Class: MinMaxScaler

Transform features by scaling each feature to a given range.

This estimator scales and translates each feature individually such that it is in the given range on the training set, e.g. between zero and one.

The transformation is given by:

Python Reference

Constructors

new MinMaxScaler()

new MinMaxScaler(opts?): MinMaxScaler

Parameters

Parameter	Type	Description
`opts`?	`object`	-
`opts.clip`?	`boolean`	Set to `true` to clip transformed values of held-out data to provided `feature range`.
`opts.copy`?	`boolean`	Set to `false` to perform inplace row normalization and avoid a copy (if the input is already a numpy array).
`opts.feature_range`?	`any`	Desired range of transformed data.

Returns MinMaxScaler

Defined in generated/preprocessing/MinMaxScaler.ts:25

Properties

Property	Type	Default value	Defined in
`_isDisposed`	`boolean`	`false`	generated/preprocessing/MinMaxScaler.ts:23
`_isInitialized`	`boolean`	`false`	generated/preprocessing/MinMaxScaler.ts:22
`_py`	`PythonBridge`	`undefined`	generated/preprocessing/MinMaxScaler.ts:21
`id`	`string`	`undefined`	generated/preprocessing/MinMaxScaler.ts:18
`opts`	`any`	`undefined`	generated/preprocessing/MinMaxScaler.ts:19

Accessors

data_max_

Get Signature

get data_max_(): Promise<ArrayLike>

Per feature maximum seen in the data

Returns Promise<ArrayLike>

Defined in generated/preprocessing/MinMaxScaler.ts:479

data_min_

Get Signature

get data_min_(): Promise<ArrayLike>

Per feature minimum seen in the data

Returns Promise<ArrayLike>

Defined in generated/preprocessing/MinMaxScaler.ts:454

data_range_

Get Signature

get data_range_(): Promise<ArrayLike>

Per feature range (data_max_ \- data_min_) seen in the data

Returns Promise<ArrayLike>

Defined in generated/preprocessing/MinMaxScaler.ts:504

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/preprocessing/MinMaxScaler.ts:579

min_

Get Signature

get min_(): Promise<ArrayLike>

Per feature adjustment for minimum. Equivalent to min \- X.min(axis=0) \* self.scale_

Returns Promise<ArrayLike>

Defined in generated/preprocessing/MinMaxScaler.ts:408

n_features_in_

Get Signature

get n_features_in_(): Promise<number>

Number of features seen during fit.

Returns Promise<number>

Defined in generated/preprocessing/MinMaxScaler.ts:529

n_samples_seen_

Get Signature

get n_samples_seen_(): Promise<number>

The number of samples processed by the estimator. It will be reset on new calls to fit, but increments across partial_fit calls.

Returns Promise<number>

Defined in generated/preprocessing/MinMaxScaler.ts:554

py

Get Signature

get py(): PythonBridge

Returns PythonBridge

Set Signature

set py(pythonBridge): void

Parameters

Parameter	Type
`pythonBridge`	`PythonBridge`

Returns void

Defined in generated/preprocessing/MinMaxScaler.ts:49

scale_

Get Signature

get scale_(): Promise<ArrayLike>

Per feature relative scaling of the data. Equivalent to (max \- min) / (X.max(axis=0) \- X.min(axis=0))

Returns Promise<ArrayLike>

Defined in generated/preprocessing/MinMaxScaler.ts:431

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/preprocessing/MinMaxScaler.ts:101

fit()

fit(opts): Promise<any>

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

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`ArrayLike`[]	The data used to compute the per-feature minimum and maximum used for later scaling along the features axis.
`opts.y`?	`any`	Ignored.

Returns Promise<any>

Defined in generated/preprocessing/MinMaxScaler.ts:118

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/preprocessing/MinMaxScaler.ts:157

get_feature_names_out()

get_feature_names_out(opts): Promise<any>

Get output feature names for transformation.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.input_features`?	`any`	Input features.

Returns Promise<any>

Defined in generated/preprocessing/MinMaxScaler.ts:199

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/preprocessing/MinMaxScaler.ts:235

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/preprocessing/MinMaxScaler.ts:62

inverse_transform()

inverse_transform(opts): Promise<ArrayLike[]>

Undo the scaling of X according to feature_range.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`ArrayLike`[]	Input data that will be transformed. It cannot be sparse.

Returns Promise<ArrayLike[]>

Defined in generated/preprocessing/MinMaxScaler.ts:269

partial_fit()

partial_fit(opts): Promise<any>

Online computation of min and max on X for later scaling.

All of X is processed as a single batch. This is intended for cases when fit is not feasible due to very large number of n_samples or because X is read from a continuous stream.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`ArrayLike`[]	The data used to compute the mean and standard deviation used for later scaling along the features axis.
`opts.y`?	`any`	Ignored.