Class: SkewedChi2Sampler
Approximate feature map for “skewed chi-squared” kernel.
Read more in the User Guide.
Constructors
new SkewedChi2Sampler()
new SkewedChi2Sampler(
opts?):SkewedChi2Sampler
Parameters
| Parameter | Type | Description |
|---|---|---|
opts? | object | - |
opts.n_components? | number | Number of Monte Carlo samples per original feature. Equals the dimensionality of the computed feature space. |
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. |
opts.skewedness? | number | “skewedness” parameter of the kernel. Needs to be cross-validated. |
Returns SkewedChi2Sampler
Defined in generated/kernel_approximation/SkewedChi2Sampler.ts:23
Properties
| Property | Type | Default value | Defined in |
|---|---|---|---|
_isDisposed | boolean | false | generated/kernel_approximation/SkewedChi2Sampler.ts:21 |
_isInitialized | boolean | false | generated/kernel_approximation/SkewedChi2Sampler.ts:20 |
_py | PythonBridge | undefined | generated/kernel_approximation/SkewedChi2Sampler.ts:19 |
id | string | undefined | generated/kernel_approximation/SkewedChi2Sampler.ts:16 |
opts | any | undefined | generated/kernel_approximation/SkewedChi2Sampler.ts:17 |
Accessors
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/kernel_approximation/SkewedChi2Sampler.ts:434
n_features_in_
Get Signature
get n_features_in_():
Promise<number>
Number of features seen during fit.
Returns Promise<number>
Defined in generated/kernel_approximation/SkewedChi2Sampler.ts:407
py
Get Signature
get py():
PythonBridge
Returns PythonBridge
Set Signature
set py(
pythonBridge):void
Parameters
| Parameter | Type |
|---|---|
pythonBridge | PythonBridge |
Returns void
Defined in generated/kernel_approximation/SkewedChi2Sampler.ts:47
random_offset_
Get Signature
get random_offset_():
Promise<ArrayLike[]>
Bias term, which will be added to the data. It is uniformly distributed between 0 and 2*pi.
Returns Promise<ArrayLike[]>
Defined in generated/kernel_approximation/SkewedChi2Sampler.ts:380
random_weights_
Get Signature
get random_weights_():
Promise<ArrayLike[]>
Weight array, sampled from a secant hyperbolic distribution, which will be used to linearly transform the log of the data.
Returns Promise<ArrayLike[]>
Defined in generated/kernel_approximation/SkewedChi2Sampler.ts:353
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/kernel_approximation/SkewedChi2Sampler.ts:101
fit()
fit(
opts):Promise<any>
Fit the model with X.
Samples random projection according to n_features.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | 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/SkewedChi2Sampler.ts:120
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/kernel_approximation/SkewedChi2Sampler.ts:161
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/kernel_approximation/SkewedChi2Sampler.ts:209
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/kernel_approximation/SkewedChi2Sampler.ts:247
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/kernel_approximation/SkewedChi2Sampler.ts:60
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/kernel_approximation/SkewedChi2Sampler.ts:285
transform()
transform(
opts):Promise<ArrayLike>
Apply the approximate feature map to X.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | New data, where n_samples is the number of samples and n_features is the number of features. All values of X must be strictly greater than “-skewedness”. |
Returns Promise<ArrayLike>
Defined in generated/kernel_approximation/SkewedChi2Sampler.ts:319