def export_path_html(self, key):
if len(self.selection)>0:
explst = self.selection
print 'export selection;', len(self.selection)
else:
explst = self.new_votes
print 'export votes;', len(self.new_votes)
query = [p for p in list(explst) if not p in self.trash]
self.message('\n'.join([
'Export assemblage of selected images. ({} imgs)'.format(
len(query[:20])),
'','This may take a while.']))
# FIXME: this is limited to 20 images???
path = index.chain(query=query[:20])
index.export_html(path, 'selection.html')
self.redraw=True
D_op = np.load("var_p_one_30n_50_10.npy") # read OSI profiles
Q_p = np.load("var_p_Q_30n.npy").item().get(0) # read accuracy
count = 0
chain1 = []
chain2 = []
chain3 = []
ban1 = []
ban2 = []
ban3 = []
for i in range(var_p): # get statistics from profiles
for j in range(rp):
print("processing #", count, "/", var_p * rp * 2)
count = count + 1
d = D_bp.item().get(i * rp + j)
r.append([p[i], "IBRD", index.del_r(d, n)])
chain = index.chain(d, n)
lon.append([p[i], "IBRD", chain[0]])
short.append([p[i], "IBRD", chain[1]])
avglen.append([p[i], "IBRD", chain[2]])
for k in range(n):
ban.append(cal_banzhaf(d, n, k, beta))
ban_s = index.banzhaf(ban, n)
ma.append([p[i], "IBRD", ban_s[0]])
mi.append([p[i], "IBRD", ban_s[1]])
avgban.append([p[i], "IBRD", ban_s[2]])
gini.append([p[i], "IBRD", index.gini(ban, n)])
acu.append([p[i], "IBRD", index.average_accuracy(d, Q_p, n)])
ban = []
for j in range(rp):
print("processing #", count, "/", var_p * rp * 2)
count = count + 1
chain3 = []
ban1 = []
ban2 = []
ban3 = []
concon = 0 # count non-converging cases
for i in range(len(A)): # get statistics from profiles
name1 = "q in [0.0, 1.0]"
for j in range(rp):
print("processing #", count, "/", len(A) * rp * 2)
count = count + 1
d = D_bb.item().get(i * rp + j)
if 30 in d:
concon = concon + 1
continue
r.append([A[i], name1, index.del_r(d, n)])
chain = index.chain(d, n)
lon.append([A[i], name1, chain[0]])
short.append([A[i], name1, chain[1]])
avglen.append([A[i], name1, chain[2]])
for k in range(n):
ban.append(cal_banzhaf(d, n, k, beta))
# ban.append(random.uniform(0, 1))
ban_s = index.banzhaf(ban, n)
ma.append([A[i], name1, ban_s[0]])
mi.append([A[i], name1, ban_s[1]])
avgban.append([A[i], name1, ban_s[2]])
gini.append([A[i], name1, index.gini(ban, n)])
acu.append([A[i], name1, index.average_accuracy(d, Q_mis, n)])
ban = []
con.append([A[i], 1, 50 - concon])
concon = 0
import index
from random import randrange as rnd
index.load()
for n in range(6):
stars = sorted(index.pictures(), key=lambda p:p.rating, reverse=True)[:5+n*2]
query = []
print 'Checkpoints:'
for i in range(3+n):
p = stars[rnd(len(stars))]
query.append(p)
stars.remove(p)
print '{}. {}'.format(i+1, p.name)
path = index.chain(query=query)
print '\nPath:'
print ' > '.join([p.name for p in path])
reference = [p for p in query if p in path]
index.export_html(reference+[None]+path, 'path{}.html'.format(n))