}
#############
# get params (x) and cost (z)
x, z = xyz.T[:,:-1], xyz.T[:,-1]
#HACK: remove any duplicate points by adding noise
_x = x + np.random.normal(scale=1e-8, size=x.shape)
if len(z) > N:
N = max(int(round(len(z)/float(N))),1)
print("for speed, sampling {} down to {}".format(len(z),len(z)/N))
x, _x, z = x[::N], _x[::N], z[::N]
f = Rbf(*np.vstack((_x.T, z)), **args)
f.__doc__ = model.__doc__
# convert to 'model' format (i.e. takes a parameter vector)
_model = lambda x: f(*x).tolist()
_model.__doc__ = f.__doc__
mz = np.argmin(z)
print("min: {}; min@f: {}".format(z[mz], f(*x[mz])))
mz = np.argmax(z)
print("max: {}; max@f: {}".format(z[mz], f(*x[mz])))
# print("TOOK: %s" % (time.time() - start))
# plot
#############
# specify 2-of-N dim (with bounds) and (N-2) with values
axes = (0,1) # axes to plot (specified by user)
}
#############
# get params (x) and cost (z)
x, z = xyz.T[:,:-1], xyz.T[:,-1]
#HACK: remove any duplicate points by adding noise
_x = x + np.random.normal(scale=1e-8, size=x.shape)
if len(z) > N:
N = max(int(round(len(z)/float(N))),1)
print "for speed, sampling {} down to {}".format(len(z),len(z)/N)
x, _x, z = x[::N], _x[::N], z[::N]
f = Rbf(*np.vstack((_x.T, z)), **args)
f.__doc__ = model.__doc__
# convert to 'model' format (i.e. takes a parameter vector)
_model = lambda x: f(*x).tolist()
_model.__doc__ = f.__doc__
mz = np.argmin(z)
print "min: {}; min@f: {}".format(z[mz], f(*x[mz]))
mz = np.argmax(z)
print "max: {}; max@f: {}".format(z[mz], f(*x[mz]))
# print "TOOK: %s" % (time.time() - start)
# plot
#############
# specify 2-of-N dim (with bounds) and (N-2) with values
axes = (0,1) # axes to plot (specified by user)
