LDA

Defined in: ds/src/mva/estimators/LDA.js:29

Extends

Classifier

Constructors

Constructor

new LDA(params?): LDA

Defined in: ds/src/mva/estimators/LDA.js:33

Parameters

params?

any = {}

optional hyperparameters (none required for basic LDA)

Returns

LDA

Overrides

Classifier.constructor

Properties

_state

_state: object

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

Inherited from

Classifier._state

_warnings

_warnings: any[]

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

Inherited from

Classifier._warnings

classes_

classes_: any

Defined in: ds/src/core/estimators/estimator.js:515

Inherited from

Classifier.classes_

fitted

fitted: boolean

Defined in: ds/src/mva/estimators/LDA.js:38

Inherited from

Classifier.fitted

labelEncoder_

labelEncoder_: any

Defined in: ds/src/core/estimators/estimator.js:514

Inherited from

Classifier.labelEncoder_

model

model: any

Defined in: ds/src/mva/estimators/LDA.js:37

params

params: any

Defined in: ds/src/mva/estimators/LDA.js:36

Inherited from

Classifier.params

Methods

_accuracy()

_accuracy(yTrue, yPred): number

Defined in: ds/src/core/estimators/estimator.js:644

Parameters

yTrue

any

yPred

any

Returns

number

Inherited from

Classifier._accuracy

_decodeLabels()

_decodeLabels(predictions): any[]

Defined in: ds/src/core/estimators/estimator.js:606

Decode numeric predictions to original labels

Parameters

predictions

any[]

Numeric predictions or label strings

Returns

any[]

Decoded labels (or original if no encoder)

Inherited from

Classifier._decodeLabels

_extractLabelEncoder()

_extractLabelEncoder(prepared): boolean

Defined in: ds/src/core/estimators/estimator.js:541

Extract and store label encoder from prepared data

Parameters

prepared

any

Result from prepareXY/prepareDataset

Returns

boolean

True if encoder was found and stored

Inherited from

Classifier._extractLabelEncoder

_getClasses()

_getClasses(preparedY, onlyPresentClasses?): any

Defined in: ds/src/core/estimators/estimator.js:563

Get unique classes from labels (encoded or raw) If labelEncoder exists, preparedY is assumed to be numeric indices [0, 1, 2, …] Otherwise, creates classes from unique values in preparedY

Parameters

preparedY

any[]

Label array (numeric if encoded, or raw labels)

onlyPresentClasses?

boolean = true

If true, only return classes present in preparedY

Returns

any

{ numericY, classes }

Inherited from

Classifier._getClasses

_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

Classifier._prepareArgsForFit

_repr_html_()

_repr_html_(): string

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

Observable/Jupyter HTML representation

Returns

string

HTML representation

Inherited from

Classifier._repr_html_

clearWarnings()

clearWarnings(): void

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

Clear all warnings

Returns

void

Inherited from

Classifier.clearWarnings

fit()

fit(X, y?, opts?): LDA

Defined in: ds/src/mva/estimators/LDA.js:53

Fit the LDA model.

Supports a positional numeric call (fit(Xarray, yarray)) or a declarative { X, y, data } object (fit({ X: 'col'|'[cols]', y: 'label', data: tableLike, omit_missing, encoders })).

Parameters

X

any

Data matrix (n samples × p features), or a declarative { data, X, y } object

y?

number[] | string[]

Class labels, one per sample (positional call form)

opts?

Fitting options (used for the positional call form)

scale?

boolean

Whether to scale columns to unit variance

scaling?

number

Ordination scaling convention

Returns

LDA

The fitted estimator (for chaining)

Overrides

Classifier.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

Classifier.getMemoryUsage

getParams()

getParams(): any

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

Get a shallow copy of parameters.

Returns

any

Inherited from

Classifier.getParams

getScores()

getScores(type?, scaled?): any

Defined in: ds/src/mva/estimators/LDA.js:149

Retrieve site or variable scores (scaled or raw).

Parameters

type?

"sites" | "samples" | "variables" | "loadings"

scaled?

boolean = true

Returns

any

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

Classifier.getState

getWarnings()

getWarnings(): any[]

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

Get all warnings

Returns

any[]

Array of warning objects

Inherited from

Classifier.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

Classifier.getWarningsByType

hasWarnings()

hasWarnings(): boolean

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

Check if model has warnings

Returns

boolean

Inherited from

Classifier.hasWarnings

isFitted()

isFitted(): boolean

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

Check if model is fitted

Returns

boolean

Inherited from

Classifier.isFitted

predict()

predict(X): number[] | string[]

Defined in: ds/src/mva/estimators/LDA.js:116

Predict class labels for X.

Accepts a numeric array or a declarative object { X: cols, data: tableLike }. Returns decoded labels if a label encoder is present, otherwise numeric predictions.

Parameters

X

any

Data matrix (n samples × p features), or a declarative { data, X } object

Returns

number[] | string[]

Predicted class label for each row

Overrides

Classifier.predict

predictProba()

predictProba(_X): void

Defined in: ds/src/core/estimators/estimator.js:531

Predict probabilities - subclasses should override Ensures model is fitted before prediction

Parameters

_X

any

Returns

void

Inherited from

Classifier.predictProba

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

Classifier.save

score()

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

Defined in: ds/src/core/estimators/estimator.js:622

Default accuracy scoring:

score(yTrue, yPred)
or score({ X, y, data }) which predicts internally

Parameters

yTrueOrOpts

any

yPred

any

_opts?

args?

…any[] = {}

Returns

number

Inherited from

Classifier.score

setParams()

setParams(params?): LDA

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

Set parameters (mutates instance).

Parameters

params?

any = {}

Returns

LDA

Inherited from

Classifier.setParams

summary()

summary(): object

Defined in: ds/src/mva/estimators/LDA.js:130

Return a small summary of the fitted model.

Returns

object

classes

classes: any

eigenvalues

eigenvalues: any

nComponents

nComponents: any

scaling

scaling: any

toJSON()

toJSON(): object

Defined in: ds/src/mva/estimators/LDA.js:175

JSON serialization helper.

Returns

object

class

__class__: string = 'LDA'

fitted

fitted: boolean

model

model: any

params

params: any

Overrides

Classifier.toJSON

transform()

transform(X): any[]

Defined in: ds/src/mva/estimators/LDA.js:100

Transform input X to discriminant scores (delegates to functional transform).

Accepts a numeric array or a declarative object { X: cols, data: tableLike }.

Parameters

X

any

Data matrix (n samples × p features), or a declarative { data, X } object

Returns

any[]

Discriminant score objects, one per row, keyed by axis (e.g. ld1, ld2, …)

Overrides

Classifier.transform

fromJSON()

static fromJSON(obj?): LDA

Defined in: ds/src/mva/estimators/LDA.js:187

Restore an instance from JSON produced by toJSON().

Parameters

obj?

Returns

LDA

Overrides

Classifier.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

Classifier.load