Simple example precomputed¶

print(__doc__)

import numpy as np
from sklearn.metrics.pairwise import pairwise_kernels

from sklearn_rvm import EMRVC

# General a toy dataset:s it's just a straight line with some Gaussian noise:
n_samples = 100
np.random.seed(0)
X = np.random.normal(size=n_samples)
y = (X > 0).astype(np.float)
X[X > 0] *= 4
X += .3 * np.random.normal(size=n_samples)

X = X[:, np.newaxis]

K = pairwise_kernels(X)
# Fit the classifier
clf = EMRVC(kernel="precomputed")
clf.fit(K, y)

print(clf.predict(K))
print(clf.predict_proba(K))
print(clf.score(K, y))