Function normalize

sklearn/preprocessing/_data.py:1978–2089

Scale input vectors individually to unit norm (vector length). Read more in the :ref:`User Guide `. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The data to normalize, element by element. scipy.s

(X, norm="l2", *, axis=1, copy=True, return_norm=False)

    prefer_skip_nested_validation=True,
)
def normalize(X, norm="l2", *, axis=1, copy=True, return_norm=False):
    """Scale input vectors individually to unit norm (vector length).

    Read more in the :ref:`User Guide <preprocessing_normalization>`.

    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.

    copy : bool, 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_norm : bool, default=False
        Whether to return the computed norms.

    Returns
    -------
    X : {ndarray, sparse matrix} of shape (n_samples, n_features)
        Normalized input X.

    norms : ndarray 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 :class:`~sklearn.pipeline.Pipeline`).

    Notes
    -----
    For a comparison of the different scalers, transformers, and normalizers,
    see: :ref:`sphx_glr_auto_examples_preprocessing_plot_all_scaling.py`.

    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],