``````

In [3]:

import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np

``````
``````

In [9]:

import numpy as np

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)

``````

### Tournament Random vs LineProb

``````

In [54]:

#outcomes.append(0)
his = plt.hist(outcomes,bins=3)
offset = -.3
plt.title("Tournament using Line Probabilities, 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') )

``````
``````

Out[54]:

[<matplotlib.text.Text at 0x1141d5610>,
<matplotlib.text.Text at 0x1141e0b50>,
<matplotlib.text.Text at 0x1142725d0>]

``````
``````

In [ ]:

``````