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. Read more in the User Guide.  Parameters 
 
norm{‘l1’, ‘l2’, ‘max’}, default=’l2’ 

The norm to use to normalize each non zero sample. If norm=’max’ is used, values will be rescaled by the maximum of the absolute values.  
copybool, default=True 

set to False to perform inplace row normalization and avoid a copy (if the input is already a numpy array or a scipy.sparse CSR matrix).      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.  
yNone 

Ignored.    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
 
  Scalable learning with polynomial kernel aproximation  

  Compare the effect of different scalers on data with outliers  

  Clustering text documents using k-means