def optimize(self, nIter):
if self._t is None:
self._t0 = time.time()
self._t = self._t0
pAsk = Perf()
pTell = Perf()
pPhi = MultiPerf()
for _ in range(nIter):
pAsk.start()
ws = np.array(self._es.ask())
pAsk.stop()
phis = self._phi(pPhi, ws)
pTell.start()
self._es.tell(phis)
pTell.stop()
self._iReport += 1
if self._iReport == self._nReport:
t = time.time()
print(
"EVAL: iter = %d t = %.2f dt = %.4f phi = %.4e wmn = %.4f wmx = %.4f sigma = %.4e nEvalsPerW = %d"
% (self._nIter, t - self._t0, t - self._t, phis.mean(),
ws.mean(), np.abs(ws).max(), self._es.getSigma(),
self._nEvalsThisTime))
self._phiLatest = phis.mean()
perf = self._collectPerf()
print("PERF: pA = %.3f pT = %.3f pP = %s %s" %
(1000 * pAsk.mean(), 1000 * pTell.mean(), ' '.join(
["%.3f" % (1000 * p) for p in pPhi.means()]), perf))
self._t = t
self._nIter += 1
self._trace.append(phis.mean())
self._iReport = 0
self._wfav = np.array(self._es.getXFavorite())
self._bbo.setParams(self._wfav)
