def generate_minimax_square(N, seed):
np.random.seed(seed)
domain = psdr.BoxDomain(0 * np.zeros(2), np.ones(2))
# Generate a design
try:
X = psdr.minimax_lloyd(domain,
N,
maxiter=500,
xtol=1e-9,
verbose=False)
# Compute the disc diameter to cover the domain
V = psdr.voronoi_vertex(domain, X)
D = psdr.cdist(X, V)
radius = np.max(np.min(D, axis=0))
# save the file
design = {
'author': 'Jeffrey M. Hokanson',
'notes':
f'psdr.minimax_lloyd seed={seed}, maxiter=500, xtol = 1e-9',
'objective': 'minimax',
'metric': 'l2',
'domain': 'square',
'radius': radius,
'X': X.tolist()
}
except:
design = {'radius': np.inf}
print(f"M: {N:4d} \t seed {seed:4d} finished")
return design
def generate_minimax_square(N = 10, seed = 0):
domain = psdr.BoxDomain(0*np.zeros(2), np.ones(2))
# Generate a design
X = psdr.minimax_lloyd(domain, N, maxiter = 500, xtol = 1e-7, verbose = True)
#X = domain.sample(N)
# Compute the disc diameter to cover the domain
V = psdr.voronoi_vertex(domain, X)
D = psdr.cdist(X, V)
radius = np.max(np.min(D, axis= 0))
# save the file
design = {
'author': 'Jeffrey M. Hokanson',
'objective': 'minimax',
'metric': 'l2',
'domain': 'square',
'radius': radius,
'X': X.tolist()
}
with open('square_%04d.dat' % N, 'w') as f:
json.dump(design, f)
def test_minimax_l2_design(fname):
r""" This checks the design for consistency.
It does not check if there is an improvment
"""
print(f"Loading design '{fname}'")
design = get_new_design(fname)
if design['domain'] == 'square':
domain = psdr.BoxDomain(np.zeros(2), np.ones(2))
else:
raise AssertionError('domain type "%s" not recognized' %
design['domain'])
assert design[
'metric'] == 'l2', "Expected metric 'l2', got '%s'" % design['metric']
assert design[
'objective'] == 'minimax', "Expected objective 'minimax', got '%s'" % design[
'objective']
X = np.array(design['X'])
M = int(re.search(r'_(.*?).json', fname).group(1))
assert X.shape[
0] == M, f"Number of points does not match the file name: name suggests {M}, files has {X.shape[0]}"
assert X.shape[1] == len(
domain), "Points are in a different dimensional space than the domain"
assert np.all(domain.isinside(X)), "All points must be inside the domain"
# Check the objective value
V = psdr.voronoi_vertex(domain, X)
D = psdr.cdist(X, V)
radius = np.max(np.min(D, axis=0))
print("Measured radius", '%20.15e' % radius)
print("Reported radius", '%20.15e' % design['radius'])
assert np.isclose(radius, design['radius'], rtol=1e-10, atol=1e-10)
def score_design(Xhat):
V = psdr.voronoi_vertex(domain, Xhat, L)
D = psdr.cdist(Xhat, V, L)
return np.max(np.min(D, axis=0))
def check_vertex(dom, Xhat, L = None): V = voronoi_vertex(dom, Xhat, L = L ) check_voronoi(dom, Xhat, V, L = L)
def test_voronoi_vertex(m = 2, M = 10): domain = psdr.BoxDomain(-1*np.ones(m), np.ones(m)) Xhat = domain.sample_grid(2) V = psdr.voronoi_vertex(domain, Xhat)