Generalized Relevance Learning Vector Quantization

This example shows how GRLVQ projects and classifies. The plot shows the target class of each data point (big circle) and which class was predicted (smaller circle). It also shows the prototypes (black diamond) and their labels (small point inside the diamond). The projected data is shown in the right plot.

Out:

GRLVQ:
('variance coverd by projection:', 100.0)
('classification accuracy:', 1.0)

import numpy as np
import matplotlib.pyplot as plt

from sklearn_lvq import GrlvqModel
from sklearn_lvq.utils import plot2d

print(__doc__)


nb_ppc = 100
toy_label = np.append(np.zeros(nb_ppc), np.ones(nb_ppc), axis=0)

print('GRLVQ:')
toy_data = np.append(
    np.random.multivariate_normal([0, 0], np.array([[0.3, 0], [0, 4]]),
                                  size=nb_ppc),
    np.random.multivariate_normal([4, 4], np.array([[0.3, 0], [0, 4]]),
                                  size=nb_ppc), axis=0)
grlvq = GrlvqModel()
grlvq.fit(toy_data, toy_label)
plot2d(grlvq, toy_data, toy_label, 1, 'grlvq')

print('classification accuracy:', grlvq.score(toy_data, toy_label))
plt.show()

Total running time of the script: ( 0 minutes 5.710 seconds)