Class: NeighborhoodComponentsAnalysis

Neighborhood Components Analysis.

Neighborhood Component Analysis (NCA) is a machine learning algorithm for metric learning. It learns a linear transformation in a supervised fashion to improve the classification accuracy of a stochastic nearest neighbors rule in the transformed space.

Constructors

new NeighborhoodComponentsAnalysis()

new NeighborhoodComponentsAnalysis(opts?): NeighborhoodComponentsAnalysis

Parameters

Parameter	Type	Description
`opts`?	`object`	-
`opts.callback`?	`any`	If not `undefined`, this function is called after every iteration of the optimizer, taking as arguments the current solution (flattened transformation matrix) and the number of iterations. This might be useful in case one wants to examine or store the transformation found after each iteration.
`opts.init`?	`ArrayLike`[] \| `"auto"` \| `"random"` \| `"identity"` \| `"pca"` \| `"lda"`	Initialization of the linear transformation. Possible options are `'auto'`, `'pca'`, `'lda'`, `'identity'`, `'random'`, and a numpy array of shape `(n_features_a, n_features_b)`.
`opts.max_iter`?	`number`	Maximum number of iterations in the optimization.
`opts.n_components`?	`number`	Preferred dimensionality of the projected space. If `undefined` it will be set to `n_features`.
`opts.random_state`?	`number`	A pseudo random number generator object or a seed for it if int. If `init='random'`, `random_state` is used to initialize the random transformation. If `init='pca'`, `random_state` is passed as an argument to PCA when initializing the transformation. Pass an int for reproducible results across multiple function calls. See Glossary.
`opts.tol`?	`number`	Convergence tolerance for the optimization.
`opts.verbose`?	`number`	If 0, no progress messages will be printed. If 1, progress messages will be printed to stdout. If > 1, progress messages will be printed and the `disp` parameter of `scipy.optimize.minimize` will be set to `verbose \- 2`.
`opts.warm_start`?	`boolean`	If `true` and `fit` has been called before, the solution of the previous call to `fit` is used as the initial linear transformation (`n_components` and `init` will be ignored).

Returns NeighborhoodComponentsAnalysis

Defined in generated/neighbors/NeighborhoodComponentsAnalysis.ts:25

Properties

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

Accessors

components_

Get Signature

get components_(): Promise<ArrayLike[]>

The linear transformation learned during fitting.

Returns Promise<ArrayLike[]>

Defined in generated/neighbors/NeighborhoodComponentsAnalysis.ts:392

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/neighbors/NeighborhoodComponentsAnalysis.ts:500

n_features_in_

Get Signature

get n_features_in_(): Promise<number>

Number of features seen during fit.

Returns Promise<number>

Defined in generated/neighbors/NeighborhoodComponentsAnalysis.ts:419

n_iter_

Get Signature

get n_iter_(): Promise<number>

Counts the number of iterations performed by the optimizer.

Returns Promise<number>

Defined in generated/neighbors/NeighborhoodComponentsAnalysis.ts:446

py

Get Signature

get py(): PythonBridge

Returns PythonBridge

Set Signature

set py(pythonBridge): void

Parameters

Parameter	Type
`pythonBridge`	`PythonBridge`

Returns void

Defined in generated/neighbors/NeighborhoodComponentsAnalysis.ts:80

random_state_

Get Signature

get random_state_(): Promise<any>

Pseudo random number generator object used during initialization.

Returns Promise<any>

Defined in generated/neighbors/NeighborhoodComponentsAnalysis.ts:473

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/neighbors/NeighborhoodComponentsAnalysis.ts:136

fit()

fit(opts): Promise<any>

Fit the model according to the given training data.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`ArrayLike`[]	The training samples.
`opts.y`?	`ArrayLike`	The corresponding training labels.

Returns Promise<any>

Defined in generated/neighbors/NeighborhoodComponentsAnalysis.ts:153

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/neighbors/NeighborhoodComponentsAnalysis.ts:196

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