def minimize_f(fi, method = None, wantrestarts = None):
"""
Minimize the ``fi`` function instance. Returns the number of minimization
iterations performed.
"""
f = fi.f
n_restarts = -1
n_iters = 0
mm = MinimizeMethod(method, fi)
mmdata = SteppingData(fi)
# independent restarts until maxfunevals or ftarget is reached
while not ((f.evaluations > 1 and f.fbest < f.ftarget)
or f.evaluations > fi.maxfunevals):
n_restarts += 1
if n_restarts > 0:
f.restart('independent restart') # additional info
maxfevals = fi.maxfunevals / (wantrestarts + 1)
x0 = 10. * np.random.rand(fi.dim) - 5.
class MMCallback:
def __init__(self, fi, f, maxfevals, mm, data, n_iters):
self.restarts = 0
self.fi = fi
self.f = f
self.maxfevals = maxfevals
self.basefevals = self.f.evaluations
self.mm = mm
self.data = data
self.n_iters = n_iters
def __call__(self, x):
self.n_iters += 1
y = self.fi.evalfun(x)
self.data.record(0, self.mm.name, self.n_iters, y - self.fi.f.fopt, x)
if y < self.f.ftarget:
raise MMCancel()
elif self.f.evaluations - self.basefevals > self.maxfevals:
raise MMCancel()
elif self.f.evaluations > self.fi.maxfunevals:
raise MMCancel()
cb = MMCallback(fi, f, maxfevals, mm, mmdata, n_iters)
try:
warnings.simplefilter("ignore") # ignore warnings about unused/ignored options
mm(f.evalfun, x0, inner_cb = cb)
except MMCancel:
pass # Ok.
n_iters = cb.n_iters
return n_restarts
def __init__(self, fi, K, methods):
self.fi = fi
self.K = K
self.methods = methods
# A population of solution x points
self.points = 10. * np.random.rand(self.K, self.fi.dim) - 5.
# A population of solution y points
self.values = np.zeros(self.K) + 1e10
# A population of minimizers
self.minimizers = [self._minimizer_make(i) for i in range(self.K)]
# A population of iteration counters
self.iters = np.zeros(self.K, dtype=np.int)
self.total_steps = 0
self.total_iters = 0
self.data = SteppingData(self.fi)
