def run_lambda_tst(y, M, Omega, epsilon, lbd):
"""
Wrapper for TST algorithm (with default optimized params)
for approximate analysis recovery within ABS-lambda approach
"""
nsweep = 300
return ABSlambda.tst_recom(y, M, Omega, epsilon, lbd, nsweep)
def run_lambda_tst(y, M, Omega, epsilon, lbd):
"""
Wrapper for TST algorithm (with default optimized params)
for approximate analysis recovery within ABS-lambda approach
"""
nsweep = 300
return ABSlambda.tst_recom(y, M, Omega, epsilon, lbd, nsweep)
def run_lambda_sl0(y,M,Omega,epsilon,lbd): """ Wrapper for SL0 algorithm within ABS-lambda approach for approximate analysis recovery """ sigma_min = 0.001 sigma_decrease_factor = 0.5 mu_0 = 2 L = 10 return ABSlambda.sl0(y,M,Omega,epsilon, lbd, sigma_min, sigma_decrease_factor, mu_0, L)
def run_lambda_sl0(y, M, Omega, epsilon, lbd):
"""
Wrapper for SL0 algorithm within ABS-lambda approach for approximate analysis recovery
"""
sigma_min = 0.001
sigma_decrease_factor = 0.5
mu_0 = 2
L = 10
return ABSlambda.sl0(y, M, Omega, epsilon, lbd, sigma_min, sigma_decrease_factor, mu_0, L)
def run_lambda_ompeps(y, M, Omega, epsilon, lbd):
"""
Wrapper for OMP algorithm, with stopping criterion = epsilon,
for approximate analysis recovery within ABS-lambda approach
"""
return ABSlambda.ompeps(y, M, Omega, epsilon, lbd)
def run_lambda_bp(y, M, Omega, epsilon, lbd):
"""
Wrapper for BP algorithm within ABS-lambda approach for approximate analysis recovery
"""
return ABSlambda.bp(y, M, Omega, epsilon, lbd, numpy.zeros(Omega.shape[0]))
def run_lambda_ompeps(y, M, Omega, epsilon, lbd):
"""
Wrapper for OMP algorithm, with stopping criterion = epsilon,
for approximate analysis recovery within ABS-lambda approach
"""
return ABSlambda.ompeps(y, M, Omega, epsilon, lbd)
def run_lambda_bp(y, M, Omega, epsilon, lbd):
"""
Wrapper for BP algorithm within ABS-lambda approach for approximate analysis recovery
"""
return ABSlambda.bp(y, M, Omega, epsilon, lbd, numpy.zeros(Omega.shape[0]))
