diff --git a/agent.py b/agent.py
index 4a1fc191596fbc0ab2d199058453711ab67a9e14..6c6e1d5129161edae23d456b21da25b8ae7f64d8 100644
--- a/agent.py
+++ b/agent.py
@@ -35,10 +35,7 @@ class Agent:
def updateReward(self, round, action, loss):
"""
- :param round:
- :param action:
- :param loss:
- :return:
+ updates the Q-table by receiving a payoff
"""
newWealth = self.wealth * (1-action) * (1-loss)
reward = newWealth - self.wealth
@@ -54,9 +51,13 @@ class Agent:
else:
print("ERROR: Illegal round number")
exit(2)
+
+ """Update function"""
self.qTable[round][index] += self.learnRate * (
reward + self.discount * maxNextQ - self.qTable[round][index])
- # print("QTABLE:", self.qTable)
+
+ # if self.iteration == 999:
+ # print("QTABLE:", self.qTable)
if round == self.R - 1:
self.iteration += 1
@@ -64,14 +65,15 @@ class Agent:
def chooseAction(self, roundNumber):
-
- """Method: Q-learning"""
-
+ """
+ Choose an action based on current round number
+ :return: an action (float type)
+ """
randomAct = False
if self.multiArm == 'decrease':
"""Epsilon Decrease"""
if np.random.uniform(0, 1) <= 1 * self.epsilon ** self.iteration:
- randomAct = True
+ randomAct = True
elif self.multiArm == 'greedy':
"""EPSILON GREEDY"""
diff --git a/game.py b/game.py
index a91475ce1d437e5dd73f251466b06efd621b5661..7a93bf88a72384ded6f99b8718d9940b6d050ad2 100644
--- a/game.py
+++ b/game.py
@@ -1,24 +1,30 @@
+"""
+This file contains an implementation of a 2-player R-round collective-risk game
+model. Each player can choose to contribute part of their wealth to a common
+pool to reduce the risk of a collective climate catastrophe. N players are
+randomly paired with one another in a graph-based model in each iteration, and
+play one game. Each player plays at least one game in one iteration. If N is
+odd, a player could play multiple games, but the payoffs are averaged.
+
+Author: Liyao Zhu liyao@student.unimelb.edu.au
+Date: Apr. 2019
+"""
+
+
import numpy as np
import agent, graph
class Game:
- def __init__(self, N=100, R=1, K=99, P=0, Actions=[0, 0.2, 0.4, 0.6, 0.8], I=1000, RF=0, alpha=1, epsilon=0.1,
- multiArm='greedy', threshold=0.8):
- # datamap = utilis.read()
- # self.N = datamap['N'] # N-Player Game
- # self.M = datamap['M'] # Randomly choose M players to play the game (normally 2)
- # self.RF = datamap['RF'] # Parsed number of risk function chosen for the game
- # self.alpha = datamap['alpha'] # Loss fraction
- # self.R = datamap['R'] # Rounds of a game
-
+ def __init__(self, N=100, R=1, K=99, P=0, Actions=[0, 0.2, 0.4, 0.6, 0.8],
+ I=1000, RF=0, alpha=1, epsilon=0.1,multiArm='greedy',
+ threshold=0.8):
self.N = N
self.M = 2
self.RF = RF
self.alpha = alpha
self.R = R
- self.threshold = threshold # Threshold
- # self.actions = [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1]
+ self.threshold = threshold
self.actions = Actions
self.iterations = I
@@ -27,11 +33,11 @@ class Game:
|
| P: Probability of rewiring each original edge in the graph
|
- | K: The number of edges(games) connected to each player. Has to be an even number.
- | If 1 is desired, don't use graph model. Max k: n - 2 (for even n) | n - 1 (for odd n)
- | * k can be odd as n - 1 (for even n). In cases k = n - 1 (for all n) -> a fully connected graph
+ | K: The number of edges(games) connected to each player. Has to be an
+ | even number. Max k: n - 2 (for even n) | n - 1 (for odd n)
+ | k can only be odd as n - 1 (for all n). In cases k = n - 1 -> a
+ | fully connected graph
"""
- # self.graph_based = True
self.rewire_p = P
self.rewire_k = K
# assert (self.rewire_k < self.N)
@@ -39,14 +45,17 @@ class Game:
"Create players"
self.players = []
- IW = 100 # Initial Wealth - can be input, can be variable to distinguish population
- self.totalWealth = self.M * IW # subject to change
+ IW = 100 # Initial Wealth
+ self.totalWealth = self.M * IW
for i in range(self.N):
- self.players.append(agent.Agent(self.R, IW, self.actions, epsilon=epsilon, multiArm=multiArm))
+ self.players.append(agent.Agent(self.R, IW, self.actions,
+ epsilon=epsilon, multiArm=multiArm))
def riskfunc(self, contribution, totalwealth):
"""
- the probability of collective-risk happening, given contribution
+ Implemented different risk functions here.
+ :return: the probability of disaster happening, given contribution
+ and total wealth
"""
proportion = contribution / totalwealth
@@ -73,13 +82,16 @@ class Game:
return "error"
def play(self):
-
- # lastStrategyTable = np.zeros((self.N, self.R))
- # sameStrategyRounds = 0
+ """
+ Play a whole trial of I (1000) iterations, N (100) players games
+ :return: a 3d numpy matrix, recording the averaged counted number of
+ all actions in each round in all iterations.
+ """
results = np.zeros((self.iterations, self.R, len(self.actions)))
"""ITERATION"""
for iter in range(self.iterations):
+ # print("GAME ITERATION", iter)
actionTable = np.zeros((self.N, self.R))
strategyTable = np.zeros((self.R, self.N)) # DIFFERENT AXIS R-N
@@ -107,13 +119,6 @@ class Game:
for i in range(self.N):
self.players[i].updateReward(r, actionTable[i][r], lossTable[i][r])
- """Strategy Stats"""
- # if np.array_equal(strategyTable, lastStrategyTable):
- # sameStrategyRounds += 1
- # else:
- # sameStrategyRounds = 0
- # lastStrategyTable = strategyTable
-
for r in range(self.R):
unique, count = np.unique(strategyTable[r], return_counts=True)
round_counter = dict(zip(unique, count))
diff --git a/main.py b/main.py
index cbaf2a5264c8ae69c2278e29053be9eeed3c082c..b2db86b1688ce22842d87c76d4bf6179be223acd 100644
--- a/main.py
+++ b/main.py
@@ -1,3 +1,12 @@
+"""
+This file contains graph methods and t-test implementations. The main
+function should produce all Figures and t-test results as the thesis.
+
+Author: Liyao Zhu liyao@student.unimelb.edu.au
+Date: Apr. 2019
+"""
+
+
import matplotlib.pyplot as plt
from matplotlib import cm
from mpl_toolkits.mplot3d import Axes3D
@@ -6,7 +15,10 @@ from scipy import stats
import game
-def stackPlot(data, r, Actions, Iterations, titleComment=""):
+def stackPlot(data, r, Actions, Iterations, legendLoc='best', titleComment=""):
+ """
+ Draw a stack plot from averaged data of round r.
+ """
A = len(Actions)
x = range(Iterations)
y = np.zeros((Iterations, A))
@@ -15,12 +27,13 @@ def stackPlot(data, r, Actions, Iterations, titleComment=""):
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')
+ ax.stackplot(x, y, labels=Actions, colors=[str(0.9 - 0.9 * x) for x in
+ Actions])
+ ax.legend(loc=legendLoc)
+ plt.ylabel('Percentage of each action')
plt.xlabel('Time(iterations)')
- title = 'Average Number of Actions in Round ' + str(r + 1)
+ title = 'Average Composition of Actions in Round ' + str(r + 1)
if titleComment:
title += "\n" + titleComment
@@ -32,6 +45,11 @@ def stackPlot(data, r, Actions, Iterations, titleComment=""):
def rep(repeat=30, R=1, Actions=[0, 0.2, 0.4, 0.6, 0.8], I=1000, **kwargs):
+ """
+ Repeat the game over (30) trials and retrieve the average data of
+ game.play()
+ :return: Averaged game results, same shape as the return of game.play()
+ """
data = np.zeros((I, R, len(Actions)))
Actions.sort()
for re in range(repeat):
@@ -44,6 +62,11 @@ def rep(repeat=30, R=1, Actions=[0, 0.2, 0.4, 0.6, 0.8], I=1000, **kwargs):
def averageOfLast(data, Actions, N=100, r=0, lastIterations=100):
+ """
+ Averaged contribution and action counter of last (100) iterations from the
+ data produced by rep()
+ :return: a tuple: (average contribution, a dictionary as action counter)
+ """
sum = 0
action_counter = {action: 0 for action in Actions}
@@ -54,16 +77,22 @@ def averageOfLast(data, Actions, N=100, r=0, lastIterations=100):
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):
+def graph_kp3d(Actions, Klist=[2, 4, 8, 10], Plist=[0, 0.3, 0.6, 0.9],
+ repeat=30, N=100, **kwargs):
+ """
+ Draw a 3D graph for graph-based model, showing the effect of K and P on
+ average contributions. (No effect observed)
+ """
+
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
+ data = rep(repeat, K=K[k], P=P[p], Actions=Actions, **kwargs)
+ meanA[k][p] = averageOfLast(data, Actions, lastIterations=100,
+ N=N)[0]
print("k, p, mean", k, p, meanA[k][p])
P, K = np.meshgrid(P, K)
@@ -78,19 +107,26 @@ def graph_kp3d(Actions, Klist=[2, 4, 8, 10], Plist=[0, 0.3, 0.6, 0.9], repeat=30
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):
-
+def graph3d_alpha_threshold(Actions, repeat=30,
+ AlphaList=np.arange(0, 1.01, 0.05),
+ ThreshList=np.arange(0.1, 1.05, 0.05),
+ N=100, **kwargs):
+ """
+ Draw two 3D graphs showing the average contribution and the average
+ contribution divided by threshold on two parameters: alpha and threshold
+ """
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]
+ 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()
@@ -119,11 +155,12 @@ def graph3d_alpha_threshold(Actions, repeat=30, AlphaList=np.arange(0, 1.01, 0.0
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]
+def stackBar(r, Actions, repeat=30, multiArm='greedy', legendLoc='best',
+ **kwargs):
+ """
+ Draw a stack bar graph, to compare the composition of actions on one
+ parameter, specified as a list in **kwargs
+ """
key = -1
alist = []
@@ -152,14 +189,17 @@ def stackBar(r, Actions, repeat=30, multiArm='greedy', **kwargs): # Plotting th
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]
+ 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])))
+ 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')
@@ -167,14 +207,20 @@ def stackBar(r, Actions, repeat=30, multiArm='greedy', **kwargs): # Plotting th
plt.xlabel(key + ' (' + multiArm + ')')
else:
plt.xlabel(key)
- plt.title('Average Number of Actions in Round ' + str(r + 1))
+ plt.title('Average Composition 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.legend(tuple([p[x][0] for x in range(A)][::-1]), tuple(Actions[::-1]),
+ loc=legendLoc)
plt.show()
-def t_test(repeat, Actions, r=0, R=1, I=1000, lastIterations=100, N=100, byThreshold=False, **kwargs):
+def t_test(repeat, Actions, r=0, R=1, I=1000, lastIterations=100, N=100,
+ byThreshold=False, **kwargs):
+ """
+ Compute the p-value of average contributions on two values of one
+ parameter, specified as a tuple in **kwargs
+ """
key = -1
atuple = ()
for k, v in kwargs.items():
@@ -191,7 +237,8 @@ def t_test(repeat, Actions, r=0, R=1, I=1000, lastIterations=100, N=100, byThres
for s in (0, 1):
newArgs = {**{key: atuple[s]}, **kwargs}
- samples[s] = repHist(repeat, Actions, R, r, I, lastIterations, N, **newArgs)
+ samples[s] = repHist(repeat, Actions, R, r, I, lastIterations, N,
+ **newArgs)
if byThreshold:
samples[s] /= newArgs["threshold"]
print("Sample", s, samples[s])
@@ -199,7 +246,11 @@ def t_test(repeat, Actions, r=0, R=1, I=1000, lastIterations=100, N=100, byThres
print(stats.ttest_ind(samples[0], samples[1]))
-def repHist(repeat, Actions, R=1, r=0, I=1000, lastIterations=100, N=100, **kwargs):
+def repHist(repeat, Actions, R=1, r=0, I=1000, lastIterations=100, N=100,
+ **kwargs):
+ """
+ :return: A list of average contributions of all repetitions
+ """
hist = np.zeros(repeat)
for re in range(repeat):
# print("HistREP", re)
@@ -209,10 +260,8 @@ def repHist(repeat, Actions, R=1, r=0, I=1000, lastIterations=100, N=100, **kwar
return hist
-
-
def main():
- # Read-in or Define Parameters
+ # Default values
N = 100
R = 1
@@ -222,6 +271,76 @@ def main():
RF = 0
alpha = 1
Actions = [0, 0.2, 0.4, 0.6, 0.8]
+ repeat = 1
+
+
+ """Fig. 2"""
+ # data = rep(repeat=repeat, N=100, alpha=0.8, R=8)
+ # stackPlot(data, r=0, Iterations=I, Actions=Actions,
+ # legendLoc='lower right')
+
+
+ """Fig. 3"""
+ # stackBar(0, Actions, repeat=repeat,
+ # alpha=[0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1],
+ # legendLoc='lower left')
+
+ """Fig. 4"""
+ # data = rep(repeat, R=8)
+ # for r in [0, 1, 3]:
+ # stackPlot(data, r, Actions, I, legendLoc='lower right')
+
+ """Fig. 5"""
+ # stackBar(0, Actions, repeat=repeat,
+ # threshold=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1],
+ # legendLoc='upper left', RF=2)
+
+ """Fig. 6"""
+ # data = rep(repeat=repeat, Actions=Actions, R=1, I=I, RF=2, threshold=0.2)
+ # stackPlot(data, r=0, Iterations=I, Actions=Actions,
+ # titleComment="threshold = 0.2")
+
+ """Fig. 7 & 8 - 3D graph comparing alpha and threshold"""
+ # graph3d_alpha_threshold(Actions, repeat=repeat, RF=2)
+
+ """Fig. 9 - simple line graph comparing alpha for threshold=0.2"""
+ # alphaList = np.arange(0, 1.01, 0.02)
+ # mean = np.zeros(len(alphaList))
+ # for i in range(len(alphaList)):
+ # data = rep(repeat, alpha=alphaList[i], threshold=0.2, RF=2)
+ # mean[i] = averageOfLast(data, Actions)[0]
+ #
+ # plt.plot([0.2, 0.2], '--', color='0.5')
+ # plt.plot(alphaList, mean, color='black')
+ #
+ # plt.ylabel('Average Contributions')
+ # plt.xlabel('Alpha, the loss fraction')
+ # plt.show()
+
+ """Fig. 10 - Comparing composition on epsilon (greedy)"""
+ # stackBar(0, Actions, repeat=repeat, multiArm='greedy',
+ # legendLoc='lower right',
+ # epsilon=[0.05, 0.1, 0.15, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8,0.9])
+
+ """Fig. 11 - Extending 0.2-greedy to 5000 iterations"""
+ # data = rep(repeat=repeat, Actions=Actions, multiArm='greedy',
+ # epsilon=0.2, I=5000)
+ # stackPlot(data, r=0, Iterations=5000, Actions=Actions,
+ # titleComment="0.2 - greedy", legendLoc='lower left')
+
+ """Fig. 12 - Comparing composition on epsilon (decrease)"""
+ # stackBar(0, Actions, repeat=repeat, multiArm='decrease',
+ # legendLoc='lower left',
+ # epsilon=[0.1, 0.4, 0.8, 0.9, 0.95, 0.98, 0.99, 0.999, 0.9999])
+
+ """Fig. 13 - Extending 0.999-decrease to 5000 iterations"""
+ # data = rep(repeat=repeat, Actions=Actions, multiArm='decrease',
+ # epsilon=0.999, I=5000)
+ # stackPlot(data, r=0, Iterations=5000, Actions=Actions,
+ # titleComment="0.999 - decrease", legendLoc='lower left')
+
+
+
"""
Graph1: Number of Actions of Round r (start by 0) by Iteration
@@ -242,35 +361,56 @@ def main():
# 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])
+ # 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
+ Graph4: Actions by different epsilon with multi-arm bandit algorithms
"""
- # 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)
+ # stackBar(0, Actions, repeat=30, multiArm='greedy', legendLoc='lower right',
+ # epsilon=[0.05, 0.1, 0.15, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
+ # threshold=0.3, RF=2)
+
+
+ # data = rep(repeat=30, Actions=Actions, multiArm='greedy', epsilon=0.05)
+ # stackPlot(data, r=0, Iterations=I, Actions=Actions, titleComment="0.05 - greedy",legendLoc='lower left')
+ #
+ # data = rep(repeat=30, Actions=Actions, multiArm='greedy', epsilon=0.1)
+ # stackPlot(data, r=0, Iterations=I, Actions=Actions, titleComment="0.1 - greedy",legendLoc='lower left')
+ #
+ # data = rep(repeat=30, Actions=Actions, multiArm='greedy', epsilon=0.2)
+ # stackPlot(data, r=0, Iterations=I, Actions=Actions, titleComment="0.2 - greedy",legendLoc='lower left')
+
+
+
+ # data = rep(repeat=30, Actions=Actions, multiArm='greedy', epsilon=0.6, I=5000)
+ # stackPlot(data, r=0, Iterations=5000, Actions=Actions, titleComment="0.6 - greedy", legendLoc='lower left')
+
+ # data = rep(repeat=30, Actions=Actions, multiArm='decrease', epsilon=0.9)
+ # stackPlot(data, r=0, Iterations=I, Actions=Actions, titleComment="0.9 - decrease",legendLoc='lower left')
+
+ # data = rep(repeat=30, Actions=Actions, multiArm='decrease', epsilon=0.99)
+ # stackPlot(data, r=0, Iterations=I, Actions=Actions, titleComment="0.99 - decrease",legendLoc='lower left')
+ #
+ # data = rep(repeat=30, Actions=Actions, multiArm='decrease', epsilon=0.999)
+ # stackPlot(data, r=0, Iterations=I, Actions=Actions, titleComment="0.999 - decrease",legendLoc='lower left')
+
+
+
+
+
+
+
"""
T-Test
@@ -293,6 +433,29 @@ def main():
# 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
+ # base = repHist(30, Actions, alpha=1, RF=2, threshold=0.2)
+ # for alpha in np.arange(0.8, 1, 0.01):
+ # compare = repHist(30, Actions, alpha=alpha, RF=2, threshold=0.2)
+ # print("Alpha=", alpha, stats.ttest_ind(base, compare))
+
+ """Epsilon-decrease 0.99 with 0.1 and 0.999"""
+
+ # base = repHist(30, Actions, multiArm='decrease', epsilon=0.99)
+ # for epsilon in (0.1, 0.999):
+ # compare = repHist(30, Actions, multiArm='decrease', epsilon=epsilon)
+ # print("Epsilon=", epsilon, stats.ttest_ind(base, compare))
+
+ """T-TEST for 0.999-decrease 5000 iterations with 0.9"""
+ # base = repHist(30, Actions, multiArm='decrease', epsilon=0.9)
+ # compare = repHist(30, Actions, multiArm='decrease', epsilon=0.999, I=5000)
+ # print(stats.ttest_ind(base, compare))
+
+
+
+
+
+
+
"""T-TEST K,P"""
# t_test(30, Actions, K=(2, 99), P=0) #pvalue=0.4278
diff --git a/utilis.py b/utilis.py
deleted file mode 100644
index 8d466778debea4912cf9f3e5f9a2847903e3bbe0..0000000000000000000000000000000000000000
--- a/utilis.py
+++ /dev/null
@@ -1,18 +0,0 @@
-file = open("workfile","r")
-print(file.readline())
-
-
-# regular expression
-
-
-def read():
-
-
- map = {'N':100, 'M':2}
-
- return map
-
-
-
-def combine_dict(dict1, dict2):
- return {k: (dict1[k] + dict2[k]) for k in dict1}
\ No newline at end of file
diff --git a/workfile b/workfile
deleted file mode 100644
index 07cfd59aa3e976ed63829aaf838acbd27ba190dc..0000000000000000000000000000000000000000
--- a/workfile
+++ /dev/null
@@ -1,2 +0,0 @@
-N,100
-#Number of players in a game