def do_alphaBeta(board, player, ply, alpha, beta, depth):
"""
For memoization.
"""
nonlocal node_count
nonlocal table_hits
# update stats
node_count += 1
# Transposition:
board_hash = board.encode()
if USE_TRANSPOSITION and board_hash in rec_table and\
depth <= rec_table[board_hash][1]:
table_hits += 1
return rec_table[board_hash][0]
# evaluate board
b_eval = Evaluate(board)
if b_eval.utility(ply) != Constants.NON_TERMINAL: # terminal node
ret = b_eval.utility(ply)
elif depth <= 0:
ret = b_eval.evaluation()
else:
successors = board.successors(player)
if len(successors) <= 0:
ret = Constants.DRAW
elif player == Constants.MAX:
v = Constants.NEGINF
for child in successors:
v = max(v, do_alphaBeta(child, Constants.MIN, ply+1, \
alpha, beta, depth-1))
alpha = max(alpha, v)
if beta <= alpha:
break # beta cut-off
ret = v
else:
v = Constants.INF
for child in successors:
v = min(v, do_alphaBeta(child, Constants.MAX, ply+1, \
alpha, beta, depth-1))
beta = min(beta, v)
if beta <= alpha:
break # alpha cut-off
ret = v
# Transposition:
if USE_TRANSPOSITION:
rec_table[board_hash] = (ret, depth)
return ret
def do_minimax(board, player, ply, depth):
"""
For memoization.
"""
# Stats:
nonlocal node_count
nonlocal table_hits
node_count += 1
# Transposition:
board_hash = board.encode()
if USE_TRANSPOSITION and board_hash in rec_table and\
depth <= rec_table[board_hash][1]:
table_hits += 1
return rec_table[board_hash][0]
# evaluate board
b_eval = Evaluate(board)
if b_eval.utility(ply) != Constants.NON_TERMINAL: # End game
ret = b_eval.utility(ply)
elif depth <= 0: # max search depth hit
ret = b_eval.evaluation()
else: # recursive case
successors = board.successors(player)
# No successors is a draw
if len(successors) <= 0:
ret = Constants.DRAW
elif player == Constants.MAX:
best_value = Constants.NEGINF
for succ in successors:
v = do_minimax(succ, Constants.MIN, ply + 1, depth - 1)
best_value = max(best_value, v)
ret = best_value
else: # if player is minimizer
best_value = Constants.INF
for succ in successors:
v = do_minimax(succ, Constants.MAX, ply + 1, depth - 1)
best_value = min(best_value, v)
ret = best_value
# Transposition:
if USE_TRANSPOSITION:
rec_table[board_hash] = (ret, depth)
return ret
Created on Tue Jun 16 17:57:09 2015
@author: Paco
"""
from utils import Utils
from evaluate import Evaluate
from metrics import Metrics
# Load data
u = Utils()
train_hard = u.load_matrix('data/data_train_difficile.mat')
#generate pairs
pairs_idx, pairs_label = u.generate_pairs(train_hard['label'], 1000, 0.1)
# Calculate distance
m = Metrics()
dist = m.braycurtis_dist(train_hard['X'], pairs_idx)
# Evaluate model
e = Evaluate()
e.evaluation(pairs_label,dist)
# display results
e.display_roc()
e.hard_score()
# Evaluate test dataset and save it
test_hard = u.load_matrix('data/data_test_difficile.mat')
dist_test = m.braycurtis_dist(test_hard['X'], test_hard['pairs'])
u.save_test(dist_test,filetxt='soumission_dur.txt')
