Normalizer¶
Scales a set of features so that it has unit norm.
This is particularly useful when used after a feature_extraction.TFIDF
.
Parameters¶
-
order
Default →
2
Order of the norm (e.g. 2 corresponds to the \(L^2\) norm).
Examples¶
from river import preprocessing
from river import stream
scaler = preprocessing.Normalizer(order=2)
X = [[4, 1, 2, 2],
[1, 3, 9, 3],
[5, 7, 5, 1]]
for x, _ in stream.iter_array(X):
print(scaler.transform_one(x))
{0: 0.8, 1: 0.2, 2: 0.4, 3: 0.4}
{0: 0.1, 1: 0.3, 2: 0.9, 3: 0.3}
{0: 0.5, 1: 0.7, 2: 0.5, 3: 0.1}
Methods¶
learn_one
Update with a set of features x
.
A lot of transformers don't actually have to do anything during the learn_one
step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the learn_one
can override this method.
Parameters
- x — 'dict'
transform_one
Transform a set of features x
.
Parameters
- x — 'dict'
Returns
dict: The transformed values.