RBFSampler
Approximate a RBF kernel feature map using random Fourier features.
It implements a variant of Random Kitchen Sinks.[1]
Read more in the User Guide.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new RBFSampler(opts?: object): RBFSampler;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.gamma? | number | "scale" | Parameter of RBF kernel: exp(-gamma * x^2). If gamma='scale' is passed then it uses 1 / (n_features * X.var()) as value of gamma. Default Value 1 |
opts.n_components? | number | Number of Monte Carlo samples per original feature. Equals the dimensionality of the computed feature space. Default Value 100 |
opts.random_state? | number | Pseudo-random number generator to control the generation of the random weights and random offset when fitting the training data. Pass an int for reproducible output across multiple function calls. See Glossary. |
Returns
Defined in: generated/kernel_approximation/RBFSampler.ts:25 (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/kernel_approximation/RBFSampler.ts:104 (opens in a new tab)
fit()
Fit the model with X.
Samples random projection according to n_features.
Signature
fit(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | Training data, where n\_samples is the number of samples and n\_features is the number of features. |
opts.y? | ArrayLike | Target values (undefined for unsupervised transformations). |
Returns
Promise<any>
Defined in: generated/kernel_approximation/RBFSampler.ts:123 (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/kernel_approximation/RBFSampler.ts:163 (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/kernel_approximation/RBFSampler.ts:212 (opens in a new tab)
get_metadata_routing()
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Signature
get_metadata_routing(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.routing? | any | A MetadataRequest encapsulating routing information. |
Returns
Promise<any>
Defined in: generated/kernel_approximation/RBFSampler.ts:250 (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/kernel_approximation/RBFSampler.ts:62 (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/kernel_approximation/RBFSampler.ts:287 (opens in a new tab)
transform()
Apply the approximate feature map to X.
Signature
transform(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | New data, where n\_samples is the number of samples and n\_features is the number of features. |
Returns
Promise<ArrayLike>
Defined in: generated/kernel_approximation/RBFSampler.ts:320 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/kernel_approximation/RBFSampler.ts:23 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/kernel_approximation/RBFSampler.ts:22 (opens in a new tab)
_py
PythonBridge
Defined in: generated/kernel_approximation/RBFSampler.ts:21 (opens in a new tab)
id
string
Defined in: generated/kernel_approximation/RBFSampler.ts:18 (opens in a new tab)
opts
any
Defined in: generated/kernel_approximation/RBFSampler.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/kernel_approximation/RBFSampler.ts:428 (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/kernel_approximation/RBFSampler.ts:403 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/kernel_approximation/RBFSampler.ts:49 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/kernel_approximation/RBFSampler.ts:53 (opens in a new tab)
random_offset_
Random offset used to compute the projection in the n\_components dimensions of the feature space.
Signature
random_offset_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/kernel_approximation/RBFSampler.ts:353 (opens in a new tab)
random_weights_
Random projection directions drawn from the Fourier transform of the RBF kernel.
Signature
random_weights_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/kernel_approximation/RBFSampler.ts:378 (opens in a new tab)