Class: 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:

Constructors

new MiniBatchDictionaryLearning()

new MiniBatchDictionaryLearning(opts?): MiniBatchDictionaryLearning

Parameters

Parameter	Type	Description
`opts`?	`object`	-
`opts.alpha`?	`number`	Sparsity controlling parameter.
`opts.batch_size`?	`number`	Number of samples in each mini-batch.
`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:
`opts.max_iter`?	`number`	Maximum number of iterations over the complete dataset before stopping independently of any early stopping criterion heuristics.
`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. To disable convergence detection based on cost function, set `max_no_improvement` to `undefined`.
`opts.n_components`?	`number`	Number of dictionary elements to extract.
`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.
`opts.positive_dict`?	`boolean`	Whether to enforce positivity when finding the dictionary.
`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.
`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.
`opts.tol`?	`number`	Control early stopping based on the norm of the differences in the dictionary between 2 steps. To disable early stopping based on changes in the dictionary, set `tol` to 0.0.
`opts.transform_algorithm`?	`"threshold"` \| `"lars"` \| `"lasso_lars"` \| `"lasso_cd"` \| `"omp"`	Algorithm used to transform the data:
`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'`.
`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.

Returns MiniBatchDictionaryLearning

Defined in generated/decomposition/MiniBatchDictionaryLearning.ts:25

Properties

Property	Type	Default value	Defined in
`_isDisposed`	`boolean`	`false`	generated/decomposition/MiniBatchDictionaryLearning.ts:23
`_isInitialized`	`boolean`	`false`	generated/decomposition/MiniBatchDictionaryLearning.ts:22
`_py`	`PythonBridge`	`undefined`	generated/decomposition/MiniBatchDictionaryLearning.ts:21
`id`	`string`	`undefined`	generated/decomposition/MiniBatchDictionaryLearning.ts:18
`opts`	`any`	`undefined`	generated/decomposition/MiniBatchDictionaryLearning.ts:19

Accessors

components_

Get Signature

get components_(): Promise<ArrayLike[]>

Components extracted from the data.

Returns Promise<ArrayLike[]>

Defined in generated/decomposition/MiniBatchDictionaryLearning.ts:520

feature_names_in_

Get Signature

get feature_names_in_(): Promise<ArrayLike>

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

Returns Promise<ArrayLike>

Defined in generated/decomposition/MiniBatchDictionaryLearning.ts:574

n_features_in_

Get Signature

get n_features_in_(): Promise<number>

Number of features seen during fit.

Returns Promise<number>

Defined in generated/decomposition/MiniBatchDictionaryLearning.ts:547

n_iter_

Get Signature

get n_iter_(): Promise<number>

Number of iterations over the full dataset.

Returns Promise<number>

Defined in generated/decomposition/MiniBatchDictionaryLearning.ts:601

n_steps_

Get Signature

get n_steps_(): Promise<number>

Number of mini-batches processed.

Returns Promise<number>

Defined in generated/decomposition/MiniBatchDictionaryLearning.ts:628

py

Get Signature

get py(): PythonBridge

Returns PythonBridge

Set Signature

set py(pythonBridge): void

Parameters

Parameter	Type
`pythonBridge`	`PythonBridge`

Returns void

Defined in generated/decomposition/MiniBatchDictionaryLearning.ts:165

Methods

dispose()

dispose(): Promise<void>

Disposes of the underlying Python resources.

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

Returns Promise<void>

Defined in generated/decomposition/MiniBatchDictionaryLearning.ts:221

fit()

fit(opts): Promise<any>

Fit the model from data in X.

Parameters

Parameter	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:238

fit_transform()

fit_transform(opts): Promise<any[]>

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters

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

Returns Promise<any[]>

Defined in generated/decomposition/MiniBatchDictionaryLearning.ts:281

get_feature_names_out()

get_feature_names_out(opts): Promise<any>

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: \["class_name0", "class_name1", "class_name2"\].

Parameters

Parameter	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:329

get_metadata_routing()

get_metadata_routing(opts): Promise<any>

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Parameters

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

Returns Promise<any>

Defined in generated/decomposition/MiniBatchDictionaryLearning.ts:367

init()

init(py): Promise<void>

Initializes the underlying Python resources.

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

Parameters

Parameter	Type
`py`	`PythonBridge`

Returns Promise<void>

Defined in generated/decomposition/MiniBatchDictionaryLearning.ts:178

partial_fit()

partial_fit(opts): Promise<any>

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

Parameters

Parameter	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.