n\_features is the number of features.
opts.y? any Not used, present for API consistency by convention.

Returns

Promise<any>

Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:272

fit_transform()

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit\_params and returns a transformed version of X.

Signature

fit_transform(opts: object): Promise<any[]>;

Parameters

Name Type Description
opts object -
opts.X? ArrayLike[] Input samples.
opts.fit_params? any Additional fit parameters.
opts.y? ArrayLike Target values (undefined for unsupervised transformations).

Returns

Promise<any[]>

Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:316

get_feature_names_out()

Get output feature names for transformation.

The feature names out will prefixed by the lowercased class name. For example, if the transformer outputs 3 features, then the feature names out are: \["class\_name0", "class\_name1", "class\_name2"\].

Signature

get_feature_names_out(opts: object): Promise<any>;

Parameters

Name Type Description
opts object -
opts.input_features? any Only used to validate feature names with the names seen in fit.

Returns

Promise<any>

Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:370

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Signature

get_metadata_routing(opts: object): Promise<any>;

Parameters

Name Type Description
opts object -
opts.routing? any A MetadataRequest encapsulating routing information.

Returns

Promise<any>

Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:410

init()

Initializes the underlying Python resources.

This instance is not usable until the Promise returned by init() resolves.

Signature

init(py: PythonBridge): Promise<void>;

Parameters

Name Type
py PythonBridge

Returns

Promise<void>

Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:183

partial_fit()

Update the model using the data in X as a mini-batch.

Signature

partial_fit(opts: object): Promise<any>;

Parameters

Name Type Description
opts object -
opts.X? ArrayLike[] Training vector, where n\_samples is the number of samples and n\_features is the number of features.
opts.y? any Not used, present for API consistency by convention.

Returns

Promise<any>

Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:448

set_output()

Set output container.

See Introducing the set_output API for an example on how to use the API.

Signature

set_output(opts: object): Promise<any>;

Parameters

Name Type Description
opts object -
opts.transform? "default" | "pandas" Configure output of transform and fit\_transform.

Returns

Promise<any>

Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:493

transform()

Encode the data as a sparse combination of the dictionary atoms.

Coding method is determined by the object parameter transform\_algorithm.

Signature

transform(opts: object): Promise<ArrayLike[]>;

Parameters

Name Type Description
opts object -
opts.X? ArrayLike[] Test data to be transformed, must have the same number of features as the data used to train the model.

Returns

Promise<ArrayLike[]>

Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:533

Properties

_isDisposed

boolean = false

Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:23

_isInitialized

boolean = false

Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:22

_py

PythonBridge

Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:21

id

string

Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:18

opts

any

Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:19

Accessors

components_

Components extracted from the data.

Signature

components_(): Promise<ArrayLike[]>;

Returns

Promise<ArrayLike[]>

Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:571

feature_names_in_

Names of features seen during fit. Defined only when X has feature names that are all strings.

Signature

feature_names_in_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:625

n_features_in_

Number of features seen during fit.

Signature

n_features_in_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:598

n_iter_

Number of iterations over the full dataset.

Signature

n_iter_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:652

n_steps_

Number of mini-batches processed.

Signature

n_steps_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:679

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:170

Signature

py(pythonBridge: PythonBridge): void;

Parameters

Name Type
pythonBridge PythonBridge

Returns

void

Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:174