def test_train_batch_IP_diagonal(self):
training_data = self._training_data()
embed = GaussianEmbedding(10, 5,
covariance_type='diagonal',
energy_type='IP',
mu_max=2.0, sigma_min=0.8, sigma_max=1.2, eta=0.1, Closs=1.0
)
for k in xrange(0, len(training_data), 100):
embed.train_batch(training_data[k:(k+100)])
self._check_results(embed)
def test_train_batch_KL_diagonal(self):
training_data = self._training_data()
embed = GaussianEmbedding(10, 5,
covariance_type='diagonal',
energy_type='KL',
mu_max=2.0, sigma_min=0.8, sigma_max=1.2, eta=0.1, Closs=1.0
)
# diagonal training has more parameters so needs more then one
# epoch to fully learn data
for k in xrange(0, len(training_data), 100):
embed.train_batch(training_data[k:(k+100)])
self._check_results(embed)