def Main(
outfile="thatTest",
d=5,
grid_points=tuple([2000]),
L_max=4,
algo_name="whtp",
gamma=1.035,
L_min=1,
sampling_name="p",
nb_iter=500,
epsilon=1e-3,
nb_tests=None,
alpha=2.0,
abar=4.3,
dat_constant=10,
):
# if algo_name == 'whtp': # Really have to find a way to deal with the epsilon/eta/nbIter parameter
# epsilon = 50 # This will be rescaled later
# elif algo_name == 'wiht':
# epsilon = 1e-4
# elif algo_name == 'womp':
# epsilon = 1e-4
# elif algo_name == 'bpdn':
# epsilon = 1e-4
# else:
# epsilon = 50 # This will be rescaled later
# Create FEMModel with given diffusion coefficient, goal functional and initial mesh size
spde_model = DiffusionFEMModelML(TrigCoefficient(d, alpha, abar), ConstantCoefficient(10.0), Average(), grid_points)
# Still have to concatenate the output file name with the parameters (i.e. d and h_0)
test_result = outfile, None
# test_result = '_'.join([algo_name, str(d), str(grid_points),outfile]), None
for s in range(L_min, L_max + 1, 1): # s corresponds to the number of levels here
### Reconstruction Model
v = np.hstack((np.repeat(gamma, 2 * d), [np.inf]))
wr_model = WR.WRModel(algo_name, WR.Operators.Chebyshev, v, get_sampling_type(sampling_name), WR.check_cs)
# wr_model = WR.WRModel(WR.Algorithms.whtp, WR.Operators.Chebyshev, v,
# WR.cs_pragmatic_m, WR.check_cs) # or cs_theoretic_m
print "wr_model created"
## Number of tests
num_tests = nb_tests # change from 10 for Quinoa tests
## Don't forget to reset the original mesh
spde_model.refine_mesh(2 ** (-s))
### Execute test
test_result = test(
spde_model, wr_model, nb_iter, epsilon, s, [CrossCheck(num_tests)], dat_constant, *test_result
)
def Main():
parser = argparse.ArgumentParser(description = "")
parser.add_argument("-d", "--nb-parts", help="Number of splits (i.e. nb of parameters)", default=10, required=False)
parser.add_argument("-o", "--output-file", help="File to write the results", default="outputDiffusionML", required=False)
parser.add_argument("-L", "--nb-level", help="Number of levels used", default=4, required=False)
parser.add_argument("-m", "--mesh-size", help="Size of the coarsest level (number of grid points)", default=2000, required=False)
parser.add_argument("-N", "--nb-iter", help="Number of iterations for the (potential) iterative greedy algorithm", default=500, required=False)
parser.add_argument("-r", "--recovery-algo", help="String for the algorithm for weighted l1 recovery", default="whtp", required=False)
args = parser.parse_args()
# Corseast grid
outfile = args.output_file
grid_points = int(args.mesh_size)
d = int(args.nb_parts)
L_max = int(args.nb_level)
algo_name = args.recovery_algo.lower()
if algo_name == 'whtp': # Really have to find a way to deal with the epsilon/eta/nbIter parameter
epsilon = 50 # This will be rescaled later
elif algo_name == 'wiht':
epsilon = 1e-4
elif algo_name == 'womp':
epsilon = 1e-4
elif algo_name == 'bpdn':
epsilon = 1e-4
else:
epsilon = 50 # This will be rescaled later ##d = None, abar = None, coefs = None, parts = None
# Create FEMModel with given diffusion coefficient, goal functional and initial mesh size
spde_model = DiffusionFEMModelML(PartitionUnityConstantCoefficient(d, 5), ConstantCoefficient(10.0),
Average(), grid_points)
# Still have to concatenate the output file name with the parameters (i.e. d and h_0)
test_result = '_'.join([algo_name, str(d), str(grid_points),outfile]), None
for s in range(1,L_max+1,1): # s corresponds to the number of levels here
for gamma in np.arange(1.055, 1.06, 0.01)[::-1]:
### Reconstruction Model
v = np.hstack((np.repeat(gamma, 2*d), [np.inf]))
wr_model = WR.WRModel(algo_name, WR.Operators.Chebyshev, v,
WR.cs_theoretic_m_new, WR.check_cs)
#wr_model = WR.WRModel(WR.Algorithms.whtp, WR.Operators.Chebyshev, v,
# WR.cs_pragmatic_m, WR.check_cs) # or cs_theoretic_m
## Number of tests
num_tests = 1000 # change from 10 for Quinoa tests
## Don't forget to reset the original mesh
spde_model.refine_mesh(2**(-s))
### Execute test
test_result = test(spde_model, wr_model, epsilon, s, [CrossCheck(num_tests)], *test_result)
