In [1]:
import matplotlib.pyplot as plt
%matplotlib inline
In [2]:
import numpy as np
probs = np.genfromtxt('normed_count.csv',delimiter=',')
def move_is_correct(grid,num):
'''
@param grid: 6x7 grid containing the current game state
@param num: column
returns True if move is allowed on that column
'''
#if 0 is in column
if 0 in grid[:,num]:
#move is allowed
return True
else:
return False
def move_still_possible(S):
'''
@param S: 6x7 grid containing the current game state
returns True if grid contains no 0, therefore no move possible anymore
'''
return not(S[S==0].size == 0)
def move(S,p,col_num):
'''
@param S: 6x7 grid containing the current game state
@param p: current player
@param col_num: column number
sets the player's number on the grid and returns the grid
'''
#sanity check
if 0 in S[:,col_num]:
y = np.where(S[:,col_num]==0)[0][-1]
S[y,col_num] = p
return S , y, col_num
else:
return S, None, None
return
def move_probabilistic(S, p):
#all available columns that are not already full
_ , col = np.where(S == 0)
col_num=np.unique(col)
#x of available all columns
x_to_col_num=[np.where(S[:,x] == 0)[0][-1] for x in np.unique(col)]
#determine free position with max prob
m = max(probs[x_to_col_num,col_num])
#and the index to that value
_ , xy = np.where(probs==m)
return xy
def move_at_random(S):
'''
@param S: 6x7 grid containing the current game state
moves at random
'''
return np.random.randint(0,S.shape[1])
#neat and ugly but the fastest way to search a matrix for a vector is a string find
player1 = '1 1 1 1'
oponent = '2 2 2 2'
def move_was_winning_move(S, p):
'''
@param S: 6x7 grid containing the current game state
@param p: current player
combines all the allowed formations of the grid and string_finds with
the currents player vector. Returns true if match.
'''
if p == 1:
match = player1
else:
match = oponent
l=[]
#for every possible diag
for i in range(-2,4):
l.append(np.diag(S,k = i))
l.append(np.diag(np.fliplr(S),k=i))
#left to right
l.append(S)
#top to bottom
l.append(np.rot90(S))
if ''.join(np.array_str(e) for e in l).find(match) > -1:
return True
return False
# relate numbers (1, -1, 0) to symbols ('x', 'o', ' ')
symbols = {1:'b', 2:'r', 0:' '}
# print game state matrix using symbols
def print_game_state(S):
B = np.copy(S).astype(object)
for n in [1, 2, 0]:
B[B==n] = symbols[n]
print B
if __name__ == '__main__':
outcomes = []
for i in range(2000):
# initialize 6x7 connectfour board
gameState = np.zeros((6,7), dtype=int)
# initialize player number, move counter
player = 1
mvcntr = 1
# initialize flag that indicates win
noWinnerYet = True
while move_still_possible(gameState) and noWinnerYet:
while True:
# get player symbol
name = symbols[player]
#print '%s moves' % name
# let player move at random
if player == 1:
col_num = move_at_random(gameState)
#col_num, _ = move_probabilistic(gameState, player)
# player o/r uses statistic
else:
col_num = move_probabilistic(gameState, player)
if move_is_correct(gameState, col_num):
gameState, _ , _ = move(gameState,player,col_num)
# print current game state
#print_game_state(gameState)
# evaluate game state
if move_was_winning_move(gameState, player):
#print 'player %s wins after %d moves' % (name, mvcntr)
noWinnerYet = False
outcomes.append(player)
# switch player and increase move counter
if player == 1:
player = 2
elif player == 2:
player = 1
mvcntr += 1
break
if noWinnerYet:
#print 'game ended in a draw'
outcomes.append(0)
In [3]:
DISCUSSION EXAMPLE 1.
[[' ' ' ' ' ' ' ' ' ' ' ' ' ']
[' ' ' ' ' ' ' ' ' ' ' ' ' ']
[' ' ' ' ' ' ' ' ' ' ' ' ' ']
['b' ' ' ' ' 'r' 'r' 'b' ' ']
['b' ' ' 'b' 'r' 'r' 'b' ' ']
['b' ' ' 'r' 'r' 'r' 'b' 'b']]
this demonstartes quite clearly the problem of that approach. Player o seems to want to be
extremyl sure to win look at the corresponding probs
he build his o extractly like the corresponding heatmap
[[ 0.14303329 0.15659679 0.1405672 0.11960543 0.14426634 0.14303329
0.15289766]
[ 0.13980583 0.14045307 0.14174757 0.14627832 0.14822006 0.13980583
0.14368932]
[ 0.13335518 0.14089122 0.15104849 0.14777195 0.14482307 0.14711664
0.13499345]
[ 0.13774898 0.14470842 0.14134869 0.15166787 0.15022798 0.14350852
0.13078954]
[ 0.12749728 0.1422085 0.14965492 0.16345805 0.15201598 0.1394842
0.12568108]
[ 0.13320377 0.13858574 0.15278816 0.17312005 0.14785469 0.13006428
0.12438332]]
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his = plt.hist(outcomes,bins=3)
offset = -.3
plt.title("2nd Tournament, draws and wins")
#plt.xlabel("left: o wins, middle: draw, right: x wins")
plt.ylabel("# Games")
axes = plt.gca()
axes.set_ylim([0,2100]) # y axis should include all 2000 games
axes.set_xlim([0,2.0])
axes.set_xticks(his[1][1:]+offset)
axes.set_xticklabels( ('draw', 'Blue wins', 'Red wins') )
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