# MarkovChainLearner[v2]

 Author Emmanuel Harish Menon Submission date 2019-05-10 22:30:40.788200 Rating 4630 Matches played 217 Win rate 46.08

Use rpsrunner.py to play unranked matches on your computer.

## Source code:

``````'''
Program Name: MarkovChainLearner v2 [SubmissionCode]
Program by: Emmanuel Harish Menon
Last Updated: 8:26 AM 11/5/19
Explanation:
This program uses Markov chains to respond to the probability that the user will pick rock, paper or scissors. It was made for the contest at rpscontest.com
'''
#import modules
import random
from decimal import Decimal

#transition table of nested dictionaries
transitionTable = {"R": {"S": 0, "P": 0, "R": 0}, "S": {"S": 0, "P": 0, "R": 0}, "P": {"S": 0, "P": 0, "R": 0}}

#stores number of plays
playCount = {"R": 0, "P": 0, "S": 0}

#used to determine a random pick
choices = ["R", "P", "S"]

#stores all move history
moveHistory = []

#this function updates values in transition table
def updater(moveHistory, transitionTable):
#increases the appropriate value in the play count dict
playCount[moveHistory[-1]] += 1

#this will run as long as move history is larger than 1
if len(moveHistory) > 1:
#stores last two plays into individual vars
slice1 = moveHistory[-2]
slice2 = moveHistory[-1]

#stores a portion of the nested dict into editDict
editDict = transitionTable[slice1]

editDict[slice2] += 1

def picker():
#stores the actual percentages for the markov chains while the transitionTable stores integer values
percentageTable = {"R": 0, "P": 0, "S": 0}

#stores a the relevant portion of the nested dict
editDict = transitionTable[moveHistory[-1]]

#creates a list of R, P, S based on percentages in percentageTable
pickList = []

#iterates through and converts the values in the transition table into percentages
for k, v in editDict.items():
if v == 0:
return choices[random.randint(0, 2)]
else:
#converts the float to a decimal to an int
percentageTable[k] = int(Decimal((v / (playCount[moveHistory[-1]]- 1))*100))
for i in range(percentageTable[k]):
pickList.append(k)
#this list will find the predicted user pick
pick = pickList[random.randint(0, len(pickList) - 1)]

#returns a value based on the predicted user's pick
if pick is "R":
return "P"
elif pick is "P":
return "S"
elif pick is "S":
return "R"

if input is not "":
#add the prev pick to the list
moveHistory.append(input)
#update transition table
updater(moveHistory, transitionTable)

if len(moveHistory) == 0:
output = choices[random.randint(0, 2)]
else:
output = picker()``````