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)