From 2cbe44dcb07ca93c8b60397dc2542435e017de88 Mon Sep 17 00:00:00 2001
From: Liyao Zhu <l.zhu34@student.unimelb.edu.au>
Date: Wed, 24 Apr 2019 04:49:30 +1000
Subject: [PATCH] stacked bar of alpha added simplified select for well-mixed
graph
---
game.py | 58 +++++++++++++++++++++++++++++++++++++++++++-------------
graph.py | 55 ++++++++++++++++++++++++++++++++---------------------
2 files changed, 78 insertions(+), 35 deletions(-)
diff --git a/game.py b/game.py
index b613031..47360f6 100644
--- a/game.py
+++ b/game.py
@@ -172,20 +172,19 @@ def rep(repeat, N=100, R=1, K=99, P=0, Actions=[0, 0.2, 0.4, 0.6, 0.8], I=1000,
return data
-
def averageOfLast(data, Actions, r=0, lastIterations=100 ):
sum = 0
- action_counter = {}
+ action_counter = {action:0 for action in Actions}
for i in range(-1, -lastIterations-1, -1):
sum += np.sum(data[i, r] * Actions)
- for a in Actions:
- action_counter[a] += data[i, r, a]/lastIterations
+ for a in range(len(Actions)):
+ action_counter[Actions[a]] += data[i, r, a]/lastIterations
return (sum/100, action_counter)
-def graph_kp3d(Actions, Klist=[2], Plist=[0.2, 0.5, 0.8], repeat=1):
+def graph_kp3d(Actions, Klist=[2, 4], Plist=[0.2, 0.5, 0.8], repeat=1):
K = Klist
P = Plist
@@ -194,7 +193,8 @@ def graph_kp3d(Actions, Klist=[2], Plist=[0.2, 0.5, 0.8], repeat=1):
for k in range(len(K)):
for p in range(len(P)):
data = rep(repeat, K=K[k], P=P[p]) # Specify other params by adding here or change default of rep
- meanA[k][p] = averageOfLast(data, Actions, lastIterations=100) # Doing the first round only -- for now
+ meanA[k][p] = averageOfLast(data, Actions, lastIterations=100)[0] # Doing the first round only -- for now
+ print("k, p, mean",k,p,meanA[k][p])
P, K = np.meshgrid(P, K)
@@ -208,6 +208,35 @@ def graph_kp3d(Actions, Klist=[2], Plist=[0.2, 0.5, 0.8], repeat=1):
plt.show()
+def stackBar_alpha(r, Actions, alphaList, repeat=1): # Plotting the data for round r
+
+ A = len(Actions)
+ p = []
+ count = np.zeros((A, len(alphaList))) # of each action in each iter
+ ind = np.arange(len(alphaList))
+ width = 0.3
+
+ for al in range(len(alphaList)):
+ data = rep(repeat, Actions=Actions, alpha=alphaList[al])
+ action_counter = averageOfLast(data, Actions, r, 100)[1]
+ for a in range(A):
+ count[a, al] = action_counter[Actions[a]]
+ base = 0
+
+ for a in range(A):
+ p.append(plt.bar(ind, count[a], width, bottom=base, color=str(0.9 - 0.9 * Actions[a])))
+ base += count[a]
+
+ plt.ylabel('Number of Actions')
+ plt.xlabel('Alpha, the loss fraction')
+ plt.title('Average Number of Actions in Round ' + str(r+1))
+ plt.xticks(ind, alphaList)
+ # plt.yticks(np.arange(0, 81, 10))
+ plt.legend(tuple([p[x][0] for x in range(A)][::-1]), tuple(Actions[::-1]), loc='lower left')
+ plt.show()
+
+
+
def main():
# Read-in or Define Parameters
@@ -226,19 +255,22 @@ def main():
Graph1: Number of Actions of Round r (start by 0) by Iteration
"""
# Repeat game and get the averaged data
- RepeatTimes = 30
- data = rep(RepeatTimes, N, R, K, P, Actions, I, RF, alpha)
-
- for r in range(R):
- stackPlot(data, r, Actions, I, "Fully-Mixed Graph")
+ # RepeatTimes = 30
+ # data = rep(RepeatTimes, N, R, K, P, Actions, I, RF, alpha)
+ #
+ # for r in range(R):
+ # stackPlot(data, r, Actions, I, "Fully-Mixed Graph")
"""
Graph2: Average contribution by K, P
"""
- graph_kp3d(Actions)
-
+ # graph_kp3d(Actions)
+ """
+ Graph3: Actions by different alpha value
+ """
+ stackBar_alpha(0, Actions, alphaList=[0, 0.2, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1])
if __name__ == '__main__':
main()
\ No newline at end of file
diff --git a/graph.py b/graph.py
index b9c6e6a..59e0277 100644
--- a/graph.py
+++ b/graph.py
@@ -65,28 +65,39 @@ class Graph:
select = []
selectedNodes = {i: 0 for i in range(self.N)}
- while edges: # Loop when edges is not empty
- i, j = edges[np.random.randint(0, len(edges))]
- # print("selected nodes:", i, j)
- select.append((i, j))
- nodes.remove(i)
- nodes.remove(j)
- selectedNodes[i] += 1
- selectedNodes[j] += 1
- # print("Remaining nodes:", nodes)
- edges = [(a, b) for (a, b) in edges if (a != i) and (a != j)
- and (b != i) and (b != j)]
- # print("after removal", edges)
-
- while nodes:
- v = nodes.pop(np.random.randint(0, len(nodes)))
- v_edges = [(i, j) for (i, j) in self.edges if i == v or j == v]
- i, j = v_edges[np.random.randint(len(v_edges))]
- select.append((i, j))
- selectedNodes[i] += 1
- selectedNodes[j] += 1
-
- # print("Number of each nodes selected:", selectedNodes)
+ if self.K == self.N - 1: #Well-mixed graph
+ permutation = np.random.permutation(self.N)
+ selectedNodes = {i: 1 for i in range(self.N)}
+ if self.N % 2 == 1:
+ extraNode = np.random.randint(0, self.N)
+ while extraNode == permutation[self.N - 1]:
+ extraNode = np.random.randint(0, self.N)
+ np.append(permutation, extraNode)
+ selectedNodes[extraNode] += 1
+ select = permutation.reshape((int(len(permutation)/2), 2))
+ else:
+ while edges: # Loop when edges is not empty
+ i, j = edges[np.random.randint(0, len(edges))]
+ # print("selected nodes:", i, j)
+ select.append((i, j))
+ nodes.remove(i)
+ nodes.remove(j)
+ selectedNodes[i] += 1
+ selectedNodes[j] += 1
+ # print("Remaining nodes:", nodes)
+ edges = [(a, b) for (a, b) in edges if (a != i) and (a != j)
+ and (b != i) and (b != j)]
+ # print("after removal", edges)
+
+ while nodes:
+ v = nodes.pop(np.random.randint(0, len(nodes)))
+ v_edges = [(i, j) for (i, j) in self.edges if i == v or j == v]
+ i, j = v_edges[np.random.randint(len(v_edges))]
+ select.append((i, j))
+ selectedNodes[i] += 1
+ selectedNodes[j] += 1
+
+ # print("Number of each nodes selected:", selectedNodes)
self.selectedNodes = selectedNodes
return select
--
GitLab