RBFSampler (Radial Basis Function Kernel)

Kernel:

f(x) = \exp(-\gamma \cdot x^2)

from sklearn.kernel_approximation import RBFSampler
from sklearn.linear_model import SGDClassifier

X = [
    [0, 0],
    [1, 1],
    [1, 0],
    [0, 1],
]

y = [
    0,
    0,
    1,
    1,
]

rbfSampler = RBFSampler(
    # -------------------------------------------------------------------------
    # Parameter of RBF kernel: exp(-gamma * x^2).
    gamma=1,
    # -------------------------------------------------------------------------
    # Number of Monte Carlo samples per original feature.
    n_components=100,
    # -------------------------------------------------------------------------
    # Pseudo-random number generator to control the generation of the random
    # weights and random offset when fitting the training data.
    random_state=1,
)

X_features = rbfSampler.fit_transform(X)

X_features.shape

(4, 100)

sgdClassifier = SGDClassifier(max_iter=100, tol=1e-3)

sgdClassifier.fit(X_features, y)

sgdClassifier.score(X_features, y)

1.0