pyts.preprocessing.MinMaxScaler

class pyts.preprocessing.MinMaxScaler(sample_range=(0, 1))

Transforms samples by scaling each sample to a given range.

Parameters:

sample_range : tuple (min, max) (default = (0, 1))

Desired range of transformed data.

Examples

>>> from pyts.preprocessing import MinMaxScaler
>>> X = [[1, 5, 3, 2, 9, 6, 4, 7],
...      [1, -2, 3, 2, 2, 1, 0, 2]]
>>> scaler = MinMaxScaler()
>>> scaler.transform(X)
array([[0.   , 0.5  , 0.25 , 0.125, 1.   , 0.625, 0.375, 0.75 ],
       [0.6  , 0.   , 1.   , 0.8  , 0.8  , 0.6  , 0.4  , 0.8  ]])

Methods

__init__(self[, sample_range])	Initialize self.
fit(self[, X, y])	Pass.
fit_transform(self, X[, y])	Fit to data, then transform it.
get_params(self[, deep])	Get parameters for this estimator.
set_params(self, \*\*params)	Set the parameters of this estimator.
transform(self, X)	Scale samples of X according to sample_range.

__init__(self, sample_range=(0, 1))

Initialize self. See help(type(self)) for accurate signature.

fit(self, X=None, y=None)

Pass.

Parameters:

X

Ignored

y

Ignored

Returns:

self : object

fit_transform(self, X, y=None, **fit_params)

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:

X : numpy array of shape [n_samples, n_features]

Training set.

y : numpy array of shape [n_samples]

Target values.

**fit_params : dict

Additional fit parameters.

Returns:

X_new : numpy array of shape [n_samples, n_features_new]

Transformed array.

get_params(self, deep=True)

Get parameters for this estimator.

Parameters:

deep : bool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params : mapping of string to any

Parameter names mapped to their values.

set_params(self, **params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**params : dict

Estimator parameters.

Returns:

self : object

Estimator instance.

transform(self, X)

Scale samples of X according to sample_range.

Parameters:

X : array-like, shape = (n_samples, n_timestamps)

Data to scale.

Returns:

X_new : array-like, shape = (n_samples, n_timestamps)

Scaled data.

Examples using pyts.preprocessing.MinMaxScaler

Scalers