Almost finish decentralised voter.

DecisionSystem/DecentralisedActivityFusion/voter.py

@@ -1,5 +1,8 @@
 import paho.mqtt.client as mqtt
 import time
+import json
+import umsgpack
+import numpy as np
 
 class Voter:
     '''
@@ -17,26 +20,90 @@ class Voter:
     The original approach in the paper requires some previous training before sensing, so 
     that there is a probability of a given action based upon the previous set of actions.
     '''
-    def __init__(self, on_vote):
+    _votes = {}
+    _connected_voters = []
+    _taking_votes = False
+
+    def __init__(self, on_vote, swarm_name):
         '''
         on_vote: Callback to get the required vote to broadcast.
         '''
+        # Load config file
+        cfg = None
+        with open('config.json') as json_config:
+            cfg = json.load(json_config)
+        self._cfg = cfg
         self.on_vote = on_vote
-        self.client = mqtt.Client()
+        self._swarm = swarm_name
+        self._client = mqtt.Client()
+        self._client.on_message = self.on_message
+        self._client.on_connect = self.on_connect
+        self._client.connect(cfg["mqtt"]["host"], cfg["mqtt"]["port"], cfg["mqtt"]["timeout"])
+        self._client.loop_start()
 
     def submit_vote(self):
         # Publish to swarm where all other voters will receive a vote.
-
-
+        self._client.publish(self._swarm, self.collect_vote)
+        self._taking_votes = True
+        time.sleep(self._cfg["mqtt"]["timeout"])
+        self._taking_votes = False
         # Wait a certain amount of time for responses, then fuse the information.
         self.fuse_algorithm()
 
         # Need the error and number of timestamps since voting started to finalise the consensus.
 
     def fuse_algorithm(self):
+        # First calculate vi -> the actual vote that is taken 
+        # (Or the probability that the observation is a label for each)
+        # We're just going to be doing 1 for the detected and 0 for all others.
+        # vi = np.zeros(6,1)
+        # ANDvi = np.zeros(6,6)
+
+        # # Set diagonal of ANDvi to elements of vi.
+        # for i in np.size(vi):
+        #     ANDvi[i,i] = vi[i]
+        
+        # M is the probability of going from one state to the next, which
+        # is assumed to be uniform for our situation - someone is just as likely
+        # to raise 5 fingers from two or any other.
+        # And so a 6x6 matrix is generated with all same probability to show this.
+        # Remember they could be holding up no fingers...
+        # m = np.full((6,6), 0.2)
+
+        # Y1T = np.full((6,1),1)
+
+        # Moving to an approach that does not require the previous 
+        # timestep (or so much math...)
+        # First take other information and fuse, using algorithm
+        # as appropriate.
         pass
 
+
     def on_message(self, client, userdata, message):
-        pass
+        try:
+            message_dict = umsgpack.unpackb(message.payload)
+        except:
+            print("Incorrect message received")
+            return
 
+        if message_dict["type"] == "vote":
+            # received a vote
+            if self._taking_votes:
+                self._votes[message_dict["client"]] = message_dict["vote"]
 
+        elif message_dict["type"] == "connect":
+            # voter connected to the swarm
+            self._connected_voters.append(message_dict["client"])
+
+        elif message_dict["type"] == "disconnect":
+            # Sent as the voter's will message
+            self._connected_voters.remove(message_dict["client"])
+
+    def on_connect(self, client, userdata, flags, rc):
+        print("Connected with result code " + str(rc))
+        self._client.subscribe(self._swarm)
+
+    def collect_vote(self):
+        vote_message = umsgpack.packb({"type": "vote", 
+        "client":self._client._client_id, "vote": self.on_vote()})
+        return vote_message