def test_classification(self):
result = np.array([
[0.228, 0.312],
[0.48166667, 0.76666667],
])
nw = RBFKMeans(n_clusters=2)
nw.train(self.data, epsilon=1e-5)
self.assertTrue(np.all(result == np.round(nw.centers, 8)))
if self.draw_plot:
classes = nw.predict(self.data)
for i, center in enumerate(nw.centers):
positions = np.argwhere(classes[:, 0] == i)
class_data = np.take(self.data, positions[:, 0], axis=0)
plt.plot(class_data[:, 0], class_data[:, 1], 'o')
for center in nw.centers:
plt.plot(center[0], center[1], 'kx')
plt.axis([0, 1, 0, 1])
plt.show()
import numpy as np
from neupy.algorithms import RBFKMeans
from mnist import MNIST
mnistData=MNIST('./mnistData')
imgTrain,lblTrain=mnistData.load_training()
imgTest,lblTest=mnistData.load_testing()
data = np.array(imgTrain)
rbfk_net = RBFKMeans(n_clusters=40, verbose=False)
rbfk_net.train(data, epsilon=1e-5)
print len(rbfk_net.centers)
new_data = np.array(imgTest[1])
print rbfk_net.predict(new_data).shape
