Hi all, when I perform gaussian process for classification with this code below:

```
import torch
import torch.nn.functional as F
import numpy as np
import matplotlib.pyplot as plt
import time
import pyro
import pyro.contrib.gp as gp
import pyro.distributions as dist
from pyro.infer import SVI, Trace_ELBO
from pyro.optim import Adam
from sklearn.metrics import accuracy_score
from sklearn.datasets import make_classification
#Create dataset
N = 200
N_TRAIN = int(0.80 * N)
N_FEATURES = 7
X,y = make_classification(n_samples=N, n_features=N_FEATURES,n_redundant=0,n_repeated=0,n_clusters_per_class = 2, n_classes=2)
X_train = X[:N_TRAIN,:]
y_train =y[:N_TRAIN]
X_test = X[N_TRAIN:,:]
y_test =y[N_TRAIN:]
X_train = torch.tensor(X_train,dtype= torch.float32)
y_train = torch.tensor(y_train, dtype=torch.float32)
X_test = torch.tensor(X_test,dtype= torch.float32)
y_test = torch.tensor(y_test, dtype=torch.float32)
# Choose kernel and likelihood
kernel = gp.kernels.RBF(input_dim = N_FEATURES, variance = torch.tensor(1.),lengthscale = torch.tensor(10.))
likelihood = gp.likelihoods.Binary()
gpc = gp.models.VariationalGP(X_train,y_train,kernel=kernel,jitter = 1e-03, likelihood=likelihood,whiten =
True)
# Inference
optim = Adam({"lr":0.005})
svi = SVI(gpc.model,gpc.guide,optim,loss=Trace_ELBO())
num_steps = 2000
losses =np.zeros(num_steps)
pyro.clear_param_store()
start =time.time()
for i in range(num_steps):
losses[i]=svi.step()
if i %(num_steps//20) ==0:
print("iteration %d. Loss %.4f" % (i,losses[i]))
elapsed_time = time.time()
print("elapsed time: %.2f" %(elapsed_time-start))
end = time.time()
print("Loop take time %.2f"%(end-start))
plt.plot(losses)
plt.show()
```

What is the appropriate way to get prediction for classification?

I am trying this way:

```
# Method 1
f_loc, f_var = gpc(X_train, full_cov=True)
prediction = gpc.likelihood(f_loc,f_var)
pred= np.round(np.mean(prediction.numpy(),axis =0))
train_acc = accuracy_score(pred,y_train.numpy())
f_loc, f_var = gpc(X_test, full_cov=True)
prediction = gpc.likelihood(f_loc,f_var)
pred= np.round(np.mean(prediction.numpy(),axis =0))
test_acc = accuracy_score(pred,y_test.numpy())
print("Train accuracy: %.3f, Test accuracy: %.3f" %(train_acc,test_acc))
```

and this way:

```
# Method 2
f_loc, _ = gpc(X_train, full_cov=True)
prediction = np.zeros([300,N_TRAIN])
for i in range(300):
prediction_sample = dist.Bernoulli((F.sigmoid(f_loc)))().numpy()
prediction[i,:]= prediction_sample
pred= np.round(np.mean(prediction,axis =0))
train_acc = accuracy_score(pred,y_train.numpy())
f_loc, _ = gpc(X_test, full_cov=True)
prediction = np.zeros([300,N-N_TRAIN])
for i in range(300):
prediction_sample = dist.Bernoulli((F.sigmoid(f_loc)))().numpy()
prediction[i,:]= prediction_sample
pred= np.round(np.mean(prediction,axis =0))
test_acc = accuracy_score(pred,y_test.numpy())
print("Train accuracy: %.3f, Test accuracy: %.3f" %(train_acc,test_acc))
```

Which way is better and why?