def GPGO_EI_SA(fun, x, maxfunevals, ftarget):
"""samples new points uniformly randomly in [-5,5]^dim and evaluates
them on fun until maxfunevals or ftarget is reached, or until
1e8 * dim function evaluations are conducted.
"""
dim = len(x)
maxfunevals = min(1e8 * dim, maxfunevals)
'''
popsize = min(maxfunevals, 200)
fbest = np.inf
for iter in range(0, int(np.ceil(maxfunevals / popsize))):
xpop = 10. * np.random.rand(popsize, dim) - 5.
fvalues = fun(xpop)
idx = np.argsort(fvalues)
if fbest > fvalues[idx[0]]:
fbest = fvalues[idx[0]]
xbest = xpop[idx[0]]
if fbest < ftarget: # task achieved
break
'''
xs,ys = gpgo_ei(lambda z: -fun(z), [x], lower=[-5]*dim, upper=[5]*dim, maxeval=maxfunevals, target=-ftarget, l=[1]*(dim), K=K_SE, dim=dim)
#xs,ys = gpgo_ei(lambda z: -fun(z), [x], lower=[-5]*dim, upper=[5]*dim, maxeval=maxfunevals, target=-ftarget, l=1, K=K_SE)
#xs,ys = gpgo_ei(lambda z: -fun(z), [x], lower=[-5]*dim, upper=[5]*dim, maxeval=maxfunevals, target=-ftarget, l=1, K=K_Matern)
idx = np.argmax(ys)
xbest = xs[idx]
#print xbest, fun(xbest), ftarget
return xbest
self.xs.append(x)
self.ys.append(y)
return y
for dims in [1,2,5,10,20,50,100]:
print dims,
maxeval=150
gpgo_avg = zeros(maxeval)
anneal_avg = zeros(maxeval)
N = 25
for _ in xrange(N):
g = GPmem(dims)
xs,ys = gpgo_ei(g, [[0]*dims,[10]*dims], lower=[0]*dims, upper=[10]*dims, maxeval=maxeval)
rmax = array(list(maximum.accumulate(ys))[:maxeval] + [max(ys)] * (maxeval-len(ys)))
plot(rmax, 'b', alpha=0.2)
gpgo_avg += rmax
f = Fmem(g, [0]*dims, [10]*dims)
anneal(lambda x: -f(x), [0]*dims, lower=[0]*dims, upper=[10]*dims, maxeval=maxeval)
rmax = array(list(maximum.accumulate(squeeze(f.ys)))[:maxeval] + [max(f.ys)] * (maxeval-len(f.ys)))
plot(rmax, 'r', alpha=0.2)
anneal_avg += rmax
print '.',
print
plot(gpgo_avg / N, 'b', linewidth=2)
return y
for dims in [1, 2, 5, 10, 20, 50, 100]:
print dims,
maxeval = 150
gpgo_avg = zeros(maxeval)
anneal_avg = zeros(maxeval)
N = 25
for _ in xrange(N):
g = GPmem(dims)
xs, ys = gpgo_ei(g, [[0] * dims, [10] * dims],
lower=[0] * dims,
upper=[10] * dims,
maxeval=maxeval)
rmax = array(
list(maximum.accumulate(ys))[:maxeval] + [max(ys)] *
(maxeval - len(ys)))
plot(rmax, 'b', alpha=0.2)
gpgo_avg += rmax
f = Fmem(g, [0] * dims, [10] * dims)
anneal(lambda x: -f(x), [0] * dims,
lower=[0] * dims,
upper=[10] * dims,
maxeval=maxeval)
rmax = array(
list(maximum.accumulate(squeeze(f.ys)))[:maxeval] + [max(f.ys)] *
(maxeval - len(f.ys)))
