Class: Isomap

Isomap Embedding.

Non-linear dimensionality reduction through Isometric Mapping

Constructors

new Isomap()

new Isomap(opts?): Isomap

Parameters

Parameter	Type	Description
`opts`?	`object`	-
`opts.eigen_solver`?	`"auto"` \| `"arpack"` \| `"dense"`	‘auto’ : Attempt to choose the most efficient solver for the given problem. ‘arpack’ : Use Arnoldi decomposition to find the eigenvalues and eigenvectors. ‘dense’ : Use a direct solver (i.e. LAPACK) for the eigenvalue decomposition.
`opts.max_iter`?	`number`	Maximum number of iterations for the arpack solver. not used if eigen_solver == ‘dense’.
`opts.metric`?	`any`	The metric to use when calculating distance between instances in a feature array. If metric is a string or callable, it must be one of the options allowed by `sklearn.metrics.pairwise_distances` for its metric parameter. If metric is “precomputed”, X is assumed to be a distance matrix and must be square. X may be a Glossary.
`opts.metric_params`?	`any`	Additional keyword arguments for the metric function.
`opts.n_components`?	`number`	Number of coordinates for the manifold.
`opts.n_jobs`?	`number`	The 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.n_neighbors`?	`number`	Number of neighbors to consider for each point. If `n_neighbors` is an int, then `radius` must be `undefined`.
`opts.neighbors_algorithm`?	`"auto"` \| `"ball_tree"` \| `"kd_tree"` \| `"brute"`	Algorithm to use for nearest neighbors search, passed to neighbors.NearestNeighbors instance.
`opts.p`?	`number`	Parameter for the Minkowski metric from sklearn.metrics.pairwise.pairwise_distances. When p = 1, this is equivalent to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.
`opts.path_method`?	`"auto"` \| `"FW"` \| `"D"`	Method to use in finding shortest path. ‘auto’ : attempt to choose the best algorithm automatically. ‘FW’ : Floyd-Warshall algorithm. ‘D’ : Dijkstra’s algorithm.
`opts.radius`?	`number`	Limiting distance of neighbors to return. If `radius` is a float, then `n_neighbors` must be set to `undefined`.
`opts.tol`?	`number`	Convergence tolerance passed to arpack or lobpcg. not used if eigen_solver == ‘dense’.

Returns Isomap

Defined in generated/manifold/Isomap.ts:25

Properties

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

Accessors

dist_matrix_

Get Signature

get dist_matrix_(): Promise<ArrayLike>

Stores the geodesic distance matrix of training data.

Returns Promise<ArrayLike>

Defined in generated/manifold/Isomap.ts:493

embedding_

Get Signature

get embedding_(): Promise<ArrayLike>

Stores the embedding vectors.

Returns Promise<ArrayLike>

Defined in generated/manifold/Isomap.ts:425

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/manifold/Isomap.ts:539

kernel_pca_

Get Signature

get kernel_pca_(): Promise<any>

KernelPCA object used to implement the embedding.

Returns Promise<any>

Defined in generated/manifold/Isomap.ts:448

n_features_in_

Get Signature

get n_features_in_(): Promise<number>

Number of features seen during fit.

Returns Promise<number>

Defined in generated/manifold/Isomap.ts:516

nbrs_

Get Signature

get nbrs_(): Promise<any>

Stores nearest neighbors instance, including BallTree or KDtree if applicable.

Returns Promise<any>

Defined in generated/manifold/Isomap.ts:471

py

Get Signature

get py(): PythonBridge

Returns PythonBridge

Set Signature

set py(pythonBridge): void

Parameters

Parameter	Type
`pythonBridge`	`PythonBridge`

Returns void

Defined in generated/manifold/Isomap.ts:116

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/manifold/Isomap.ts:167

fit()

fit(opts): Promise<any>

Compute the embedding vectors for data X.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`any`	Sample data, shape = (n_samples, n_features), in the form of a numpy array, sparse matrix, precomputed tree, or NearestNeighbors object.
`opts.y`?	`any`	Not used, present for API consistency by convention.

Returns Promise<any>

Defined in generated/manifold/Isomap.ts:184

fit_transform()

fit_transform(opts): Promise<ArrayLike>

Fit the model from data in X and transform X.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`any`	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<ArrayLike>

Defined in generated/manifold/Isomap.ts:221

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/manifold/Isomap.ts:260

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/manifold/Isomap.ts:294

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/manifold/Isomap.ts:129

reconstruction_error()

reconstruction_error(opts): Promise<any>

Compute the reconstruction error for the embedding.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.reconstruction_error`?	`number`	Reconstruction error.

Returns Promise<any>

Defined in generated/manifold/Isomap.ts:326

set_output()

set_output(opts): Promise<any>

Set output container.

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

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.transform`?	`"default"` \| `"pandas"` \| `"polars"`	Configure output of `transform` and `fit_transform`.

Returns Promise<any>

Defined in generated/manifold/Isomap.ts:360

transform()

transform(opts): Promise<ArrayLike>

Transform X.

This is implemented by linking the points X into the graph of geodesic distances of the training data. First the n_neighbors nearest neighbors of X are found in the training data, and from these the shortest geodesic distances from each point in X to each point in the training data are computed in order to construct the kernel. The embedding of X is the projection of this kernel onto the embedding vectors of the training set.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`any`	If neighbors_algorithm=’precomputed’, X is assumed to be a distance matrix or a sparse graph of shape (n_queries, n_samples_fit).

Returns Promise<ArrayLike>

Defined in generated/manifold/Isomap.ts:394

IsolationForest IsotonicRegression