def sinbad(self, d, wgt):
w = wgt
d_cmd = ["-d", str(d)]
w_cmd = ["-w", str(w)]
MetaUtils.runtool(self.cmd + d_cmd + w_cmd)
logp = "%s_-d_%s_-w_%s/log" % (self.logd, d, w)
f = MetaUtils.fitness(logp)
w_values = [(w,f)]
while True:
sys.stderr.write("\n(d, w, f) => (%s, %s, %s)\n" % \
(str(d), str(w), str(f)))
neighw = self.neighbourw(w_values, w)
w_cmd = ["-w", str(neighw)]
MetaUtils.runtool(self.cmd + d_cmd + w_cmd)
logp = "%s_-d_%s_-w_%s/log" % (self.logd, d, neighw)
neighf = MetaUtils.fitness(logp)
w_values.append((neighw,neighf))
fits = [f for _,f in w_values]
if MetaUtils.localmax(fits):
for k,f in w_values:
print "(%s,%s)" % (k,f)
fitw,maxf = self.best_w(w_values)
msg = "\n%s ambiguities found" % maxf
msg += "\nd=%s, w=%s\n" % (d, fitw)
sys.stderr.write(msg)
return fitw, maxf
w, f = neighw, neighf
def run(self):
currval = self.initval
amberoptcmd = [self.switch, str(currval)]
MetaUtils.runtool(self.cmd + amberoptcmd)
currfit = MetaUtils.fitness("%s_%s/log" % (self.logd, currval))
self.k_values.append((currval,currfit))
while True:
sys.stderr.write("\n==> current value: %s, fitness: %s\n" \
% (str(currval),str(currfit)))
neighval = self.neighbour(currval)
sys.stderr.write("neighval: %s" % (str(neighval)))
amberoptcmd = [self.switch, str(neighval)]
MetaUtils.runtool(self.cmd + amberoptcmd)
neighfit = MetaUtils.fitness("%s_%s/log" % (self.logd, neighval))
sys.stderr.write("new fitness: %s " % str(neighfit))
self.k_values.append((neighval,neighfit))
fitvals = [f for _,f in self.k_values]
if MetaUtils.localmax(fitvals, 3):
MetaUtils.report(self.k_values)
sys.exit(0)
currval = neighval
currfit = neighfit
def __init__(self, cmd, logd, depth):
self.cmd = cmd
self.logd = logd
self.k_values = []
currd = depth
fitw, currf = self.sinbad_seq(currd, WGTS)
self.k_values.append((currd, fitw, currf))
while True:
msg = "\n==> current depth: %s, fitness: %s\n" % (currd, currf)
sys.stderr.write(msg)
neighd = self.neighbourd(self.k_values, currd)
fitw, neighf = self.sinbad_seq(neighd, WGTS)
self.k_values.append((neighd, fitw, neighf))
print "------"
if MetaUtils.localmax([f for _,_,f in self.k_values]):
fitd, maxf = self.best(self.k_values)
print "Max ambiguities found: %s" % maxf
print "Best depth, weights: "
for d,w in fitdw:
print "%s : %s " % (d,w)
sys.exit(0)
currd, currf = neighd, neighf
def __init__(self, cmd, logd, depth):
self.cmd = cmd
self.logd = logd
self.k_values = []
currd = depth
currf = self.sinbad(currd)
self.k_values.append((currd, currf))
while True:
msg = "\n==> current depth: %s, fitness: %s\n" % (currd, currf)
sys.stderr.write(msg)
neighd = self.neighbour(self.k_values, currd)
neighf = self.sinbad(neighd)
self.k_values.append((neighd, neighf))
print "------"
if MetaUtils.localmax([f for _,f in self.k_values], NBEST):
fitd, maxf = self.best(self.k_values)
print "Max ambiguities found: %s" % maxf
print "Best depths: " , fitd
sys.exit(0)
currd, currf = neighd, neighf
def run(self):
currlen = self.length
ambidextoptcmd = ["-k",str(currlen)]
MetaUtils.runtool(self.cmd + ambidextoptcmd)
currfit = MetaUtils.fitness("%s_%s/log" % (self.logd, currlen))
self.k_values.append((currlen,currfit))
while True:
sys.stderr.write("\n==> current length: %s, fitness: %s\n" \
% (str(currlen),str(currfit)))
neighlen = self.neighbour(currlen)
ambidextoptcmd = ["-k",str(neighlen)]
MetaUtils.runtool(self.cmd + ambidextoptcmd)
neighfit = MetaUtils.fitness("%s_%s/log" % (self.logd, neighlen))
sys.stderr.write("new fitness: %s " % str(neighfit))
self.k_values.append((neighlen,neighfit))
fitvals = [f for _,f in self.k_values]
if MetaUtils.localmax(fitvals, NBEST):
MetaUtils.report(self.k_values)
sys.exit(0)
currlen = neighlen
currfit = neighfit
