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Classes
SparseCoder

SparseCoder

Sparse coding.

Finds a sparse representation of data against a fixed, precomputed dictionary.

Each row of the result is the solution to a sparse coding problem. The goal is to find a sparse array code such that:

Python Reference (opens in a new tab)

Constructors

constructor()

Signature

new SparseCoder(opts?: object): SparseCoder;

Parameters

NameTypeDescription
opts?object-
opts.dictionary?ArrayLike[]The dictionary atoms used for sparse coding. Lines are assumed to be normalized to unit norm.
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.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.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 algorithm='omp', alpha is the tolerance parameter: the value of the reconstruction error targeted. In this case, it overrides n\_nonzero\_coefs. If undefined, default to 1.
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' and is overridden by alpha in the omp case. If undefined, then transform\_n\_nonzero\_coefs=int(n\_features / 10).

Returns

SparseCoder

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

Properties

_isDisposed

boolean = false

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

_isInitialized

boolean = false

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

_py

PythonBridge

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

id

string

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

opts

any

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

Accessors

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/SparseCoder.ts:367 (opens in a new tab)

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/decomposition/SparseCoder.ts:83 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

NameType
pythonBridgePythonBridge

Returns

void

Defined in: generated/decomposition/SparseCoder.ts:87 (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/SparseCoder.ts:148 (opens in a new tab)

fit()

Do nothing and return the estimator unchanged.

This method is just there to implement the usual API and hence work in pipelines.

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.X?anyNot used, present for API consistency by convention.
opts.y?anyNot used, present for API consistency by convention.

Returns

Promise<any>

Defined in: generated/decomposition/SparseCoder.ts:167 (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/SparseCoder.ts:207 (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/SparseCoder.ts:256 (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/SparseCoder.ts:96 (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/SparseCoder.ts:294 (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[]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<ArrayLike[]>

Defined in: generated/decomposition/SparseCoder.ts:329 (opens in a new tab)