def printReport(m):
for i, f in enumerate(m.log.y):
print "\n <f%s" %i
for era in sorted(m.history.keys()):
# pdb.set_trace()
log = m.history[era].log.y[i]
print str(era).rjust(7), xtile(log._cache, width = 33, show = "%5.2f", lo = 0, hi = 1)
def printReport(m, history):
for i, f in enumerate(m.log.y):
print "\n <f%s" % i
for era in sorted(history.keys()):
# pdb.set_trace()
log = history[era].log.y[i]
print str(era).rjust(7), xtile(log._cache,
width=33,
show="%5.2f",
lo=0,
hi=1)
def printSumReport(m, history):
# for i, f in enumerate(m.log.y):
print "\n Objective Value"
for era in sorted(history.keys()):
# pdb.set_trace()
log = [history[era].log.y[k] for k in range(len(m.log.y))]
ss = []
ss.extend([log[s]._cache for s in range(len(log))])
logsum = map(sum, zip(*ss))
minvalue = min(logsum)
maxvalue = max(logsum)
normlog = [(x - minvalue) / (maxvalue - minvalue + 0.00001) for x in logsum]
print str(era).rjust(7), xtile(normlog, width=33, show="%5.2f", lo=0, hi=1)
def printSumReport(m, history):
# for i, f in enumerate(m.log.y):
print "\n Objective Value"
for era in sorted(history.keys()):
# pdb.set_trace()
log = [history[era].log.y[k] for k in range(len(m.log.y))]
ss = []
ss.extend([log[s]._cache for s in range(len(log))])
logsum = map(sum, zip(*ss))
minvalue = min(logsum)
maxvalue = max(logsum)
normlog = [(x - minvalue) / (maxvalue - minvalue + 0.00001)
for x in logsum]
print str(era).rjust(7), xtile(normlog,
width=33,
show="%5.2f",
lo=0,
hi=1)
