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Classes
MiniBatchDictionaryLearning

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 (opens in a new tab)

Constructors

constructor()

Signature

new MiniBatchDictionaryLearning(opts?: object): MiniBatchDictionaryLearning;

Parameters

NameTypeDescription
opts?object-
opts.alpha?numberSparsity controlling parameter. Default Value 1
opts.batch_size?numberNumber of samples in each mini-batch. Default Value 3
opts.callback?anyA 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?numberMaximum 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?numberControl 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?numberNumber of dictionary elements to extract.
opts.n_iter?numberTotal number of iterations over data batches to perform. Default Value 1000
opts.n_jobs?numberNumber of parallel jobs to run. undefined means 1 unless in a joblib.parallel\_backend (opens in a new tab) context. \-1 means using all processors. See Glossary for more details.
opts.positive_code?booleanWhether to enforce positivity when finding the code. Default Value false
opts.positive_dict?booleanWhether to enforce positivity when finding the dictionary. Default Value false
opts.random_state?numberUsed 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?booleanWhether to shuffle the samples before forming batches. Default Value true
opts.split_sign?booleanWhether 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?numberControl 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?numberIf 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?numberMaximum number of iterations to perform if algorithm='lasso\_cd' or 'lasso\_lars'. Default Value 1000
opts.transform_n_nonzero_coefs?numberNumber 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 | booleanTo control the verbosity of the procedure. Default Value false

Returns

MiniBatchDictionaryLearning

Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:25 (opens in a new tab)

Properties

_isDisposed

boolean = false

Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:23 (opens in a new tab)

_isInitialized

boolean = false

Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:22 (opens in a new tab)

_py

PythonBridge

Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:21 (opens in a new tab)

id

string

Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:18 (opens in a new tab)

opts

any

Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:19 (opens in a new tab)

Accessors

components_

Components extracted from the data.

Signature

components_(): Promise<ArrayLike[]>;

Returns

Promise<ArrayLike[]>

Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:538 (opens in a new tab)

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:619 (opens in a new tab)

inner_stats_

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.

Signature

inner_stats_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:565 (opens in a new tab)

iter_offset_

The number of iteration on data batches that has been performed before.

Signature

iter_offset_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:673 (opens in a new tab)

n_features_in_

Number of features seen during fit.

Signature

n_features_in_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:592 (opens in a new tab)

n_iter_

Number of iterations over the full dataset.

Signature

n_iter_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:646 (opens in a new tab)

n_steps_

Number of mini-batches processed.

Signature

n_steps_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:727 (opens in a new tab)

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:170 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

NameType
pythonBridgePythonBridge

Returns

void

Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:174 (opens in a new tab)

random_state_

RandomState instance that is generated either from a seed, the random number generattor or by np.random.

Signature

random_state_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:700 (opens in a new tab)

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 (opens in a new tab)

fit()

Fit the model from data in X.

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLike[]Training vector, where n\_samples is the number of samples and n\_features is the number of features.
opts.y?anyNot used, present for API consistency by convention.

Returns

Promise<any>

Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:272 (opens in a new tab)

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

NameTypeDescription
optsobject-
opts.X?ArrayLike[]Input samples.
opts.fit_params?anyAdditional fit parameters.
opts.y?ArrayLikeTarget values (undefined for unsupervised transformations).

Returns

Promise<any[]>

Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:316 (opens in a new tab)

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

NameTypeDescription
optsobject-
opts.input_features?anyOnly used to validate feature names with the names seen in fit.

Returns

Promise<any>

Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:370 (opens in a new tab)

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

NameType
pyPythonBridge

Returns

Promise<void>

Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:183 (opens in a new tab)

partial_fit()

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

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLike[]Training vector, where n\_samples is the number of samples and n\_features is the number of features.
opts.iter_offset?numberThe 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.
opts.y?anyNot used, present for API consistency by convention.

Returns

Promise<any>

Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:408 (opens in a new tab)

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

NameTypeDescription
optsobject-
opts.transform?"default" | "pandas"Configure output of transform and fit\_transform.

Returns

Promise<any>

Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:460 (opens in a new tab)

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

NameTypeDescription
optsobject-
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:500 (opens in a new tab)