def clippingHGMevaluation(input_generator, branches, Plot, reference=None):
for t in range(8, 11):
t = t / 10.0
thresholds = [-t, t]
input_signal = input_generator.GetOutput()
nl_functions = [
nlsp.function_factory.hardclip(thresholds),
] * branches
filter_spec_tofind = nlsp.log_bpfilter(branches=branches,
input=input_signal)
ref_nlsystem = nlsp.HammersteinGroupModel_up(
input_signal=input_signal,
nonlinear_functions=nl_functions,
filter_irs=filter_spec_tofind,
max_harmonics=range(1, branches + 1))
sweep = nlsp.NovakSweepGenerator_Sine(
sampling_rate=input_signal.GetSamplingRate(),
length=len(input_signal))
ref_nlsystem.SetInput(sweep.GetOutput())
init_coeffs, non = nlsp.nonlinearconvolution_powerseries_temporalreversal(
sweep, ref_nlsystem.GetOutput(), branches=branches)
init_coeffs = nlsp.change_length_filterkernels(init_coeffs,
filter_length)
ref_nlsystem.SetInput(input_signal)
found_filter_spec, nl_functions = nlsp.adaptive_identification_legendre(
input_generator=input_generator,
outputs=ref_nlsystem.GetOutput(),
branches=branches,
init_coeffs=init_coeffs)
iden_nlsystem = nlsp.HammersteinGroupModel_up(
input_signal=input_signal,
nonlinear_functions=nl_functions,
filter_irs=found_filter_spec,
max_harmonics=range(1, branches + 1))
# sine = sumpf.modules.SineWaveGenerator(frequency=5000.0,phase=0.0,samplingrate=input_signal.GetSamplingRate(),length=len(input_signal)).GetSignal()
sine = sumpf.modules.SweepGenerator(
samplingrate=input_signal.GetSamplingRate(),
length=len(input_signal)).GetSignal()
ref_nlsystem.SetInput(sine)
iden_nlsystem.SetInput(sine)
if reference is not None:
reference = nlsp.change_length_signal(reference,
length=len(input_signal))
ref_nlsystem.SetInput(reference)
iden_nlsystem.SetInput(reference)
if Plot is True:
plot.relabelandplot(sumpf.modules.FourierTransform(
ref_nlsystem.GetOutput()).GetSpectrum(),
"Reference System",
show=False)
plot.relabelandplot(sumpf.modules.FourierTransform(
iden_nlsystem.GetOutput()).GetSpectrum(),
"Identified System",
show=False)
print "SNR between Reference and Identified output for symmetric hardclipping HGM(thresholds:%r): %r" % (
thresholds,
nlsp.snr(ref_nlsystem.GetOutput(), iden_nlsystem.GetOutput()))
def differentlength_evaluation(input_generator,
branches,
Plot,
reference=None):
length_ref = [2**15, 2**16, 2**17]
length_iden = [2**15, 2**16, 2**17]
input_generator_ref = input_generator
input_generator_iden = input_generator
for signal_length, ref_length in zip(length_iden, length_ref):
input_generator_ref.SetLength(ref_length)
input_ref = input_generator_ref.GetOutput()
filter_spec_tofind = nlsp.log_weightingfilter(branches=branches,
input=input_ref)
ref_nlsystem = nlsp.HammersteinGroupModel_up(
input_signal=input_ref,
nonlinear_functions=nlsp.nl_branches(
nlsp.function_factory.power_series, branches),
filter_irs=filter_spec_tofind,
max_harmonics=range(1, branches + 1))
sweep = nlsp.NovakSweepGenerator_Sine(
sampling_rate=input_ref.GetSamplingRate(), length=len(input_ref))
ref_nlsystem.SetInput(sweep.GetOutput())
init_coeffs, non = nlsp.nonlinearconvolution_powerseries_temporalreversal(
sweep, ref_nlsystem.GetOutput(), branches=branches)
init_coeffs = nlsp.change_length_filterkernels(init_coeffs,
filter_length)
ref_nlsystem.SetInput(input_ref)
found_filter_spec, nl_functions = nlsp.adaptive_identification_legendre(
input_generator=input_generator_ref,
outputs=ref_nlsystem.GetOutput(),
branches=branches,
init_coeffs=init_coeffs)
input_generator_iden.SetLength(signal_length)
input_iden = input_generator_iden.GetOutput()
iden_nlsystem = nlsp.HammersteinGroupModel_up(
input_signal=input_iden,
nonlinear_functions=nl_functions,
filter_irs=found_filter_spec,
max_harmonics=range(1, branches + 1))
if reference is not None:
reference = nlsp.change_length_signal(reference,
length=len(input_ref))
ref_nlsystem.SetInput(reference)
iden_nlsystem.SetInput(reference)
if Plot is True:
plot.relabelandplotphase(
sumpf.modules.FourierTransform(
ref_nlsystem.GetOutput()).GetSpectrum(),
"Reference Output", False)
plot.relabelandplotphase(
sumpf.modules.FourierTransform(
iden_nlsystem.GetOutput()).GetSpectrum(),
"Identified Output", True)
print "SNR between Reference(length:%r) and Identified output(length:%r) : %r" % (
len(input_ref), len(input_iden),
nlsp.snr(ref_nlsystem.GetOutput(), iden_nlsystem.GetOutput()))
def robustness_excitation_evaluation(input_generator,
branches,
Plot,
reference=None):
excitation_signal_amp = [0.5, 1.0]
sample_signal_amp = [0.5, 1.0, 2.0]
input = input_generator.GetOutput()
for excitation_amp, sample_amp in itertools.product(
excitation_signal_amp, sample_signal_amp):
input_signal = sumpf.modules.AmplifySignal(
input=input, factor=excitation_amp).GetOutput()
sample_signal = nlsp.WhiteGaussianGenerator(
sampling_rate=input_signal.GetSamplingRate(),
length=len(input_signal),
distribution=sumpf.modules.NoiseGenerator.UniformDistribution(
minimum=-sample_amp, maximum=sample_amp))
sample_signal = sample_signal.GetOutput()
filter_spec_tofind = nlsp.log_weightingfilter(branches=branches,
input=input_signal)
ref_nlsystem = nlsp.HammersteinGroupModel_up(
input_signal=input_signal,
nonlinear_functions=nlsp.nl_branches(
nlsp.function_factory.power_series, branches),
filter_irs=filter_spec_tofind,
max_harmonics=range(1, branches + 1))
sweep = nlsp.NovakSweepGenerator_Sine(
sampling_rate=input_signal.GetSamplingRate(),
length=len(input_signal))
ref_nlsystem.SetInput(sweep.GetOutput())
init_coeffs, non = nlsp.nonlinearconvolution_powerseries_temporalreversal(
sweep, ref_nlsystem.GetOutput(), branches=branches)
init_coeffs = nlsp.change_length_filterkernels(init_coeffs,
filter_length)
ref_nlsystem.SetInput(input_signal)
found_filter_spec, nl_functions = nlsp.adaptive_identification_legendre(
input_generator=input_generator,
outputs=ref_nlsystem.GetOutput(),
branches=branches,
init_coeffs=init_coeffs)
iden_nlsystem = nlsp.HammersteinGroupModel_up(
input_signal=sample_signal,
nonlinear_functions=nl_functions,
filter_irs=found_filter_spec,
max_harmonics=range(1, branches + 1))
ref_nlsystem.SetInput(sample_signal)
if Plot is True:
nlsp.relabelandplotphase(
sumpf.modules.FourierTransform(
ref_nlsystem.GetOutput()).GetSpectrum(),
"Reference Output Scaled", False)
nlsp.relabelandplot(
sumpf.modules.FourierTransform(
iden_nlsystem.GetOutput()).GetSpectrum(),
"Identified Output", True)
print "SNR between Scaled Identified with(amp:%r) and Tested with(amp:%r) output: %r" % (
excitation_amp, sample_amp,
nlsp.snr(ref_nlsystem.GetOutput(), iden_nlsystem.GetOutput()))
def hgmallpass_evaluation(input_generator,
branches,
nlfunction,
Plot,
reference=None):
input_signal = input_generator.GetOutput()
allpass = sumpf.modules.ImpulseGenerator(
samplingrate=input_signal.GetSamplingRate(),
length=len(input_signal)).GetSignal()
filter_spec_tofind = [
allpass,
] * branches
ref_nlsystem = nlsp.HammersteinGroupModel_up(
input_signal=input_signal,
nonlinear_functions=nlsp.nl_branches(nlfunction, branches),
filter_irs=filter_spec_tofind,
max_harmonics=range(1, branches + 1))
sweep = nlsp.NovakSweepGenerator_Sine(
sampling_rate=input_signal.GetSamplingRate(), length=len(input_signal))
ref_nlsystem.SetInput(sweep.GetOutput())
init_coeffs, non = nlsp.nonlinearconvolution_powerseries_temporalreversal(
sweep, ref_nlsystem.GetOutput(), branches=branches)
init_coeffs = nlsp.change_length_filterkernels(init_coeffs, filter_length)
ref_nlsystem.SetInput(input_signal)
found_filter_spec, nl_functions = nlsp.adaptive_identification_legendre(
input_generator=input_generator,
outputs=ref_nlsystem.GetOutput(),
branches=branches,
init_coeffs=init_coeffs)
iden_nlsystem = nlsp.HammersteinGroupModel_up(
input_signal=input_signal,
nonlinear_functions=nl_functions,
filter_irs=found_filter_spec,
max_harmonics=range(1, branches + 1))
if reference is not None:
reference = nlsp.change_length_signal(reference,
length=len(input_signal))
ref_nlsystem.SetInput(reference)
iden_nlsystem.SetInput(reference)
if Plot is True:
plot.relabelandplot(sumpf.modules.FourierTransform(
ref_nlsystem.GetOutput()).GetSpectrum(),
"Reference System",
show=False)
plot.relabelandplot(sumpf.modules.FourierTransform(
iden_nlsystem.GetOutput()).GetSpectrum(),
"Identified System",
show=True)
print "SNR between Reference and Identified output with all pass filters: %r" % nlsp.snr(
ref_nlsystem.GetOutput(), iden_nlsystem.GetOutput())
def adaptive_differentnlfunctions():
# generate virtual nonlinear system using HGM
ref_nlsystem = nlsp.HammersteinGroupModel_up(nonlinear_functions=nlsp.nl_branches(nlsp.function_factory.power_series,branches),
filter_irs=filter_spec_tofind_noise,
max_harmonics=range(1,branches+1))
# give input and get output from the virtual nlsystem
ref_nlsystem.SetInput(input)
output_noise = ref_nlsystem.GetOutput()
input_noise = input
# only noise based system identification
found_filter_spec_adapt_power, nl_function_adapt_power = nlsp.adaptive_identification_powerseries(input_generator=input_noise,outputs=output_noise,
branches=branches,iterations=i,filtertaps=filter_taps,Print=True)
found_filter_spec_adapt_cheby, nl_function_adapt_cheby = nlsp.adaptive_identification_chebyshev(input_generator=input_noise,outputs=output_noise,
branches=branches,iterations=i,filtertaps=filter_taps,Print=True)
found_filter_spec_adapt_hermite, nl_function_adapt_hermite = nlsp.adaptive_identification_hermite(input_generator=input_noise,outputs=output_noise,
branches=branches,iterations=i,filtertaps=filter_taps,Print=True)
found_filter_spec_adapt_legendre, nl_function_adapt_legendre = nlsp.adaptive_identification_legendre(input_generator=input_noise,outputs=output_noise,
branches=branches,iterations=i,filtertaps=filter_taps,Print=True)
iden_nlsystem_adapt_power = nlsp.HammersteinGroupModel_up(max_harmonics=range(1,branches+1),nonlinear_functions=nl_function_adapt_power,
filter_irs=found_filter_spec_adapt_power)
iden_nlsystem_adapt_cheby = nlsp.HammersteinGroupModel_up(max_harmonics=range(1,branches+1),nonlinear_functions=nl_function_adapt_cheby,
filter_irs=found_filter_spec_adapt_cheby)
iden_nlsystem_adapt_hermite = nlsp.HammersteinGroupModel_up(max_harmonics=range(1,branches+1),nonlinear_functions=nl_function_adapt_hermite,
filter_irs=found_filter_spec_adapt_hermite)
iden_nlsystem_adapt_legendre = nlsp.HammersteinGroupModel_up(max_harmonics=range(1,branches+1),nonlinear_functions=nl_function_adapt_legendre,
filter_irs=found_filter_spec_adapt_legendre)
# set reference input to virtual nlsystem and identified nl system
ref_nlsystem.SetInput(reference)
iden_nlsystem_adapt_power.SetInput(reference)
iden_nlsystem_adapt_cheby.SetInput(reference)
iden_nlsystem_adapt_hermite.SetInput(reference)
iden_nlsystem_adapt_legendre.SetInput(reference)
# calculate snr value
adaptive_powerseries_snr = nlsp.snr(ref_nlsystem.GetOutput(),iden_nlsystem_adapt_power.GetOutput())
adaptive_chebyshev_snr = nlsp.snr(ref_nlsystem.GetOutput(),iden_nlsystem_adapt_cheby.GetOutput())
adaptive_hermite_snr = nlsp.snr(ref_nlsystem.GetOutput(),iden_nlsystem_adapt_hermite.GetOutput())
adaptive_legendre_snr = nlsp.snr(ref_nlsystem.GetOutput(),iden_nlsystem_adapt_legendre.GetOutput())
# print snr value
print "adaptive_powerseries_snr: %r" %adaptive_powerseries_snr
print "adaptive_chebyshev_snr: %r" %adaptive_chebyshev_snr
print "adaptive_hermite_snr: %r" %adaptive_hermite_snr
print "adaptive_legendre_snr: %r" %adaptive_legendre_snr
def nlechocancellation(input):
iden_nl_system, ref_nl_system = nlsystem(
branches=branches,
excitation=nl_system_iden_excitation,
system_identification_alg=system_identification_alg)
iden_nl_system.SetInput(input)
echoic_op = echoic_nonlinear_chamber(input)
desired_op = echoic_op - iden_nl_system.GetOutput()
kernel, nlfunction = nlsp.adaptive_identification_legendre(
iden_nl_system.GetOutput(), desired_op, branches=1, filtertaps=2**10)
iden_lin_system = nlsp.HammersteinGroupModel_up(
nonlinear_functions=nlfunction, filter_irs=kernel)
iden_lin_system.SetInput(iden_nl_system.GetOutput())
determined_output = iden_lin_system.GetOutput() + iden_nl_system.GetOutput(
)
speech = determined_output - desired_op
return speech
def doublehgm_different_evaluation(input_generator,
branches,
Plot,
reference=None):
input_signal = input_generator.GetOutput()
filter_spec_tofind1 = nlsp.log_bpfilter(branches=branches,
input=input_signal)
filter_spec_tofind2 = nlsp.log_chebyfilter(branches=branches,
input=input_signal)
sweep = nlsp.NovakSweepGenerator_Sine(
sampling_rate=input_signal.GetSamplingRate(), length=len(input_signal))
ref_nlsystem = nlsp.HammersteinGroup_Series(
input_signal=sweep.GetOutput(),
nonlinear_functions=(nlsp.nl_branches(
nlsp.function_factory.power_series, branches),
nlsp.nl_branches(
nlsp.function_factory.chebyshev1_polynomial,
branches)),
filter_irs=(filter_spec_tofind1, filter_spec_tofind2),
max_harmonics=(range(1, branches + 1), range(1, branches + 1)),
hgm_type=(nlsp.HammersteinGroupModel_up,
nlsp.HammersteinGroupModel_up))
init_coeffs, non = nlsp.nonlinearconvolution_powerseries_temporalreversal(
sweep, ref_nlsystem.GetOutput(2), branches=branches)
init_coeffs = nlsp.change_length_filterkernels(init_coeffs, filter_length)
ref_nlsystem = nlsp.HammersteinGroup_Series(
input_signal=input_signal,
nonlinear_functions=(nlsp.nl_branches(
nlsp.function_factory.power_series, branches),
nlsp.nl_branches(
nlsp.function_factory.chebyshev1_polynomial,
branches)),
filter_irs=(filter_spec_tofind1, filter_spec_tofind2),
max_harmonics=(range(1, branches + 1), range(1, branches + 1)),
hgm_type=(nlsp.HammersteinGroupModel_up,
nlsp.HammersteinGroupModel_up))
found_filter_spec, nl_functions = nlsp.adaptive_identification_legendre(
input_generator=input_generator,
outputs=ref_nlsystem.GetOutput(2),
branches=branches,
init_coeffs=init_coeffs)
iden_nlsystem = nlsp.HammersteinGroupModel_up(
input_signal=input_signal,
nonlinear_functions=nl_functions,
filter_irs=found_filter_spec,
max_harmonics=range(1, branches + 1))
if reference is not None:
reference = nlsp.change_length_signal(reference,
length=len(input_signal))
ref_nlsystem = nlsp.HammersteinGroup_Series(
input_signal=reference,
nonlinear_functions=(
nlsp.nl_branches(nlsp.function_factory.power_series, branches),
nlsp.nl_branches(nlsp.function_factory.chebyshev1_polynomial,
branches)),
filter_irs=(filter_spec_tofind1, filter_spec_tofind2),
max_harmonics=(range(1, branches + 1), range(1, branches + 1)),
hgm_type=(nlsp.HammersteinGroupModel_up,
nlsp.HammersteinGroupModel_up))
iden_nlsystem.SetInput(reference)
if Plot is True:
plot.relabelandplotphase(sumpf.modules.FourierTransform(
ref_nlsystem.GetOutput(2)).GetSpectrum(),
"Reference System",
show=False)
plot.relabelandplotphase(sumpf.modules.FourierTransform(
iden_nlsystem.GetOutput()).GetSpectrum(),
"Identified System",
show=True)
print "SNR between Reference and Identified output for double hgm different: %r" % nlsp.snr(
ref_nlsystem.GetOutput(2), iden_nlsystem.GetOutput())
def hgmwithfilter_evaluation_sweepadaptive(input_generator,
branches,
nlfuntion,
Plot,
reference=None):
input_signal = input_generator.GetOutput()
# filter_spec_tofind = nlsp.create_bpfilter([2000,8000,30000],input_signal)
filter_spec_tofind = nlsp.log_bpfilter(branches=branches,
input=input_signal)
# filter_spec_tofind = nlsp.log_chebyfilter(branches=branches,input=input_signal)
ref_nlsystem = nlsp.HammersteinGroupModel_up(
input_signal=input_signal,
nonlinear_functions=nlsp.nl_branches(nlfuntion, branches),
filter_irs=filter_spec_tofind,
max_harmonics=range(1, branches + 1))
sweep = nlsp.NovakSweepGenerator_Sine(
sampling_rate=input_signal.GetSamplingRate(), length=len(input_signal))
ref_nlsystem.SetInput(sweep.GetOutput())
sweep_start = time.clock()
init_coeffs, non = nlsp.nonlinearconvolution_powerseries_temporalreversal(
sweep, ref_nlsystem.GetOutput(), branches=branches)
sweep_stop = time.clock()
init_coeffs = nlsp.change_length_filterkernels(init_coeffs, filter_length)
ref_nlsystem.SetInput(input_signal)
adapt_sweep_start = time.clock()
found_filter_spec, nl_functions = nlsp.adaptive_identification_legendre(
input_generator=input_generator,
outputs=ref_nlsystem.GetOutput(),
branches=branches,
init_coeffs=init_coeffs,
filtertaps=filter_length)
adapt_sweep_stop = time.clock()
adapt_start = time.clock()
found_filter_spec, nl_functions = nlsp.adaptive_identification_legendre(
input_generator=input_generator,
outputs=ref_nlsystem.GetOutput(),
branches=branches,
filtertaps=filter_length)
adapt_stop = time.clock()
print "sweep_identification time %r" % (sweep_start - sweep_stop)
print "sweep_adapt_identification time %r" % (adapt_sweep_start -
adapt_sweep_stop)
print "adapt_identification time %r" % (adapt_start - adapt_stop)
iden_nlsystem = nlsp.HammersteinGroupModel_up(
input_signal=input_signal,
nonlinear_functions=nl_functions,
filter_irs=found_filter_spec,
max_harmonics=range(1, branches + 1))
# nlsp.filterkernel_evaluation_plot(filter_spec_tofind,found_filter_spec)
# nlsp.filterkernel_evaluation_sum(filter_spec_tofind,found_filter_spec)
if reference is not None:
reference = nlsp.change_length_signal(reference,
length=len(input_signal))
ref_nlsystem.SetInput(reference)
iden_nlsystem.SetInput(reference)
if Plot is True:
plot.relabelandplot(sumpf.modules.FourierTransform(
ref_nlsystem.GetOutput()).GetSpectrum(),
"Reference Output",
show=False)
plot.relabelandplot(sumpf.modules.FourierTransform(
iden_nlsystem.GetOutput()).GetSpectrum(),
"Identified Output",
show=True)
print "SNR between Reference and Identified output without overlapping filters: %r" % nlsp.snr(
ref_nlsystem.GetOutput(), iden_nlsystem.GetOutput())
def linearmodel_evaluation(input_generator,
branches,
nlfunction,
Plot,
reference=None):
input_signal = input_generator.GetOutput()
prp = sumpf.modules.ChannelDataProperties()
prp.SetSignal(input_signal)
filter_ir = sumpf.modules.FilterGenerator(
filterfunction=sumpf.modules.FilterGenerator.BUTTERWORTH(order=10),
frequency=10000.0,
transform=False,
resolution=prp.GetResolution(),
length=prp.GetSpectrumLength()).GetSpectrum()
ref_nlsystem = nlsp.AliasCompensatingHammersteinModelUpandDown(
filter_impulseresponse=sumpf.modules.InverseFourierTransform(
filter_ir).GetSignal())
sweep = nlsp.NovakSweepGenerator_Sine(
sampling_rate=input_signal.GetSamplingRate(), length=len(input_signal))
ref_nlsystem.SetInput(sweep.GetOutput())
init_coeffs, non = nlsp.nonlinearconvolution_powerseries_temporalreversal(
sweep, ref_nlsystem.GetOutput(), branches=branches)
init_coeffs = nlsp.change_length_filterkernels(init_coeffs, filter_length)
ref_nlsystem.SetInput(input_signal)
found_filter_spec, nl_functions = nlsp.adaptive_identification_legendre(
input_generator=input_generator,
outputs=ref_nlsystem.GetOutput(),
branches=branches,
init_coeffs=init_coeffs)
iden_nlsystem = nlsp.HammersteinGroupModel_up(
input_signal=input_signal,
nonlinear_functions=nl_functions,
filter_irs=found_filter_spec,
max_harmonics=range(1, branches + 1))
# linear system identification
sweep = nlsp.NovakSweepGenerator_Sine(
length=len(input_signal), sampling_rate=input_signal.GetSamplingRate())
ref_nlsystem.SetInput(sweep.GetOutput())
kernel_linear = nlsp.linear_identification_temporalreversal(
sweep, ref_nlsystem.GetOutput())
iden_linsystem = nlsp.AliasCompensatingHammersteinModelUpandDown(
filter_impulseresponse=kernel_linear)
if reference is not None:
reference = nlsp.change_length_signal(reference,
length=len(input_signal))
ref_nlsystem.SetInput(reference)
iden_linsystem.SetInput(reference)
iden_nlsystem.SetInput(reference)
if Plot is True:
plot.relabelandplot(sumpf.modules.FourierTransform(
ref_nlsystem.GetOutput()).GetSpectrum(),
"Reference System",
show=False)
plot.relabelandplot(sumpf.modules.FourierTransform(
iden_nlsystem.GetOutput()).GetSpectrum(),
"Identified System",
show=False)
plot.relabelandplot(sumpf.modules.FourierTransform(
iden_linsystem.GetOutput()).GetSpectrum(),
"Identified linear System",
show=True)
print "SNR between Reference and Identified output for linear systems: %r" % nlsp.snr(
ref_nlsystem.GetOutput(), iden_nlsystem.GetOutput())
print "SNR between Reference and Identified output for linear systems(linear identification): %r" % nlsp.snr(
ref_nlsystem.GetOutput(), iden_linsystem.GetOutput())
def adaptive_initializedwith_sweepidentification():
# generate virtual nonlinear system using HGM
ref_nlsystem = nlsp.HammersteinGroupModel_up(nonlinear_functions=nlsp.nl_branches(nlsp.function_factory.power_series,branches),
filter_irs=filter_spec_tofind_noise,
max_harmonics=range(1,branches+1))
# give input and get output from the virtual nlsystem
ref_nlsystem.SetInput(input)
output_noise = ref_nlsystem.GetOutput()
input_noise = input
ref_nlsystem.SetInput(sine_g.GetOutput())
output_sine = ref_nlsystem.GetOutput()
ref_nlsystem.SetInput(cos_g.GetOutput())
output_cos = ref_nlsystem.GetOutput()
# only sine based system identification
found_filter_spec_sine, nl_function_sine = nlsp.systemidentification("powerhgmbp1",nlsp.nonlinearconvolution_powerseries_temporalreversal,
branches,sine_g,output_sine)
iden_nlsystem_sine = nlsp.HammersteinGroupModel_up(max_harmonics=range(1,branches+1),nonlinear_functions=nl_function_sine,
filter_irs=found_filter_spec_sine)
# only cosine based system identification
found_filter_spec_cos, nl_function_cos = nlsp.systemidentification("powerhgmbp1",nlsp.nonlinearconvolution_chebyshev_temporalreversal,
branches,cos_g,output_cos)
iden_nlsystem_cos = nlsp.HammersteinGroupModel_up(max_harmonics=range(1,branches+1),nonlinear_functions=nl_function_cos,
filter_irs=found_filter_spec_cos)
# only noise based system identification
found_filter_spec_adapt, nl_function_adapt = nlsp.systemidentification("powerhgmbp1",nlsp.adaptive_identification_hermite,
branches,wgn_normal_g,output_noise)
iden_nlsystem_adapt = nlsp.HammersteinGroupModel_up(max_harmonics=range(1,branches+1),nonlinear_functions=nl_function_adapt,
filter_irs=found_filter_spec_adapt)
# sine based as init coeff for noise based system identification
found_filter_spec_sine_reducedlength = nlsp.change_length_filterkernels(found_filter_spec_sine,length=filter_taps)
found_filter_spec_sineadapt_power, nl_function_sineadapt_power = nlsp.adaptive_identification_powerseries(input_generator=input_noise,outputs=output_noise,iterations=i,branches=branches,
step_size=0.1,filtertaps=filter_taps,algorithm=nlsp.multichannel_nlms,Plot=False,init_coeffs=found_filter_spec_sine_reducedlength,Print=True)
found_filter_spec_sineadapt_hermite, nl_function_sineadapt_hermite = nlsp.adaptive_identification_hermite(input_generator=input_noise,outputs=output_noise,iterations=i,branches=branches,
step_size=0.1,filtertaps=filter_taps,algorithm=nlsp.multichannel_nlms,Plot=False,init_coeffs=found_filter_spec_sine_reducedlength,Print=True)
found_filter_spec_sineadapt_legendre, nl_function_sineadapt_legendre = nlsp.adaptive_identification_legendre(input_generator=input_noise,outputs=output_noise,iterations=i,branches=branches,
step_size=0.1,filtertaps=filter_taps,algorithm=nlsp.multichannel_nlms,Plot=False,init_coeffs=found_filter_spec_sine_reducedlength,Print=True)
found_filter_spec_sineadapt_cheby, nl_function_sineadapt_cheby = nlsp.adaptive_identification_chebyshev(input_generator=input_noise,outputs=output_noise,iterations=i,branches=branches,
step_size=0.1,filtertaps=filter_taps,algorithm=nlsp.multichannel_nlms,Plot=False,init_coeffs=found_filter_spec_sine_reducedlength,Print=True)
iden_nlsystem_sineadapt_power = nlsp.HammersteinGroupModel_up(max_harmonics=range(1,branches+1),nonlinear_functions=nl_function_sineadapt_power,
filter_irs=found_filter_spec_sineadapt_power)
iden_nlsystem_sineadapt_cheby = nlsp.HammersteinGroupModel_up(max_harmonics=range(1,branches+1),nonlinear_functions=nl_function_sineadapt_cheby,
filter_irs=found_filter_spec_sineadapt_cheby)
iden_nlsystem_sineadapt_hermite = nlsp.HammersteinGroupModel_up(max_harmonics=range(1,branches+1),nonlinear_functions=nl_function_sineadapt_hermite,
filter_irs=found_filter_spec_sineadapt_hermite)
iden_nlsystem_sineadapt_legendre = nlsp.HammersteinGroupModel_up(max_harmonics=range(1,branches+1),nonlinear_functions=nl_function_sineadapt_legendre,
filter_irs=found_filter_spec_sineadapt_legendre)
# cos based as init coeff for noise based system identification
found_filter_spec_cos_reducedlength = nlsp.change_length_filterkernels(found_filter_spec_cos,length=filter_taps)
found_filter_spec_cosadapt_cheby, nl_function_cosadapt_cheby = nlsp.adaptive_identification_chebyshev(input_generator=input_noise,outputs=output_noise,iterations=i,branches=branches,
step_size=0.1,filtertaps=filter_taps,algorithm=nlsp.multichannel_nlms,Plot=False,init_coeffs=found_filter_spec_cos_reducedlength,Print=True)
found_filter_spec_cosadapt_power, nl_function_cosadapt_power = nlsp.adaptive_identification_powerseries(input_generator=input_noise,outputs=output_noise,iterations=i,branches=branches,
step_size=0.1,filtertaps=filter_taps,algorithm=nlsp.multichannel_nlms,Plot=False,init_coeffs=found_filter_spec_cos_reducedlength,Print=True)
found_filter_spec_cosadapt_legendre, nl_function_cosadapt_legendre = nlsp.adaptive_identification_legendre(input_generator=input_noise,outputs=output_noise,iterations=i,branches=branches,
step_size=0.1,filtertaps=filter_taps,algorithm=nlsp.multichannel_nlms,Plot=False,init_coeffs=found_filter_spec_cos_reducedlength,Print=True)
found_filter_spec_cosadapt_hermite, nl_function_cosadapt_hermite = nlsp.adaptive_identification_hermite(input_generator=input_noise,outputs=output_noise,iterations=i,branches=branches,
step_size=0.1,filtertaps=filter_taps,algorithm=nlsp.multichannel_nlms,Plot=False,init_coeffs=found_filter_spec_cos_reducedlength,Print=True)
iden_nlsystem_cosadapt_cheby = nlsp.HammersteinGroupModel_up(max_harmonics=range(1,branches+1),nonlinear_functions=nl_function_cosadapt_cheby,
filter_irs=found_filter_spec_cosadapt_cheby)
iden_nlsystem_cosadapt_power = nlsp.HammersteinGroupModel_up(max_harmonics=range(1,branches+1),nonlinear_functions=nl_function_cosadapt_power,
filter_irs=found_filter_spec_cosadapt_power)
iden_nlsystem_cosadapt_hermite = nlsp.HammersteinGroupModel_up(max_harmonics=range(1,branches+1),nonlinear_functions=nl_function_cosadapt_hermite,
filter_irs=found_filter_spec_cosadapt_hermite)
iden_nlsystem_cosadapt_legendre = nlsp.HammersteinGroupModel_up(max_harmonics=range(1,branches+1),nonlinear_functions=nl_function_cosadapt_legendre,
filter_irs=found_filter_spec_cosadapt_legendre)
# set reference input to virtual nlsystem and identified nl system
ref_nlsystem.SetInput(reference)
iden_nlsystem_sine.SetInput(reference)
iden_nlsystem_cos.SetInput(reference)
iden_nlsystem_adapt.SetInput(reference)
iden_nlsystem_sineadapt_power.SetInput(reference)
iden_nlsystem_sineadapt_cheby.SetInput(reference)
iden_nlsystem_sineadapt_legendre.SetInput(reference)
iden_nlsystem_sineadapt_hermite.SetInput(reference)
iden_nlsystem_cosadapt_power.SetInput(reference)
iden_nlsystem_cosadapt_cheby.SetInput(reference)
iden_nlsystem_cosadapt_legendre.SetInput(reference)
iden_nlsystem_cosadapt_hermite.SetInput(reference)
# calculate snr value
powerseries_snr = nlsp.snr(ref_nlsystem.GetOutput(),iden_nlsystem_sine.GetOutput())
chebyshev_snr = nlsp.snr(ref_nlsystem.GetOutput(),iden_nlsystem_cos.GetOutput())
adaptive_snr = nlsp.snr(ref_nlsystem.GetOutput(),iden_nlsystem_adapt.GetOutput())
adaptivesine_power_snr = nlsp.snr(ref_nlsystem.GetOutput(),iden_nlsystem_sineadapt_power.GetOutput())
adaptivesine_cheby_snr = nlsp.snr(ref_nlsystem.GetOutput(),iden_nlsystem_sineadapt_cheby.GetOutput())
adaptivesine_hermite_snr = nlsp.snr(ref_nlsystem.GetOutput(),iden_nlsystem_sineadapt_hermite.GetOutput())
adaptivesine_legendre_snr = nlsp.snr(ref_nlsystem.GetOutput(),iden_nlsystem_sineadapt_legendre.GetOutput())
adaptivecos_power_snr = nlsp.snr(ref_nlsystem.GetOutput(),iden_nlsystem_cosadapt_power.GetOutput())
adaptivecos_cheby_snr = nlsp.snr(ref_nlsystem.GetOutput(),iden_nlsystem_cosadapt_cheby.GetOutput())
adaptivecos_legendre_snr = nlsp.snr(ref_nlsystem.GetOutput(),iden_nlsystem_cosadapt_legendre.GetOutput())
adaptivecos_hermite_snr = nlsp.snr(ref_nlsystem.GetOutput(),iden_nlsystem_cosadapt_hermite.GetOutput())
# print snr value
print "SNR of powerseries nl convolution: %r" %powerseries_snr
print "SNR of chebyshev nl convolution: %r" %chebyshev_snr
print "SNR of adaptive: %r" %adaptive_snr
print "SNR of adaptive sine power: %r" %adaptivesine_power_snr
print "SNR of adaptive sine cheby: %r" %adaptivesine_cheby_snr
print "SNR of adaptive sine hermite: %r" %adaptivesine_hermite_snr
print "SNR of adaptive sine legendre: %r" %adaptivesine_legendre_snr
print "SNR of adaptive cos power: %r" %adaptivecos_power_snr
print "SNR of adaptive cos cheby: %r" %adaptivecos_cheby_snr
print "SNR of adaptive cos legendre: %r" %adaptivecos_legendre_snr
print "SNR of adaptive cos hermite: %r" %adaptivecos_hermite_snr
