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
Methods
Constructors
constructor
⊕ MinMaxScaler(featureRange: `object`)
Defined in preprocessing/data.ts:415
Parameters:
Param | Type | Default | Description |
---|---|---|---|
options.featureRange | number[] | ... |
Returns: MinMaxScaler
Methods
λ fit
Compute the minimum and maximum to be used for later scaling.
Defined in preprocessing/data.ts:431
Parameters:
Param | Type | Default | Description |
---|---|---|---|
X | number[] or number[][] | null | Array or sparse-matrix data input |
Returns:
void
λ fit_transform
Fit to data, then transform it.
Defined in preprocessing/data.ts:459
Parameters:
Param | Type | Default | Description |
---|---|---|---|
X | number[] 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:
Param | Type | Default | Description |
---|---|---|---|
X | number[] | null | Scaled input vector |
Returns:
number[]
λ transform
Scaling features of X according to feature_range.
Defined in preprocessing/data.ts:468
Parameters:
Param | Type | Default | Description |
---|---|---|---|
X | number[] or number[][] | null | Original input vector |
Returns: