GaussianRandomProjection
Reduce dimensionality through Gaussian random projection.
The components of the random matrix are drawn from N(0, 1 / n_components).
Read more in the User Guide.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new GaussianRandomProjection(opts?: object): GaussianRandomProjection;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.compute_inverse_components? | boolean | Learn the inverse transform by computing the pseudo-inverse of the components during fit. Note that computing the pseudo-inverse does not scale well to large matrices. Default Value false |
opts.eps? | number | Parameter to control the quality of the embedding according to the Johnson-Lindenstrauss lemma when n\_components is set to ‘auto’. The value should be strictly positive. Smaller values lead to better embedding and higher number of dimensions (n_components) in the target projection space. Default Value 0.1 |
opts.n_components? | number | "auto" | Dimensionality of the target projection space. n_components can be automatically adjusted according to the number of samples in the dataset and the bound given by the Johnson-Lindenstrauss lemma. In that case the quality of the embedding is controlled by the eps parameter. It should be noted that Johnson-Lindenstrauss lemma can yield very conservative estimated of the required number of components as it makes no assumption on the structure of the dataset. Default Value 'auto' |
opts.random_state? | number | Controls the pseudo random number generator used to generate the projection matrix at fit time. Pass an int for reproducible output across multiple function calls. See Glossary. |
Returns
Defined in: generated/random_projection/GaussianRandomProjection.ts:25 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/random_projection/GaussianRandomProjection.ts:23 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/random_projection/GaussianRandomProjection.ts:22 (opens in a new tab)
_py
PythonBridge
Defined in: generated/random_projection/GaussianRandomProjection.ts:21 (opens in a new tab)
id
string
Defined in: generated/random_projection/GaussianRandomProjection.ts:18 (opens in a new tab)
opts
any
Defined in: generated/random_projection/GaussianRandomProjection.ts:19 (opens in a new tab)
Accessors
components_
Random matrix used for the projection.
Signature
components_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/random_projection/GaussianRandomProjection.ts:417 (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/random_projection/GaussianRandomProjection.ts:498 (opens in a new tab)
inverse_components_
Pseudo-inverse of the components, only computed if compute\_inverse\_components is true.
Signature
inverse_components_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/random_projection/GaussianRandomProjection.ts:444 (opens in a new tab)
n_components_
Concrete number of components computed when n_components=”auto”.
Signature
n_components_(): Promise<number>;Returns
Promise<number>
Defined in: generated/random_projection/GaussianRandomProjection.ts:390 (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/random_projection/GaussianRandomProjection.ts:471 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/random_projection/GaussianRandomProjection.ts:62 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/random_projection/GaussianRandomProjection.ts:66 (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/random_projection/GaussianRandomProjection.ts:121 (opens in a new tab)
fit()
Generate a sparse random projection matrix.
Signature
fit(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | Training set: only the shape is used to find optimal random matrix dimensions based on the theory referenced in the afore mentioned papers. |
opts.y? | any | Not used, present here for API consistency by convention. |
Returns
Promise<any>
Defined in: generated/random_projection/GaussianRandomProjection.ts:138 (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
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Input samples. |
opts.fit_params? | any | Additional fit parameters. |
opts.y? | ArrayLike | Target values (undefined for unsupervised transformations). |
Returns
Promise<any[]>
Defined in: generated/random_projection/GaussianRandomProjection.ts:180 (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
| Name | 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/random_projection/GaussianRandomProjection.ts:234 (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
| Name | Type |
|---|---|
py | PythonBridge |
Returns
Promise<void>
Defined in: generated/random_projection/GaussianRandomProjection.ts:75 (opens in a new tab)
inverse_transform()
Project data back to its original space.
Returns an array X_original whose transform would be X. Note that even if X is sparse, X_original is dense: this may use a lot of RAM.
If compute\_inverse\_components is false, the inverse of the components is computed during each call to inverse\_transform which can be costly.
Signature
inverse_transform(opts: object): Promise<ArrayLike[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | Data to be transformed back. |
Returns
Promise<ArrayLike[]>
Defined in: generated/random_projection/GaussianRandomProjection.ts:276 (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
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.transform? | "default" | "pandas" | Configure output of transform and fit\_transform. |
Returns
Promise<any>
Defined in: generated/random_projection/GaussianRandomProjection.ts:316 (opens in a new tab)
transform()
Project the data by using matrix product with the random matrix.
Signature
transform(opts: object): Promise<ArrayLike[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | The input data to project into a smaller dimensional space. |
Returns
Promise<ArrayLike[]>
Defined in: generated/random_projection/GaussianRandomProjection.ts:353 (opens in a new tab)