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preprocessing.MinMaxScaler

Usage

import { MinMaxScaler } from 'machinelearn/preprocessing';

const minmaxScaler = new MinMaxScaler({ featureRange: [0, 1] });

// Fitting an 1D matrix
minmaxScaler.fit([4, 5, 6]);
const result = minmaxScaler.transform([4, 5, 6]);
// result = [ 0, 0.5, 1 ]

// Fitting a 2D matrix
const minmaxScaler2 = new MinMaxScaler({ featureRange: [0, 1] });
minmaxScaler2.fit([[1, 2, 3], [4, 5, 6]]);
const result2 = minmaxScaler2.transform([[1, 2, 3]]);
// result2 = [ [ 0, 0.2, 0.4000000000000001 ] ]

Constructors

  • constructor

Methods

  • fit

  • fit_transform

  • inverse_transform

  • transform

Constructors


constructor

⊕ MinMaxScaler(featureRange: `object`)

Defined in preprocessing/data.ts:415

Parameters:

ParamTypeDefaultDescription
options.featureRangenumber[]...

Returns: MinMaxScaler

Methods


λ fit

Compute the minimum and maximum to be used for later scaling.

Defined in preprocessing/data.ts:431

Parameters:

ParamTypeDefaultDescription
Xnumber[] or number[][]nullArray or sparse-matrix data input

Returns:

void

λ fit_transform

Fit to data, then transform it.

Defined in preprocessing/data.ts:459

Parameters:

ParamTypeDefaultDescription
Xnumber[] or number[][]Original input vector

Returns:

λ inverse_transform

Undo the scaling of X according to feature_range.

Defined in preprocessing/data.ts:488

Parameters:

ParamTypeDefaultDescription
Xnumber[]nullScaled input vector

Returns:

number[]

λ transform

Scaling features of X according to feature_range.

Defined in preprocessing/data.ts:468

Parameters:

ParamTypeDefaultDescription
Xnumber[] or number[][]nullOriginal input vector

Returns:

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