GaussianProcessRegressor

Defined in: ds/src/ml/estimators/GaussianProcessRegressor.js:204

Extends

• Regressor

Constructors

Constructor

new GaussianProcessRegressor(opts?): GaussianProcessRegressor

Defined in: ds/src/ml/estimators/GaussianProcessRegressor.js:217

Parameters

opts?

Options

alpha

number

Noise level / regularization (default: 1e-10)

kernel

string | Kernel

Kernel instance or type (‘rbf’, ‘periodic’, ‘rational_quadratic’)

lengthScale

number

Length scale for kernel (default: 1.0)

noiseLevel

number

Alias for alpha

normalizeY

boolean

Standardize the target (center + scale to unit variance) before fitting; predictions, std, covariance and posterior samples are back-transformed. Alias: normalize_y (default: false)

period

number

Period for periodic kernel

variance

number

Signal variance (default: 1.0)

Returns

GaussianProcessRegressor

Overrides

Regressor.constructor

Properties

_alphaVector

_alphaVector: any[]

Defined in: ds/src/ml/estimators/GaussianProcessRegressor.js:277

---

_L

_L: any

Defined in: ds/src/ml/estimators/GaussianProcessRegressor.js:276

---

_seed

_seed: any

Defined in: ds/src/ml/estimators/GaussianProcessRegressor.js:271

---

_state

_state: object

Defined in: ds/src/core/estimators/estimator.js:27

Inherited from

Regressor._state

---

_warnings

_warnings: any[]

Defined in: ds/src/core/estimators/estimator.js:29

Inherited from

Regressor._warnings

---

_XTrain

_XTrain: Matrix

Defined in: ds/src/ml/estimators/GaussianProcessRegressor.js:274

---

_yMean

_yMean: number

Defined in: ds/src/ml/estimators/GaussianProcessRegressor.js:260

---

_yStd

_yStd: number

Defined in: ds/src/ml/estimators/GaussianProcessRegressor.js:261

---

_yTrain

_yTrain: any[] | number[]

Defined in: ds/src/ml/estimators/GaussianProcessRegressor.js:275

---

alpha

alpha: number

Defined in: ds/src/ml/estimators/GaussianProcessRegressor.js:252

---

fitted

fitted: boolean

Defined in: ds/src/core/estimators/estimator.js:25

Inherited from

Regressor.fitted

---

kernel

kernel: RBF | Kernel | Periodic | RationalQuadratic | Matern | ConstantKernel

Defined in: ds/src/ml/estimators/GaussianProcessRegressor.js:222

---

logMarginalLikelihood_

logMarginalLikelihood_: number

Defined in: ds/src/ml/estimators/GaussianProcessRegressor.js:278

---

normalizeY

normalizeY: any

Defined in: ds/src/ml/estimators/GaussianProcessRegressor.js:259

---

nRestarts

nRestarts: any

Defined in: ds/src/ml/estimators/GaussianProcessRegressor.js:270

---

optimize

optimize: boolean

Defined in: ds/src/ml/estimators/GaussianProcessRegressor.js:267

---

params

params: any

Defined in: ds/src/core/estimators/estimator.js:24

Inherited from

Regressor.params

Methods

_backSubstitution()

_backSubstitution(L, b): any[]

Defined in: ds/src/ml/estimators/GaussianProcessRegressor.js:606

Parameters

L

any

b

any

Returns

any[]

---

_computePosteriorCovariance()

_computePosteriorCovariance(XTest, KStar): object

Defined in: ds/src/ml/estimators/GaussianProcessRegressor.js:543

Parameters

XTest

any

KStar

any

Returns

object

covarianceMatrix

covarianceMatrix: any

diag

diag: any[]

---

_forwardSubstitution()

_forwardSubstitution(L, b): any[]

Defined in: ds/src/ml/estimators/GaussianProcessRegressor.js:591

Parameters

L

any

b

any

Returns

any[]

---

_prepareArgsForFit()

_prepareArgsForFit(args?): { columns?: undefined; columnsX: any[]; prepared: boolean; raw?: undefined; rows: any[]; X: any[][]; y: any[]; } | { columns: any[]; columnsX?: undefined; prepared: boolean; raw?: undefined; rows: any[]; X: any[][]; y?: undefined; } | { columns?: undefined; columnsX?: undefined; prepared?: undefined; raw: any[]; rows?: undefined; X?: undefined; y?: undefined; }

Defined in: ds/src/core/estimators/estimator.js:367

Convenience helper: parse arguments passed to fit/predict/transform.

Supports declarative table-style inputs:

• fit({ X, y, data, omit_missing })
• fit({ data, columns, … })

Returns an object { X, y, prepared, rows } where X/y are numeric arrays if preparation was required, otherwise returns the original values.

Note: this helper only prepares numeric matrices/vectors using core table utilities; it does not perform encoding of categorical predictors.

Parameters

args?

any[] = []

Returns

{ columns?: undefined; columnsX: any[]; prepared: boolean; raw?: undefined; rows: any[]; X: any[][]; y: any[]; } | { columns: any[]; columnsX?: undefined; prepared: boolean; raw?: undefined; rows: any[]; X: any[][]; y?: undefined; } | { columns?: undefined; columnsX?: undefined; prepared?: undefined; raw: any[]; rows?: undefined; X?: undefined; y?: undefined; }

Inherited from

Regressor._prepareArgsForFit

---

_r2()

_r2(yTrue, yPred): number

Defined in: ds/src/core/estimators/estimator.js:489

Parameters

yTrue

any

yPred

any

Returns

number

Inherited from

Regressor._r2

---

_repr_html_()

_repr_html_(): string

Defined in: ds/src/core/estimators/estimator.js:201

Observable/Jupyter HTML representation

Returns

string

HTML representation

Inherited from

Regressor._repr_html_

---

_solveCholesky()

_solveCholesky(L, y): any[]

Defined in: ds/src/ml/estimators/GaussianProcessRegressor.js:586

Parameters

L

any

y

any

Returns

any[]

---

clearWarnings()

clearWarnings(): void

Defined in: ds/src/core/estimators/estimator.js:139

Clear all warnings

Returns

void

Inherited from

Regressor.clearWarnings

---

fit()

fit(X, y?): GaussianProcessRegressor

Defined in: ds/src/ml/estimators/GaussianProcessRegressor.js:288

Fit the GP to training data

Parameters

X

any

Training inputs (n samples × d features), or a declarative spec { X, columns, y, data, omit_missing }

y?

number[] = null

Training targets (n)

Returns

GaussianProcessRegressor

The fitted estimator (for chaining)

Overrides

Regressor.fit

---

getMemoryUsage()

getMemoryUsage(): string

Defined in: ds/src/core/estimators/estimator.js:97

Get memory usage in human-readable format

Returns

string

Memory usage string (e.g., “2.3 MB” or “145 KB”)

Inherited from

Regressor.getMemoryUsage

---

getParams()

getParams(): any

Defined in: ds/src/core/estimators/estimator.js:294

Get a shallow copy of parameters.

Returns

any

Inherited from

Regressor.getParams

---

getState()

getState(): any

Defined in: ds/src/core/estimators/estimator.js:65

Get comprehensive model state

Returns

any

State information including fitted status, memory estimate, warnings

Inherited from

Regressor.getState

---

getWarnings()

getWarnings(): any[]

Defined in: ds/src/core/estimators/estimator.js:124

Get all warnings

Returns

any[]

Array of warning objects

Inherited from

Regressor.getWarnings

---

getWarningsByType()

getWarningsByType(type): any[]

Defined in: ds/src/core/estimators/estimator.js:148

Get warnings of a specific type

Parameters

type

string

Warning type

Returns

any[]

Filtered warnings

Inherited from

Regressor.getWarningsByType

---

hasWarnings()

hasWarnings(): boolean

Defined in: ds/src/core/estimators/estimator.js:132

Check if model has warnings

Returns

boolean

Inherited from

Regressor.hasWarnings

---

isFitted()

isFitted(): boolean

Defined in: ds/src/core/estimators/estimator.js:36

Check if model is fitted

Returns

boolean

Inherited from

Regressor.isFitted

---

logMarginalLikelihood()

logMarginalLikelihood(): number

Defined in: ds/src/ml/estimators/GaussianProcessRegressor.js:360

Log marginal likelihood of the training data under the current hyperparameters: log p(y|X) = -½ yᵀK⁻¹y - ½ log|K| - n/2 log(2π). Requires the model to have seen training data (via fit).

Returns

number

---

predict()

predict(X, opts?): number[] | { covariance?: number[][]; mean: number[]; std?: number[]; }

Defined in: ds/src/ml/estimators/GaussianProcessRegressor.js:439

Predict at test points

Parameters

X

number[][]

Test inputs (m samples × d features)

opts?

Options

returnCov?

boolean

Return the full posterior covariance

returnStd?

boolean

Return per-point standard deviations

Returns

number[] | { covariance?: number[][]; mean: number[]; std?: number[]; }

Predicted means, or an object with mean and std/covariance when requested

Overrides

Regressor.predict

---

sample()

sample(X, nSamples?, seed?): any[][]

Defined in: ds/src/ml/estimators/GaussianProcessRegressor.js:483

Sample from the posterior distribution

Parameters

X

any[]

Test inputs

nSamples?

number = 1

Number of samples

seed?

number = null

Random seed for reproducibility

Returns

any[][]

Array of samples

---

samplePrior()

samplePrior(X, nSamples?, seed?): any[][]

Defined in: ds/src/ml/estimators/GaussianProcessRegressor.js:520

Sample from the prior (unfitted GP)

Parameters

X

any[]

Input points

nSamples?

number = 1

Number of samples

seed?

number = null

Random seed for reproducibility

Returns

any[][]

Array of samples

---

save()

save(): string

Defined in: ds/src/core/estimators/estimator.js:329

Save model to JSON string

Returns

string

JSON representation of the model

Inherited from

Regressor.save

---

score()

score(yTrueOrOpts, yPred, _opts?, …args?): number

Defined in: ds/src/core/estimators/estimator.js:461

Default R^2 scoring implementation: 1 - SS_res / SS_tot

Accepts either:

• arrays: score(yTrue, yPred)
• table-style: score({ X, y, data }) where predict will be called internally

Parameters

yTrueOrOpts

any

yPred

any

_opts?

args?

…any[] = {}

Returns

number

Inherited from

Regressor.score

---

setParams()

setParams(params?): GaussianProcessRegressor

Defined in: ds/src/core/estimators/estimator.js:285

Set parameters (mutates instance).

Parameters

params?

any = {}

Returns

GaussianProcessRegressor

Inherited from

Regressor.setParams

---

toJSON()

toJSON(): object

Defined in: ds/src/ml/estimators/GaussianProcessRegressor.js:621

Serialize minimal model metadata. Subclasses may override to include learned parameters.

Returns

object

alpha

alpha: number

alphaVector

alphaVector: any[]

fitted

fitted: boolean

kernel

kernel: object

kernel.params

params: any

kernel.type

type: string

L

L: any

normalizeY

normalizeY: any

type

type: string = 'GaussianProcessRegressor'

XTrain

XTrain: number[][]

yMean

yMean: number

yStd

yStd: number

yTrain

yTrain: any[] | number[]

Overrides

Regressor.toJSON

---

transform()

transform(): void

Defined in: ds/src/core/estimators/estimator.js:431

Transform should be implemented by transformers.

Returns

void

Inherited from

Regressor.transform

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fromJSON()

static fromJSON(json): GaussianProcessRegressor

Defined in: ds/src/ml/estimators/GaussianProcessRegressor.js:640

Basic deserialization. Subclasses should override if they need to restore learned arrays / matrices.

Parameters

json

any

Returns

GaussianProcessRegressor

Overrides

Regressor.fromJSON

---

load()

static load(jsonString): Estimator

Defined in: ds/src/core/estimators/estimator.js:346

Load model from JSON string

Parameters

jsonString

string

JSON representation

Returns

Estimator

Reconstructed estimator instance

Inherited from

Regressor.load