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Fuzzer.java
main.py 10.65 KiB
import matplotlib.pyplot as plt
from matplotlib import cm
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
from scipy import stats
import game
def stackPlot(data, r, Actions, Iterations, titleComment=""):
A = len(Actions)
x = range(Iterations)
y = np.zeros((Iterations, A))
for i in range(Iterations):
y[i] = data[i][r]
y = np.vstack(y.T)
fig, ax = plt.subplots()
ax.stackplot(x, y, labels=Actions, colors=[str(0.9 - 0.9 * x) for x in Actions])
ax.legend(loc='best')
plt.ylabel('Number of Actions')
plt.xlabel('Time(iterations)')
title = 'Average Number of Actions in Round ' + str(r + 1)
if titleComment:
title += "\n" + titleComment
plt.title(title)
# plt.savefig(titleComment + " in round " + str(r+1) + ".png")
plt.show()
def rep(repeat=30, R=1, Actions=[0, 0.2, 0.4, 0.6, 0.8], I=1000, **kwargs):
data = np.zeros((I, R, len(Actions)))
Actions.sort()
for re in range(repeat):
print("REP", re)
g = game.Game(R=R, Actions=Actions, I=I, **kwargs)
result = g.play()
data += result
data /= repeat
return data
def averageOfLast(data, Actions, N=100, r=0, lastIterations=100):
sum = 0
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 range(len(Actions)):
action_counter[Actions[a]] += data[i, r, a] / lastIterations
return (sum / (lastIterations * N), action_counter)
def graph_kp3d(Actions, Klist=[2, 4, 8, 10], Plist=[0, 0.3, 0.6, 0.9], repeat=30, N=100):
K = Klist
P = Plist
meanA = np.zeros((len(K), len(P)))
for k in range(len(K)):
for p in range(len(P)):
data = rep(repeat, K=K[k], P=P[p], Actions=Actions) # Specify other params by adding here
meanA[k][p] = averageOfLast(data, Actions, lastIterations=100, N=N)[0] # Doing the first round only -- for now
print("k, p, mean", k, p, meanA[k][p])
P, K = np.meshgrid(P, K)
fig = plt.figure()
ax = fig.gca(projection='3d')
surf = ax.plot_surface(P, K, meanA, cmap=cm.coolwarm,
linewidth=0, antialiased=False)
fig.colorbar(surf, shrink=0.5, aspect=5)
plt.show()
def graph3d_alpha_threshold(Actions, repeat=30, AlphaList=np.arange(0, 1.01, 0.05), ThreshList=np.arange(0.1, 1.1, 0.1), N=100, **kwargs):
mean = np.zeros((len(ThreshList), len(AlphaList)))
ratio_by_threshold = np.zeros((len(ThreshList), len(AlphaList)))
for t in range(len(ThreshList)):
for a in range(len(AlphaList)):
print("Calculating... t, alpha = ", t, a)
data = rep(repeat=repeat, Actions=Actions, alpha=AlphaList[a], threshold=ThreshList[t], **kwargs)
mean[t][a] = averageOfLast(data, Actions, lastIterations=100, N=N)[0]
ratio_by_threshold[t] = mean[t] / ThreshList[t]
A, T = np.meshgrid(AlphaList, ThreshList)
fig = plt.figure()
ax = fig.gca(projection='3d')
surf = ax.plot_surface(A, T, mean, cmap=cm.Greys,
linewidth=0, antialiased=False)
ax.set_xlabel('Alpha')
# ax.invert_xaxis()
ax.set_ylabel('Threshold')
ax.set_zlabel('Average contribution')
fig.colorbar(surf, shrink=0.5, aspect=5)
# plt.show()
fig2 = plt.figure()
ax2 = fig2.gca(projection='3d')
surf2 = ax2.plot_surface(A, T, ratio_by_threshold, cmap=cm.Greys,
linewidth=0, antialiased=False)
ax2.set_xlabel('Alpha')
# ax.invert_xaxis()
ax2.set_ylabel('Threshold')
# ax.invert_yaxis()
ax2.set_zlabel('Average contribution by threshold')
fig2.colorbar(surf2, shrink=0.5, aspect=5)
plt.show()
def stackBar(r, Actions, repeat=30, multiArm='greedy', **kwargs): # Plotting the data for round r
# if len(kwargs) != 1:
# print("ERROR, Stack Bar Graph Expects 1 List, gets:", len(kwargs))
# key, alist = list(kwargs.items())[0]
key = -1
alist = []
for k, v in kwargs.items():
if isinstance(v, list):
if key == -1:
key = k
alist = v
else:
print("ERROR, Stack Bar Graph Expects Only 1 List to Compare")
exit(4)
del kwargs[key]
print("Comparing:", key)
print("On:", alist)
A = len(Actions)
p = []
count = np.zeros((A, len(alist))) # of each action in each iter
ind = np.arange(len(alist))
width = 0.3
for al in range(len(alist)):
newKwargs = {**{key: alist[al]}, **kwargs}
if key == 'N':
newKwargs['K'] = alist[al] - 1
elif 'N' not in newKwargs.keys():
newKwargs['N'] = 100 # default value
data = rep(repeat, Actions=Actions, multiArm=multiArm, **newKwargs) / newKwargs['N'] * 100
action_counter = averageOfLast(data, Actions, r=r, lastIterations=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('Percentage of Actions')
if key == 'epsilon':
plt.xlabel(key + ' (' + multiArm + ')')
else:
plt.xlabel(key)
plt.title('Average Number of Actions in Round ' + str(r + 1))
plt.xticks(ind, alist)
plt.yticks(np.arange(0, 101, 10))
plt.legend(tuple([p[x][0] for x in range(A)][::-1]), tuple(Actions[::-1]), loc='best')
plt.show()
def t_test(repeat, Actions, r=0, R=1, I=1000, lastIterations=100, N=100, byThreshold=False, **kwargs):
key = -1
atuple = ()
for k, v in kwargs.items():
if isinstance(v, tuple):
if key == -1:
key = k
atuple = v
else:
print("ERROR, T-Test Expects Only 1 Tuple to Compare")
exit(5)
del kwargs[key]
samples = np.zeros((2, repeat))
for s in (0, 1):
newArgs = {**{key: atuple[s]}, **kwargs}
samples[s] = repHist(repeat, Actions, R, r, I, lastIterations, N, **newArgs)
if byThreshold:
samples[s] /= newArgs["threshold"]
print("Sample", s, samples[s])
print(stats.ttest_ind(samples[0], samples[1]))
def repHist(repeat, Actions, R=1, r=0, I=1000, lastIterations=100, N=100, **kwargs):
hist = np.zeros(repeat)
for re in range(repeat):
# print("HistREP", re)
g = game.Game(R=R, Actions=Actions, I=I, N=N, **kwargs)
result = g.play()
hist[re] = averageOfLast(result, Actions, N, r, lastIterations)[0]
return hist
def main():
# Read-in or Define Parameters
N = 100
R = 1
K = 99
P = 0
I = 1000
RF = 0
alpha = 1
Actions = [0, 0.2, 0.4, 0.6, 0.8]
"""
Graph1: Number of Actions of Round r (start by 0) by Iteration
"""
# RepeatTimes = 30
# for N in [5, 10, 20, 50, 100]:
# K = N - 1
# for R in [1, 2, 4]:
# for alpha in [0.2, 0.4, 0.6, 0.8, 1]:
# data = rep(RepeatTimes, R, Actions, I, N=N, K=K, alpha=alpha)
# for r in range(R):
# stackPlot(data, r, Actions, I, titleComment="N="+ str(N) + ", R=" + str(R) + ", alpha=" +str(alpha) + ", Well-Mixed graph")
# for k in [2, 99]:
# for p in [0.8]:
# data = rep(repeat=30, N=100, K=k, Actions=Actions, R=1, I=I, P=p)
# stackPlot(data, r=0, Iterations=I, Actions=Actions, titleComment=("K=" + str(k) + ", P=" + str(p)))
# data = rep(repeat=30, Actions=Actions, R=1, I=I, RF=2, threshold=0.3)
# stackPlot(data, r=0, Iterations=I, Actions=Actions, titleComment="threshold = 0.3")
"""
Graph2: Average contribution by K, P
"""
# graph_kp3d(Actions)
"""
Graph3: Comparing a parameter (put in a list)
"""
# stackBar(0, Actions, repeat=1, alpha=[0, 0.2, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1])
# stackBar(0, Actions, repeat=1, N=[5, 10, 20, 50, 100], threshold=0.6, RF=2)
stackBar(0, Actions, repeat=30, RF=2, threshold=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1])
"""
Graph4: Actions by different epsilon method + value
"""
# stackBar(0, Actions, repeat=1, multiArm='greedy', epsilon=[0.05, 0.1, 0.15, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])
# stackBar(0, Actions, repeat=1, multiArm='decrease', epsilon=[0.8, 0.9, 0.95, 0.98, 0.99, 0.999, 0.9999])
"""
Graph5: Average contribution by Alpha and Threshold
"""
# graph3d_alpha_threshold(Actions, repeat=1, RF=2)
"""
T-Test
"""
# t_test(30, Actions, alpha=1, RF=2, threshold=(0.2, 0.3), byThreshold=True) #p=3.324e-31
# t_test(30, Actions, alpha=1, RF=2, threshold=(0.6, 1.0)) #pvalue=0.2208
# t_test(30, Actions, alpha=1, RF=2, threshold=(0.8, 1.0)) #pvalue=0.1096
# t_test(30, Actions, alpha=1, RF=2, threshold=(0.5, 1.0)) #pvalue=2.2067e-08
# t_test(30, Actions, alpha=0.85, RF=2, threshold=(0.2, 0.3), byThreshold=True) #pvalue=0.005865
# t_test(30, Actions, alpha=(1, 0.9), RF=2, threshold=0.2) #pvalue=0.3748
# t_test(30, Actions, alpha=(1, 0.85), RF=2, threshold=0.2) #pvalue=0.001466
# t_test(30, Actions, alpha=(1, 0.8), RF=2, threshold=0.2) #pvalue=0.0002030
# t_test(30, Actions, alpha=(1, 0.75), RF=2, threshold=0.2) #pvalue=3.9617e-07
# t_test(30, Actions, alpha=(1, 0.7), RF=2, threshold=0.2) #pvalue=2.2428e-09
# t_test(30, Actions, alpha=(1, 0.65), RF=2, threshold=0.2) #pvalue=6.8966e-09
# t_test(30, Actions, alpha=(1, 0.6), RF=2, threshold=0.2) #pvalue=7.1621e-15
# t_test(30, Actions, alpha=(1, 0.5), RF=2, threshold=0.2) #pvalue=4.1760e-13
# t_test(30, Actions, alpha=(1, 0.45), RF=2, threshold=0.2) #pvalue=1.3749e-11
# t_test(30, Actions, alpha=(1, 0.4), RF=2, threshold=0.2) #pvalue=3.8352e-19
"""T-TEST K,P"""
# t_test(30, Actions, K=(2, 99), P=0) #pvalue=0.4278
# t_test(30, Actions, K=(2, 99), P=0.9) #pvalue=0.4541
# for _ in range(5):
# t_test(30, Actions, K=(2, 99), P=0.8) #pvalue=0.01502 ***
# t_test(30, Actions, K=(2, 99), P=0.85) #pvalue=0.1931
# t_test(30, Actions, K=(2, 99), P=0.75) #pvalue=0.5630
# t_test(30, Actions, K=2, P=(0, 0.9)) #pvalue=0.9806
# t_test(30, Actions, K=2, P=(0, 0.8)) #pvalue=0.4523
# t_test(30, Actions, K=(2, 99), P=0.9) #pvalue=0.4541
# t_test(30, Actions, K=(2, 99), P=0.7) #pvalue=0.3698
# t_test(30, Actions, K=99, P=(0, 0.8)) #pvalue=0.8167
# base = repHist(30, Actions, K=99)
# for p in np.arange(0.76, 0.85, 0.01): #ALL >.05
# compare = repHist(30, Actions, K=2, P=p)
# print("K=2, P=", p, stats.ttest_ind(base, compare))
if __name__ == '__main__':
main()