normalize (original) (raw)

sklearn.preprocessing.normalize(X, norm='l2', *, axis=1, copy=True, return_norm=False)[source]#

Scale input vectors individually to unit norm (vector length).

Read more in the User Guide.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features)

The data to normalize, element by element. scipy.sparse matrices should be in CSR format to avoid an un-necessary copy.

norm{‘l1’, ‘l2’, ‘max’}, default=’l2’

The norm to use to normalize each non zero sample (or each non-zero feature if axis is 0).

axis{0, 1}, default=1

Define axis used to normalize the data along. If 1, independently normalize each sample, otherwise (if 0) normalize each feature.

copybool, default=True

If False, try to avoid a copy and normalize in place. This is not guaranteed to always work in place; e.g. if the data is a numpy array with an int dtype, a copy will be returned even with copy=False.

return_normbool, default=False

Whether to return the computed norms.

Returns:

X{ndarray, sparse matrix} of shape (n_samples, n_features)

Normalized input X.

normsndarray of shape (n_samples, ) if axis=1 else (n_features, )

An array of norms along given axis for X. When X is sparse, a NotImplementedError will be raised for norm ‘l1’ or ‘l2’.

See also

Normalizer

Performs normalization using the Transformer API (e.g. as part of a preprocessing Pipeline).

Notes

For a comparison of the different scalers, transformers, and normalizers, see: Compare the effect of different scalers on data with outliers.

Examples

from sklearn.preprocessing import normalize X = [[-2, 1, 2], [-1, 0, 1]] normalize(X, norm="l1") # L1 normalization each row independently array([[-0.4, 0.2, 0.4], [-0.5, 0. , 0.5]]) normalize(X, norm="l2") # L2 normalization each row independently array([[-0.67, 0.33, 0.67], [-0.71, 0. , 0.71]])