def test_lin_poly_8(self):
"""
Linear polynomial in 8 variables.
"""
m = functools.partial(self.modeler, ord_cap=3, deg_cap=3)
print model_validation.modeler_function_l2_errors(
m, 80,
model_validation.uniform_sampler(8, [-1.0, 1.0]),
test_functions.lin_poly_8
)
def test_corr_poly_1(self):
"""
Source: "General formulation of HDMR component functions with
independent and correlated variables", Rabitz/Li, J Math Chem 2011.
Two identical copies of the polynomial taken and added together (on
distinct sets of variables) to increase dimensionality to make this
a more interesting example.
"""
mu = np.array([1.0, 2.0, 3.0, 1.0, 2.0, 3.0])
cov = np.array([
[1.0, 1.0, 0.0, 0.0, 0.0, 0.0],
[0.5, 1.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 1.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 1.0, 1.0, 0.0],
[0.0, 0.0, 0.0, 0.5, 1.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 1.0]
])
sample_fn = model_validation.multi_gaussian_sampler(mu, cov)
test_fn = test_functions.gen_corr_poly_1(mu)
m = functools.partial(self.modeler, ord_cap=3, deg_cap=4)
print "Cross-validation relative L2 errors", model_validation.modeler_function_l2_errors(
m, 200, sample_fn, test_fn
)
def main():
with open(out_file, 'w') as f:
writer = csv.writer(f)
modeler = functools.partial(cs_hdmr.cs_hdmr_modeler,
ord_cap=2,
deg_cap=5)
errors = [
model_validation.modeler_function_l2_errors(
modeler,
n * sample_divisions,
sample_fn,
test_poly,
sample_divisions=sample_divisions) for n in n_list
]
for n, error_list in zip(n_list, errors):
writer.writerow([n] + error_list)
def main():
with open(out_file, 'w') as f:
writer = csv.writer(f)
modeler = functools.partial(
cs_hdmr.cs_hdmr_modeler,
ord_cap=2,
deg_cap=5
)
errors = [
model_validation.modeler_function_l2_errors(
modeler, n*sample_divisions, sample_fn,
test_poly, sample_divisions=sample_divisions
)
for n in n_list
]
for n, error_list in zip(n_list, errors):
writer.writerow([n] + error_list)
def main():
with open(out_file, 'w') as f:
writer = csv.writer(f)
modeler = functools.partial(cs_hdmr.cs_hdmr_modeler,
ord_cap=3,
deg_cap=6)
errors = [
model_validation.modeler_function_l2_errors(
modeler,
N * sample_divisions,
model_validation.uniform_sampler(6, [-1.0, 1.0]),
test_poly,
sample_divisions=sample_divisions,
noise=model_validation.make_gaussian_noise_fn(0, noise))
for noise in noise_list
]
for noise, error_list in zip(noise_list, errors):
writer.writerow([noise] + error_list)
def main():
with open(out_file, 'w') as f:
writer = csv.writer(f)
modeler = functools.partial(
cs_hdmr.cs_hdmr_modeler,
ord_cap=3,
deg_cap=6,
independent=True
)
errors = [
model_validation.modeler_function_l2_errors(
modeler, n*sample_divisions,
model_validation.uniform_sampler(6, [-1.0, 1.0]),
test_poly, sample_divisions=sample_divisions
)
for n in n_list
]
for n, error_list in zip(n_list, errors):
writer.writerow([n] + error_list)
def main():
with open(out_file, 'w') as f:
writer = csv.writer(f)
modeler = functools.partial(
cs_hdmr.cs_hdmr_modeler,
ord_cap=3,
deg_cap=6
)
errors = [
model_validation.modeler_function_l2_errors(
modeler, N*sample_divisions,
model_validation.uniform_sampler(6, [-1.0, 1.0]),
test_poly, sample_divisions=sample_divisions,
noise=model_validation.make_gaussian_noise_fn(0, noise)
)
for noise in noise_list
]
for noise, error_list in zip(noise_list, errors):
writer.writerow([noise] + error_list)
