RBFSampler¶
Extracts random features which approximate an RBF kernel.
This is a powerful way to give non-linear capacity to linear classifiers. This method is also called "random Fourier features" in the literature.
Parameters¶
-
gamma
Default →
1.0RBF kernel parameter in
(-gamma * x^2). -
n_components
Default →
100Number of samples per original feature. Equals the dimensionality of the computed feature space.
-
seed
Type → int | None
Default →
NoneRandom number seed.
Examples¶
from river import feature_extraction as fx
from river import linear_model as lm
from river import optim
from river import stream
X = [[0, 0], [1, 1], [1, 0], [0, 1]]
Y = [0, 0, 1, 1]
model = lm.LogisticRegression(optimizer=optim.SGD(.1))
for x, y in stream.iter_array(X, Y):
model = model.learn_one(x, y)
y_pred = model.predict_one(x)
print(y, int(y_pred))
0 0
0 0
1 0
1 1
model = (
fx.RBFSampler(seed=3) |
lm.LogisticRegression(optimizer=optim.SGD(.1))
)
for x, y in stream.iter_array(X, Y):
model = model.learn_one(x, y)
y_pred = model.predict_one(x)
print(y, int(y_pred))
0 0
0 0
1 1
1 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'
Returns
Transformer: self
transform_one
Transform a set of features x.
Parameters
- x — 'dict'
- y — defaults to
None
Returns
dict: The transformed values.