from decentralized.devices import GossipDevice
from decentralized.mergers import SlowMerge
from learning.nn import MLP
node = ... # a node identifier object (can be any object)
features = ... # feature vector, should have the same length for each device
labels = ... # one hot encoding of class labels, zeroes if no label is known
predictor = MLP(features.shape[0], labels.shape[0])  # or load a pretrained model with
device = GossipDevice(node, predictor, features, labels, gossip_merge=SlowMerge)

send = u.send()
receive = v.receive(u.name, send)
u.ack(v.name, receive)

from decentralized.devices import GossipDevice
from decentralized.mergers import AvgMerge
from decentralized.simulation import create_network

dataset_name = ... # "cora", "citeseer" or "pubmed"
network, test_labels = create_network(dataset_name, 
                                      GossipDevice,
                                      pretrained=False,
                                      gossip_merge=AvgMerge,
                                      gossip_pull=False,
                                      seed=0)
for epoch in range(800):
    network.round()
    accuracy_base = sum(1. if network.devices[u].predict(False) == label else 0 for u, label in test_labels.items()) / len(test_labels)
    accuracy = sum(1. if network.devices[u].predict() == label else 0 for u, label in test_labels.items()) / len(test_labels)
    print(f"Epoch {epoch} \t Acc {accuracy:.3f} \t Base acc {accuracy_base:.3f}")

TBD