`sklearn.preprocessing`.Normalizer¶

class sklearn.preprocessing.Normalizer(norm='l2', *, copy=True)[source]¶

Normalize samples individually to unit norm.

Each sample (i.e. each row of the data matrix) with at least one non zero component is rescaled independently of other samples so that its norm (l1, l2 or inf) equals one.

This transformer is able to work both with dense numpy arrays and scipy.sparse matrix (use CSR format if you want to avoid the burden of a copy / conversion).

Scaling inputs to unit norms is a common operation for text classification or clustering for instance. For instance the dot product of two l2-normalized TF-IDF vectors is the cosine similarity of the vectors and is the base similarity metric for the Vector Space Model commonly used by the Information Retrieval community.

See also

normalize: Equivalent function without the estimator API.

Notes

This estimator is stateless (besides constructor parameters), the fit method does nothing but is useful when used in a pipeline.

For a comparison of the different scalers, transformers, and normalizers, see examples/preprocessing/plot_all_scaling.py.

Examples

>>> from sklearn.preprocessing import Normalizer
>>> X = [[4, 1, 2, 2],
...      [1, 3, 9, 3],
...      [5, 7, 5, 1]]
>>> transformer = Normalizer().fit(X)  # fit does nothing.
>>> transformer
Normalizer()
>>> transformer.transform(X)
array([[0.8, 0.2, 0.4, 0.4],
       [0.1, 0.3, 0.9, 0.3],
       [0.5, 0.7, 0.5, 0.1]])

Methods

`fit`(X[, y])	Do nothing and return the estimator unchanged.
`fit_transform`(X[, y])	Fit to data, then transform it.
`get_params`([deep])	Get parameters for this estimator.
`set_params`(**params)	Set the parameters of this estimator.
`transform`(X[, copy])	Scale each non zero row of X to unit norm.

fit(X, y=None)[source]¶

Do nothing and return the estimator unchanged.

This method is just there to implement the usual API and hence work in pipelines.

Parameters

X{array-like, sparse matrix} of shape (n_samples, n_features): The data to estimate the normalization parameters.
yIgnored: Not used, present here for API consistency by convention.

Returns

selfobject: Fitted transformer.

fit_transform(X, y=None, **fit_params)[source]¶

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

Xarray-like of shape (n_samples, n_features): Input samples.
yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None: Target values (None for unsupervised transformations).
**fit_paramsdict: Additional fit parameters.

Returns

X_newndarray array of shape (n_samples, n_features_new): Transformed array.

get_params(deep=True)[source]¶

Get parameters for this estimator.

Parameters

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

Returns

paramsdict: Parameter names mapped to their values.

set_params(**params)[source]¶

Set the parameters of this estimator.

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

Parameters

**paramsdict: Estimator parameters.

Returns

selfestimator instance: Estimator instance.

transform(X, copy=None)[source]¶

Scale each non zero row of X to unit norm.

Parameters

X{array-like, sparse matrix} of shape (n_samples, n_features): The data to normalize, row by row. scipy.sparse matrices should be in CSR format to avoid an un-necessary copy.
copybool, default=None: Copy the input X or not.

Returns

X_tr{ndarray, sparse matrix} of shape (n_samples, n_features): Transformed array.

Examples using `sklearn.preprocessing.Normalizer`¶

sklearn.preprocessing.Normalizer¶

Examples using sklearn.preprocessing.Normalizer¶

`sklearn.preprocessing`.Normalizer¶

Examples using `sklearn.preprocessing.Normalizer`¶