normalize#

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

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

Parameters:

X{array-like, sparse matrix}, shape [n_samples, n_features]: The data to normalize, element by element. Please provide CSC matrix to normalize on axis 0, conversely provide CSR matrix to normalize on axis 1
norm‘l1’, ‘l2’, or ‘max’, optional (‘l2’ by default): The norm to use to normalize each non zero sample (or each non-zero feature if axis is 0).
axis0 or 1, optional (1 by default): axis used to normalize the data along. If 1, independently normalize each sample, otherwise (if 0) normalize each feature.
copyboolean, optional, default True: Whether a forced copy will be triggered. If copy=False, a copy might be triggered by a conversion.
return_normboolean, default False: whether to return the computed norms

Returns:

X{array-like, sparse matrix}, shape [n_samples, n_features]: Normalized input X.
normsarray, 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’.