[research/lotterysim/reports] finalize report

script/research/lotterysim/acc_vs_staked_ratio.py

@@ -10,7 +10,7 @@ ERC20DRK=2.1*10**9
 NODES=100
 plot = []
 EXPS=10
-for nodes in numpy.linspace(1,NODES, 5):
+for nodes in numpy.linspace(1,NODES, 10):
     accs = []
     for _ in range(EXPS):
         darkies = []
@@ -18,7 +18,7 @@ for nodes in numpy.linspace(1,NODES, 5):
         darkies += [ Darkie(random.gauss(egalitarian, egalitarian*0.1), strategy=random_strategy(EPOCH_LENGTH)) for id in range(int(nodes)) ]
         #darkies += [Darkie() for _ in range(NODES*2)]
         airdrop = ERC20DRK
-        dt = DarkfiTable(airdrop, RUNNING_TIME, CONTROLLER_TYPE_DISCRETE, kp=0.005999999999989028, ki=-0.005999999985257798, kd=0.01299999999999478, r_kp=0.47, r_ki=7.2, r_kd=21.33)
+        dt = DarkfiTable(airdrop, RUNNING_TIME, CONTROLLER_TYPE_DISCRETE, kp=0.005999999999989028, ki=-0.005999999985257798, kd=0.01299999999999478,  r_kp=-2.53, r_ki=29.5, r_kd=53.77)
         for darkie in darkies:
             dt.add_darkie(darkie)
         acc, apy, reward, staked_ratio, apr = dt.background(rand_running_time=False)

script/research/lotterysim/core/constants.py

@@ -19,7 +19,7 @@ ONE_YEAR = Num(365.25*24*60*60/SLOT)
 
 TARGET_APR = Num(0.12)
 
-PRIMARY_REWARD_TARGET = 0.5 # staked ratio
+PRIMARY_REWARD_TARGET = 0.35 # staked ratio
 SECONDARY_LEAD_TARGET = 1 #number of lead per slot
 
 EPSILON = 1

script/research/lotterysim/core/lottery.py

@@ -39,9 +39,9 @@ class DarkfiTable:
             f = self.secondary_pid.pid_clipped(float(feedback), debug)
 
             if count%EPOCH_LENGTH == 0:
-                #acc = self.secondary_pid.acc_percentage()
-                staked_ratio = self.avg_stake_ratio()
-                reward = self.primary_pid.pid_clipped(float(staked_ratio), debug)
+                acc = self.secondary_pid.acc_percentage()
+                #staked_ratio = self.avg_stake_ratio()
+                reward = self.primary_pid.pid_clipped(acc, debug)
                 self.rewards += [reward]
 
             #note! thread overhead is 10X slower than sequential node execution!

script/research/lotterysim/core/strategy.py

@@ -76,10 +76,10 @@ class SigmoidStrategy(Strategy):
 
 def random_strategy(epoch_length):
     rnd = random.random()
-    if rnd < 0.25:
+    if rnd < 0.5:
         return Hodler(epoch_length)
-    elif rnd < 0.5 and rnd >= 0.25:
-        return LinearStrategy(epoch_length)
+    #elif rnd < 0.5 and rnd >= 0.25:
+        #return LinearStrategy(epoch_length)
     elif rnd < 0.75 and rnd >= 0.5:
         return LogarithmicStrategy(epoch_length)
     else:

script/research/lotterysim/discrete_instance.py

@@ -9,7 +9,7 @@ import math
 os.system("rm log/*_feedback.hist; rm log/*_output.hist")
 
 RUNNING_TIME = int(input("running time:"))
-NODES=1000
+NODES=100
 
 if __name__ == "__main__":
     egalitarian = ERC20DRK/NODES
@@ -25,7 +25,7 @@ if __name__ == "__main__":
     for darkie in darkies:
         effective_airdrop+=darkie.stake
     print("network airdrop: {}, staked token: {}/{}% on {} nodes".format(airdrop, effective_airdrop, effective_airdrop/airdrop*100, len(darkies)))
-    dt = DarkfiTable(airdrop, RUNNING_TIME, CONTROLLER_TYPE_DISCRETE, kp=-0.010399999999938556, ki=-0.0365999996461878, kd=0.03840000000000491,  r_kp=0.41, r_ki=5.65, r_kd=89.23)
+    dt = DarkfiTable(airdrop, RUNNING_TIME, CONTROLLER_TYPE_DISCRETE, kp=-0.010399999999938556, ki=-0.0365999996461878, kd=0.03840000000000491,  r_kp=-2.53, r_ki=29.5, r_kd=53.77)
     for darkie in darkies:
         dt.add_darkie(darkie)
     acc, avg_apy, avg_reward, stake_ratio, avg_apr = dt.background(rand_running_time=False)