def speed_test():
from approximator import Approximator
import time
import tensorflow as tf
from learn.preprocessing import faster_featurize
import settings
from environment import load_DS
settings.init()
load_DS('dataset/krk.epd')
settings.params['PL'] = list('KRkr')
model_fn = 'Models/stem_leaf/TDLeaf/TDLeaf_stem_or_leaf_7__03_07/TDLeaf_stem_or_leaf_7__03_07-1_13299-0'
with tf.Session() as sess:
saver = tf.train.import_meta_graph(model_fn + '.meta')
saver.restore(sess, model_fn)
approx = Approximator(sess)
V = approx.value
F = faster_featurize
avg_time1 = 0
avg_time2 = 0
avg_time3 = 0
for _ in xrange(20):
env = Environment()
flag = False
mv_cnt = 0
time1 = 0
time2 = 0
time3 = 0
while not flag:
if env.is_terminal():
flag = True
else:
start = time.time()
a, score = alphabeta_native(V, F, env, 3, -float('inf'),
float('inf'))
end = time.time()
a2, score2 = alphabeta_batch_hist(V, F, env,
list(env.hist.keys()), 3,
-float('inf'),
float('inf'))
end2 = time.time()
a3, score = alphabeta_batch(V, F, env, 3, -float('inf'),
float('inf'))
end3 = time.time()
env.perform_action(a)
time1 += end - start
time2 += end2 - end
time3 += end3 - end2
mv_cnt += 1
avg_time1 += time1 / mv_cnt
avg_time2 += time2 / mv_cnt
avg_time3 += time3 / mv_cnt
print avg_time1 / 100, avg_time2 / 100, avg_time3 / 100
help='number of episodes to play',
type=int)
parser.add_argument('-p', help='piece cfg')
parser.add_argument('-D', help='dset file')
parser.add_argument(
'-R',
default=10,
type=int,
help='number of random moves to play before registration')
parser.add_argument('-d', default=3, type=int, help='depth')
parser.add_argument('-w', action='store_true')
args = parser.parse_args()
settings.init()
settings.params['USE_DSET'] = True
settings.params['PL'] = args.p
load_DS(args.D)
settings.params['RAND'] = args.R
settings.params['OC_DEPTH'] = args.d
model_fn = args.c
with tf.Session() as sess:
saver = tf.train.import_meta_graph(model_fn + '.meta')
saver.restore(sess, model_fn)
approx = Approximator(sess)
agent = tdstem.TDStemPlayAgent(approx, depth=3)
A, evaldict, all_s = opt.recursive_eval_sim(agent, N=args.N, w=args.w)
with open(args.o, 'wb') as f:
cp.dump((A, evaldict, all_s), f)
def comparison_3p():
import tablebases
with open('Models/3P/TDS4/meta','rb') as f:
stem=cp.load(f)
with open('Models/3P/TDL4/meta','rb') as f:
leaf=cp.load(f)
settings.init()
load_DS('dataset/3p_2.epd')
settings.params['PL']='KQRBNPkqrbnp'
settings.params['USE_DSET']=True
N_l=np.array([0]+leaf['N'],dtype=float)
N_s=np.array([1]+stem['N'],dtype=float)
eps_l=leaf['eps']
eps_s=stem['eps']
w_l=np.array([0]+leaf['w_list'])/N_l
w_s=np.array([0]+stem['w_list'])/N_s
e_l=np.cumsum(N_l)
e_s=np.cumsum(N_s)
plt.figure(1)
plt.subplot(221)
line_stem, =plt.plot(e_s,w_s,label='TD-Stem'+r'$(\lambda)$')
line_leaf, =plt.plot(e_l,w_l,label='TD-Leaf'+r'$(\lambda)$')
plt.xlabel(r'$N$')
plt.ylabel('winning rate')
plt.legend(handles=[line_stem])
#plt.show()
for i in range(3,8):
with open('Models/3P/TDL{}/meta'.format(i),'rb') as f:
leaf=cp.load(f)
with open('Models/3P/TDS{}/meta'.format(i),'rb') as f:
stem=cp.load(f)
print leaf['lambda']
with open('Models/3P/TDL{}/sim'.format(i),'rb') as f:
A,evaldict,S=cp.load(f)
wcl=np.mean(np.array(evaldict['wc']))
wel=np.mean(np.array(evaldict['we']))
lhsl=np.mean(np.array(evaldict['lhs']))
with open('Models/3P/TDS{}/sim'.format(i),'rb') as f:
A,evaldict,S=cp.load(f)
wcs=np.mean(np.array(evaldict['wc']))
wes=np.mean(np.array(evaldict['we']))
lhss=np.mean(np.array(evaldict['lhs']))
print '{}: STEM: {}, {}, {} \tLEAF: {}, {}, {}'.format(i,wcs,wes,lhss,wcl,wel,lhsl)
mps_s=np.mean(np.array(stem['mps']))
mps_l=np.mean(np.array(leaf['mps']))
print '{}, {}'.format(mps_s,mps_l)
print stem['episodes']
'''
Sw=[t[-1] for t in tw]
print Sw[:100]
dtmw=[t[1] for t in tw]
wdlw=[t[0] for t in tw]
vw_s=Approximator.V(Sw,model_fn)
hist_wcs=90*[0]
hist_wes=90*[0]
hist_lhss=90*[0]
hist_dcs=90*[0]
avg_vs=90*[0]
#print A
for i in xrange(len(hist_wcs)):
hist_wcs[i]=np.mean(np.array([wc(t) for t in A if wc(t) is not None and
t[1]==i+1]))
hist_wes[i]=np.mean(np.array([we(t) for t in A if we(t) is not None and
t[1]==i+1]))
hist_lhss[i]=np.mean(np.array([lhs(t) for t in A if lhs(t) is not None and
t[1]==i+1]))
hist_dcs[i]=np.mean(np.array([dc(t) for t in A if dc(t) is not None and
t[1]==i+1]))
avg_vs[i]=np.mean(np.array([vw_s[j] for j in xrange(vw_s.shape[0]) if
dtmw[j]==i and wdlw[j]==1]))
'''
'''
model_fn='Models/3PIECES_LEAF/TDLeaf_3pieces_leaf_5__25_07/TDLeaf_3pieces_leaf_5__25_07-1_67793-40'
with open('Models/3PIECES_LEAF/sim','rb') as f:
A,evaldict,S=cp.load(f)
dcl=np.mean(np.array(evaldict['dc']))
tw=[t for t in A if tablebases.probe_result(t[-1])==1]
td=[t for t in A if tablebases.probe_result(t[-1])==0]
tb=[t for t in A if tablebases.probe_result(t[-1])==-1]
Sw=[t[-1] for t in tw]
dtmw=[t[1] for t in tw]
wdlw=[t[0] for t in tw]
vw_l=Approximator.V(Sw,model_fn)
hist_wcl=90*[0]
hist_wel=90*[0]
hist_lhsl=90*[0]
hist_dcl=90*[0]
avg_vl=90*[0]
#print A
for i in xrange(len(hist_wcs)):
hist_wcl[i]=np.mean(np.array([wc(t) for t in A if wc(t) is not None and
t[1]==i+1]))
hist_wel[i]=np.mean(np.array([we(t) for t in A if we(t) is not None and
t[1]==i+1]))
hist_lhsl[i]=np.mean(np.array([lhs(t) for t in A if lhs(t) is not None and
t[1]==i+1]))
hist_dcl[i]=np.mean(np.array([dc(t) for t in A if dc(t) is not None and
t[1]==i+1]))
avg_vl[i]=np.mean(np.array([vw_l[j] for j in xrange(vw_l.shape[0]) if
dtmw[j]==i and wdlw[j]==1]))
'''
'''
def comparison_stem_leaf_kqk():
model_fn_leaf='Models/KQK/TDL/network'
with open('Models/KQK/TDL_BAD/sim','rb') as f:
A,evaldict,S=cp.load(f)
wc_l=np.mean(np.array(evaldict['wc']))
we_l=np.mean(np.array(evaldict['we']))
lhs_l=np.mean(np.array(evaldict['lhs']))
#t=stem['']
print wc_l, we_l, lhs_l
model_fn_leaf='Models/KQK/TDS/network'
with open('Models/KQK/TDS_BAD/sim','rb') as f:
A,evaldict,S=cp.load(f)
wc_s=np.mean(np.array(evaldict['wc']))
we_s=np.mean(np.array(evaldict['we']))
lhs_s=np.mean(np.array(evaldict['lhs']))
#t=stem['']
print wc_s, we_s, lhs_s
with open('Models/KQK/TDL_BAD/meta','rb') as f:
leaf=cp.load(f)
with open('Models/KQK/TDS_BAD/meta','rb') as f:
stem=cp.load(f)
mps_s=np.mean(np.array(stem['mps']))
mps_l=np.mean(np.array(leaf['mps']))
ntot_s=stem['episodes']
ntot_l=leaf['episodes']
el_s=stem['elapsed_time']
el_l=leaf['elapsed_time']
print mps_s, mps_l, ntot_s, ntot_l, el_s, el_l
import tablebases
with open('Models/KQK/TDL/meta','rb') as f:
leaf=cp.load(f)
with open('Models/KQK/TDS/meta','rb') as f:
stem=cp.load(f)
settings.init()
load_DS('dataset/kqk_fics.epd')
settings.params['PL']='KQkq'
settings.params['USE_DSET']=True
N_l=np.array([0]+leaf['N'],dtype=float)
N_s=np.array([0]+stem['N'],dtype=float)
eps_l=leaf['eps']
eps_s=stem['eps']
w_l=np.array([0]+leaf['w_list'])/N_l
w_s=np.array([0]+stem['w_list'])/N_s
e_l=np.cumsum(N_l)
e_s=np.cumsum(N_s)
stages_s=[t[0] for t in stem['lambda']]
stages_l=[t[0] for t in leaf['lambda']]
print leaf['lambda']
l_l=leaf['avg_len']
l_s=stem['avg_len']
plt.figure(1)
plt.subplot(111)
line_stem, =plt.plot(e_s,w_s,label='TD-Stem'+r'$(\lambda)$')
line_leaf, =plt.plot(e_l,w_l,label='TD-Leaf'+r'$(\lambda)$')
for i in stages_s:
plt.axvline(x=i,color='#99ccff')
for i in stages_l:
plt.axvline(x=i,color='#ffc266')
plt.xlabel(r'$N$')
plt.ylabel('winning rate')
plt.legend(handles=[line_leaf,line_stem])
plt.xlim(0,max(max(e_l),max(e_s)))
plt.ylim(0,1)
plt.show()
mps_s=np.mean(np.array(stem['mps']))
mps_l=np.mean(np.array(leaf['mps']))
ntot_s=stem['episodes']
ntot_l=leaf['episodes']
el_s=stem['elapsed_time']
el_l=leaf['elapsed_time']
print mps_s, mps_l, ntot_s, ntot_l, el_s, el_l
model_fn_stem='Models/KQK/TDS/network'
with open('Models/KQK/TDS/sim2','rb') as f:
A,evaldict,S=cp.load(f)
wc_s=np.mean(np.array(evaldict['wc']))
we_s=np.mean(np.array(evaldict['we']))
lhs_s=np.mean(np.array(evaldict['lhs']))
#t=stem['']
print wc_s, we_s, lhs_s
tw=[t for t in A if tablebases.probe_result(t[-1])==1]
td=[t for t in A if tablebases.probe_result(t[-1])==0]
tb=[t for t in A if tablebases.probe_result(t[-1])==-1]
Sw=[t[-1] for t in tw]
dtmw=[t[1] for t in tw]
print min(dtmw)
wdlw=[t[0] for t in tw]
Sb=[t[-1] for t in tb]
dtmb=[t[1] for t in tb]
wdlb=[t[0] for t in tb]
print min(dtmb)
vw_s=Approximator.V(Sw,model_fn_stem)
vb_s=Approximator.V(Sb,model_fn_stem)
hist_wcs=20*[0]
hist_wes=20*[0]
hist_lhss=20*[0]
hist_dcs=20*[0]
avg_vs=20*[0]
std_vs=20*[0]
avg_vsb=20*[0]
std_vsb=20*[0]
#print A
for i in xrange(len(hist_wcs)):
hist_wcs[i]=np.mean(np.array([wc(t) for t in A if wc(t) is not None and
t[1]==i+1]))
hist_wes[i]=np.mean(np.array([we(t) for t in A if we(t) is not None and
t[1]==i+1]))
hist_lhss[i]=np.mean(np.array([lhs(t) for t in A if lhs(t) is not None and
t[1]==i+1]))
hist_dcs[i]=np.mean(np.array([dc(t) for t in A if dc(t) is not None and
t[1]==i+1]))
avg_vs[i]=np.mean(np.array([vw_s[j] for j in xrange(vw_s.shape[0]) if
dtmw[j]==i+1 ]))
std_vs[i]=np.std(np.array([vw_s[j] for j in xrange(vw_s.shape[0]) if
dtmw[j]==i+1 and wdlw[j]==1]))
avg_vsb[i]=np.mean(np.array([vb_s[j] for j in xrange(vb_s.shape[0]) if
dtmb[j]==i+1 ]))
std_vsb[i]=np.std(np.array([vb_s[j] for j in xrange(vb_s.shape[0]) if
dtmb[j]==i+1 and wdlb[j]==1]))
model_fn_leaf='Models/KQK/TDL/network'
with open('Models/KQK/TDL/sim2','rb') as f:
A,evaldict,S=cp.load(f)
wc_l=np.mean(np.array(evaldict['wc']))
we_l=np.mean(np.array(evaldict['we']))
lhs_l=np.mean(np.array(evaldict['lhs']))
#t=stem['']
print wc_l, we_l, lhs_l
tw=[t for t in A if tablebases.probe_result(t[-1])==1]
td=[t for t in A if tablebases.probe_result(t[-1])==0]
tb=[t for t in A if tablebases.probe_result(t[-1])==-1]
Sw=[t[-1] for t in tw]
dtmw=[t[1] for t in tw]
wdlw=[t[0] for t in tw]
Sb=[t[-1] for t in tb]
dtmb=[t[1] for t in tb]
wdlb=[t[0] for t in tb]
vw_l=Approximator.V(Sw,model_fn_leaf)
vb_l=Approximator.V(Sb,model_fn_leaf)
hist_wcl=20*[0]
hist_wel=20*[0]
hist_lhsl=20*[0]
hist_dcl=20*[0]
avg_vl=20*[0]
std_vl=20*[0]
avg_vlb=20*[0]
std_vlb=20*[0]
for i in xrange(len(hist_wcs)):
hist_wcl[i]=np.mean(np.array([wc(t) for t in A if wc(t) is not None and
t[1]==i+1]))
hist_wel[i]=np.mean(np.array([we(t) for t in A if we(t) is not None and
t[1]==i+1]))
hist_lhsl[i]=np.mean(np.array([lhs(t) for t in A if lhs(t) is not None and
t[1]==i+1]))
hist_dcl[i]=np.mean(np.array([dc(t) for t in A if dc(t) is not None and
t[1]==i+1]))
avg_vl[i]=np.mean(np.array([vw_l[j] for j in xrange(vw_l.shape[0]) if
dtmw[j]==i+1 ]))
std_vl[i]=np.std(np.array([vw_l[j] for j in xrange(vw_l.shape[0]) if
dtmw[j]==i+1 and wdlw[j]==1]))
avg_vlb[i]=np.mean(np.array([vb_l[j] for j in xrange(vb_l.shape[0]) if
dtmb[j]==i+1 ]))
std_vlb[i]=np.std(np.array([vb_l[j] for j in xrange(vb_l.shape[0]) if
dtmb[j]==i+1 and wdlb[j]==1]))
x=np.array(range(1,len(hist_wcs)+1))
plt.figure(2)
plt.subplot(111)
b1=plt.bar(x-1./6, hist_wcs,width=1./3,align='center',label='TD-Stem'+r'$(\lambda)$ ')
b2=plt.bar(x+1./6, hist_wcl,width=1./3,align='center',label='TD-Leaf'+r'$(\lambda)$ ')
#plt.title('kqk endgame win conversion rate')
plt.legend(handles=[b1,b2])
plt.xlabel('DTM')
plt.ylabel('WCR')
plt.xlim(0,x.max())
plt.ylim(0,1)
plt.xticks(x,x)
plt.show()
plt.figure(3)
plt.subplot(111)
b1=plt.bar(x-1./6, hist_wes,width=1./3,align='center',label='TD-Stem'+r'$(\lambda)$ ')
b2=plt.bar(x+1./6, hist_wel,width=1./3,align='center',label='TD-Leaf'+r'$(\lambda)$ ')
#plt.title('kqk endgame win efficiency')
plt.legend(handles=[b1,b2])
plt.xlabel('DTM')
plt.ylabel('WE')
plt.xlim(0,x.max())
plt.ylim(0,1)
plt.xticks(x,x)
plt.show()
plt.figure(4)
plt.subplot(111)
b1=plt.bar(x-1./6, hist_lhss,width=1./3,align='center',label='TD-Stem'+r'$(\lambda)$ ')
b2=plt.bar(x+1./6, hist_lhsl,width=1./3,align='center',label='TD-Leaf'+r'$(\lambda)$ ')
#plt.title('kqk endgame loss holding score')
plt.legend(handles=[b1,b2])
plt.xlabel('DTM')
plt.ylabel('LHS')
plt.xlim(0,x.max())
plt.ylim(0,1)
plt.xticks(x,x)
plt.show()
plt.figure(5)
b1,=plt.plot(x,avg_vs,label='TD-Stem'+r'$(\lambda)$ ')
b2,=plt.plot(x,avg_vl,label='TD-Leaf'+r'$(\lambda)$ ')
s1,=plt.plot(x,np.array(avg_vs)+2*np.array(std_vs),color='#99ccff')
s2,=plt.plot(x,np.array(avg_vs)-2*np.array(std_vs),color='#99ccff')
s3,=plt.plot(x,np.array(avg_vl)+2*np.array(std_vl),color='#ffc266')
s4,=plt.plot(x,np.array(avg_vl)-2*np.array(std_vl),color='#ffc266')
c1,=plt.plot(x,avg_vsb,label='TD-Stem'+r'$(\lambda)$ ',color=b1.get_color())
c2,=plt.plot(x,avg_vlb,label='TD-Leaf'+r'$(\lambda)$ ',color=b2.get_color())
t1,=plt.plot(x,np.array(avg_vsb)+2*np.array(std_vsb),color='#99ccff')
t2,=plt.plot(x,np.array(avg_vsb)-2*np.array(std_vsb),color='#99ccff')
t3,=plt.plot(x,np.array(avg_vlb)+2*np.array(std_vlb),color='#ffc266')
t4,=plt.plot(x,np.array(avg_vlb)-2*np.array(std_vlb),color='#ffc266')
plt.xticks(x,x)
#plt.title('krk endgame win conversion rate')
plt.legend(handles=[b1,b2])
plt.xlabel('DTM')
plt.ylabel('E[V]')
plt.xlim(0,x.max())
#plt.ylim(0,1)
plt.show()
def comparison_stem_leaf():
settings.init()
settings.params['USE_DSET']=True
settings.params['PL']='KRkr'
load_DS('dataset/krk.epd')
with open('Models/stem_leaf/TDLeaf/TDLeaf_stem_or_leaf_7__03_07/stem_or_leaf_7_meta.sv','rb') as f:
leaf=cp.load(f)
with open('Models/stem_leaf/TDStem/TDStem_stem_or_leaf_7__28_06/stem_or_leaf_7_meta.sv','rb') as f:
stem=cp.load(f)
print leaf.keys()
print stem.keys()
N_l=leaf['N'][0]
N_s=stem['N'][0]
#print N_l, N_s
w_l=leaf['w_list']
r_l=leaf['r_lists']
l_l=leaf['avg_len']
w_s=stem['w_list']
r_s=stem['r_lists']
l_s=stem['avg_len']
mps_s=np.mean(np.array(stem['mps']))
mps_l=np.mean(np.array(leaf['mps']))
ntot_s=stem['episodes']
ntot_l=leaf['episodes']
el_s=stem['elapsed_time']
el_l=leaf['elapsed_time']
print mps_s, mps_l, ntot_s, ntot_l, el_s, el_l
ep_s2=[0]
rate_s=[]
cumsum=0
for i in xrange(len(w_s)):
if i<53:
cumsum+=5000
rate_s.append(5000.)
elif i<73:
cumsum+=500
rate_s.append(500.)
else:
cumsum+=250
rate_s.append(250.)
ep_s2.append(cumsum)
wr_s=np.array([0]+w_s)/np.array([1]+rate_s)
rrw_s=5000*np.array(r_s[0])/np.array(rate_s)
rrb_s=5000*np.array(r_s[1])/np.array(rate_s)
ep_l2=[0]
rate_l=[]
cumsum=0
for i in xrange(len(w_l)):
if i<63:
cumsum+=5000
rate_l.append(5000.)
elif i<83:
cumsum+=500
rate_l.append(500.)
else:
cumsum+=250
rate_l.append(250.)
ep_l2.append(cumsum)
wr_l=np.array([0]+w_l)/np.array([1]+rate_l)
rrw_l=5000*np.array(r_l[0])/np.array(rate_l)
rrb_l=5000*np.array(r_l[1])/np.array(rate_l)
plt.figure(1)
plt.subplot(111)
line_stem, =plt.plot(ep_s2,wr_s,label='TD-Stem'+r'$(\lambda)$')
for i in [100000,170000,264000,275000,283200,291000]:
plt.axvline(x=i,color='#99ccff')
line_leaf, =plt.plot(ep_l2,wr_l,label='TD-Leaf'+r'$(\lambda)$')
for i in [120000,220000,315000,325250,333000,341000]:
plt.axvline(x=i,color='#ffc266')
plt.xlabel(r'$N$')
plt.ylabel('winning rate')
plt.legend(handles=[line_leaf,line_stem])
plt.xlim(0,max(ep_l2))
plt.ylim(0,1)
#plt.title('krk endgame learning curve')
plt.show()
mps_s=np.mean(np.array(stem['mps']))
mps_l=np.mean(np.array(leaf['mps']))
ntot_s=stem['episodes']
ntot_l=leaf['episodes']
el_s=stem['elapsed_time']
el_l=leaf['elapsed_time']
print mps_s, mps_l, ntot_s, ntot_l, el_s, el_l
model_fn='Models/stem_leaf/TDStem/TDStem_stem_or_leaf_7__28_06/TDStem_stem_or_leaf_7__28_06-1_23116-0'
with open('Models/stem_leaf/TDStem/sim','rb') as f:
A,evaldict,S=cp.load(f)
wc_s=np.mean(np.array(evaldict['wc']))
we_s=np.mean(np.array(evaldict['we']))
lhs_s=np.mean(np.array(evaldict['lhs']))
#t=stem['']
print wc_s, we_s, lhs_s
S=[t[-1] for t in A]
dtm=[t[1] for t in A]
wdl=[t[0] for t in A]
v=Approximator.V(S,model_fn)
hist_wcs=33*[0]
hist_wes=33*[0]
hist_lhss=33*[0]
avg_vs=33*[0]
std_vs=33*[0]
#print A
for i in xrange(len(hist_wcs)):
hist_wcs[i]=np.mean(np.array([wc(t) for t in A if wc(t) is not None and
t[1]==i]))
hist_wes[i]=np.mean(np.array([we(t) for t in A if we(t) is not None and
t[1]==i]))
hist_lhss[i]=np.mean(np.array([lhs(t) for t in A if lhs(t) is not None and
t[1]==i]))
avg_vs[i]=np.mean(np.array([v[j] for j in xrange(v.shape[0]) if
dtm[j]==i and wdl[j]==1]))
std_vs[i]=np.std(np.array([v[j] for j in xrange(v.shape[0]) if
dtm[j]==i and wdl[j]==1]))
model_fn='Models/stem_leaf/TDLeaf/TDLeaf_stem_or_leaf_7__03_07/TDLeaf_stem_or_leaf_7__03_07-1_13299-0'
with open('Models/stem_leaf/TDLeaf/sim','rb') as f:
A,evaldict,S=cp.load(f)
wc_l=np.mean(np.array(evaldict['wc']))
we_l=np.mean(np.array(evaldict['we']))
lhs_l=np.mean(np.array(evaldict['lhs']))
#t=stem['']
print wc_l, we_l, lhs_l
S=[t[-1] for t in A]
dtm=[t[1] for t in A]
wdl=[t[0] for t in A]
v=Approximator.V(S,model_fn)
hist_wcl=33*[0]
hist_wel=33*[0]
hist_lhsl=33*[0]
avg_vl=33*[0]
std_vl=33*[0]
#print A
for i in xrange(len(hist_wcl)):
hist_wcl[i]=np.mean(np.array([wc(t) for t in A if wc(t) is not None and
t[1]==i+1]))
hist_wel[i]=np.mean(np.array([we(t) for t in A if we(t) is not None and
t[1]==i+1]))
hist_lhsl[i]=np.mean(np.array([lhs(t) for t in A if lhs(t) is not None and
t[1]==i+1]))
avg_vl[i]=np.mean(np.array([v[j] for j in xrange(v.shape[0]) if
dtm[j]==i+1 and wdl[j]==1]))
std_vl[i]=np.std(np.array([v[j] for j in xrange(v.shape[0]) if
dtm[j]==i+1 and wdl[j]==1]))
x=np.array(range(1,len(hist_wcs)+1))
plt.figure(2)
plt.subplot(111)
b1=plt.bar(x-1./6, hist_wcs,width=1./3,align='center',label='TD-Stem'+r'$(\lambda)$ ')
b2=plt.bar(x+1./6, hist_wcl,width=1./3,align='center',label='TD-Leaf'+r'$(\lambda)$ ')
#plt.title('krk endgame win conversion rate')
plt.legend(handles=[b1,b2])
plt.xlabel('DTM')
plt.ylabel('WCR')
plt.show()
plt.figure(3)
plt.subplot(111)
b1=plt.bar(x-1./6, hist_wes,width=1./3,align='center',label='TD-Stem'+r'$(\lambda)$ ')
b2=plt.bar(x+1./6, hist_wel,width=1./3,align='center',label='TD-Leaf'+r'$(\lambda)$ ')
#plt.title('krk endgame win efficiency')
plt.legend(handles=[b1,b2])
plt.xlabel('DTM')
plt.ylabel('WE')
plt.show()
plt.figure(4)
plt.subplot(111)
b1=plt.bar(x-1./6, hist_lhss,width=1./3,align='center',label='TD-Stem'+r'$(\lambda)$ ')
b2=plt.bar(x+1./6, hist_lhsl,width=1./3,align='center',label='TD-Leaf'+r'$(\lambda)$ ')
#plt.title('krk endgame loss holding score')
plt.legend(handles=[b1,b2])
plt.xlabel('DTM')
plt.ylabel('LHS')
plt.show()
plt.figure(5)
plt.subplot(111)
b1,=plt.plot(x,avg_vs,label='TD-Stem'+r'$(\lambda)$ ')
b2,=plt.plot(x,avg_vl,label='TD-Leaf'+r'$(\lambda)$ ')
s1,=plt.plot(x,np.array(avg_vs)+2*np.array(std_vs),color='#99ccff')
s2,=plt.plot(x,np.array(avg_vs)-2*np.array(std_vs),color='#99ccff')
s3,=plt.plot(x,np.array(avg_vl)+2*np.array(std_vl),color='#ffc266')
s4,=plt.plot(x,np.array(avg_vl)-2*np.array(std_vl),color='#ffc266')
#plt.title('krk endgame value function')
plt.legend(handles=[b1,b2])
plt.xlabel('DTM')
plt.ylabel(r'$V$')
plt.show()
parser.add_argument('--mode', default='stem')
parser.add_argument('-R', default=2, type=int)
parser.add_argument('--mk', type=int)
parser.add_argument('--ocd', default=5, type=int)
parser.add_argument('--cnn-f', action='store_true')
args = parser.parse_args()
settings.init()
settings.params['USE_DSET'] = args.dset
settings.params['OC_DEPTH'] = args.ocd
settings.params['MK'] = args.mk
change_PL(list(args.piece_conf))
settings.params['RAND'] = args.R
print settings.params['PL']
if settings.params['USE_DSET']:
load_DS(args.ds_file)
if args.eps_factor == 0:
decay_f = lambda n: 1 - 0.01 * n
else:
decay_f = lambda n: (n + 1)**(-args.eps_factor)
if args.old_model == None:
D = faster_featurize('8/6k1/8/8/3K4/8/8/8 w - -').shape[1]
print D
M = [int(m) for m in args.M.split()]
if args.cnn:
c0 = len(args.piece_conf) * 2
if args.cnn_f:
kwargs = {
'D': D,
'M': M,