def collect_parameter_spec_list_cocktail16_w0_hyper_alpha_lambda1p6():
"""
Add the lambda1p6 (i.e., lambda fixed to value 1.6)
:return:
"""
spec_list = generate_parameter_spec_ab_product_hyper_alpha_debug(
gen_param_files_p=False)
pspec_list = [
experiment_tools.ParameterSpec(parameters_file, parameters_dir,
model_filename_postfix) for
parameters_file, parameters_dir, model_filename_postfix in spec_list
]
pspec_list += \
[experiment_tools.ParameterSpec(
parameters_file='music_bach_LT_HMM_W0-J600_aalpha1_balpha1_lambda1p6.config',
parameters_dir='experiment/parameters/music_bach_hyper_alpha_debug',
model_filename_postfix='aalpha1_balpha1_lambda1p6'),
experiment_tools.ParameterSpec(
parameters_file='music_bach_LT_HMM_W0-J600_aalpha1_balpha0p1_lambda1p6.config',
parameters_dir='experiment/parameters/music_bach_hyper_alpha_debug',
model_filename_postfix='aalpha1_balpha0p1_lambda1p6'),
experiment_tools.ParameterSpec(
parameters_file='music_bach_LT_HMM_W0-J600_aalpha0p1_balpha1_lambda1p6.config',
parameters_dir='experiment/parameters/music_bach_hyper_alpha_debug',
model_filename_postfix='aalpha0p1_balpha1_lambda1p6'),
experiment_tools.ParameterSpec(
parameters_file='music_bach_LT_HMM_W0-J600_aalpha0p1_balpha0p1_lambda1p6.config',
parameters_dir='experiment/parameters/music_bach_hyper_alpha_debug',
model_filename_postfix='aalpha0p1_balpha0p1_lambda1p6')
]
return pspec_list
def collect_parameter_spec_list_cocktail16_w0_hyperab(parameters_path,
hyperparam):
"""
cp **NO** weight learning (w0), 1500 iterations, D=16, and J=600 for hmm
works with: cocktail_s16_m12
:return:
"""
bfact = [experiment_tools.ParameterSpec
('music_bach_BFact_HMM_W0_a{0}{1}_b{0}{2}.config' \
.format(hyperparam, float2string(aalpha), float2string(balpha)),
parameters_path,
'a{0}{1}_b{0}{2}' \
.format(hyperparam, float2string(aalpha), float2string(balpha)))
for aalpha in (0.01, 0.1, 5)
for balpha in (0.01, 0.1, 5)]
lt = [experiment_tools.ParameterSpec
('music_bach_LT_HMM_W0-J600_a{0}{1}_b{0}{2}.config' \
.format(hyperparam, float2string(aalpha), float2string(balpha)),
parameters_path,
'a{0}{1}_b{0}{2}' \
.format(hyperparam, float2string(aalpha), float2string(balpha)))
for aalpha in (0.01, 0.1, 5)
for balpha in (0.01, 0.1, 5)]
nolt = [experiment_tools.ParameterSpec
('music_bach_noLT_HMM_W0-J600_a{0}{1}_b{0}{2}.config' \
.format(hyperparam, float2string(aalpha), float2string(balpha)),
parameters_path,
'a{0}{1}_b{0}{2}' \
.format(hyperparam, float2string(aalpha), float2string(balpha)))
for aalpha in (0.01, 0.1, 5)
for balpha in (0.01, 0.1, 5)]
return bfact + lt + nolt
def collect_parameter_spec_list_latent_continue_syn_block_diag12_10000itr_hmc(parameters_path):
"""
Latent continuous state synthetic experiment parameters
:return:
"""
return [ experiment_tools.ParameterSpec('block_diag12_LT_10000itr_hmc.config', parameters_path),
experiment_tools.ParameterSpec('block_diag12_noLT_10000itr_hmc.config', parameters_path) ]
def collect_parameter_spec_list_latent_continue_syn_block_diag40_lambda1(parameters_path):
"""
Latent continuous state synthetic experiment parameters
J=100, to accommodate the 40-state block-diagonal data
10,000 iterations, hmc
:return:
"""
return [ experiment_tools.ParameterSpec('block_diag40_LT_lambda1.config', parameters_path),
experiment_tools.ParameterSpec('block_diag40_noLT_lambda1.config', parameters_path) ]
def collect_parameter_spec_list_music_chord1_J2(parameters_path):
"""
Music chord 1 **NO** weight learning (w0), 10000 iterations, J=2
works with:
:return:
"""
return [
experiment_tools.ParameterSpec('music_chord1_LT_J2.config',
parameters_path),
experiment_tools.ParameterSpec('music_chord1_noLT_J2.config',
parameters_path)
]
def collect_parameter_spec_list_music_simple_melody1_pitch(parameters_path):
"""
Music simple melody 1 PITCH ONLY **NO** weight learning (w0), 100,000 iterations
works with:
:return:
"""
return [
experiment_tools.ParameterSpec('music_simple_melody1_pitch_LT.config',
parameters_path),
experiment_tools.ParameterSpec(
'music_simple_melody1_pitch_noLT.config', parameters_path)
]
def collect_parameter_spec_list_cocktail16_w0_hyper_h():
spec_list = generate_parameter_spec_ab_product_hyper_h(
gen_param_files_p=False)
return [
experiment_tools.ParameterSpec(parameters_file, parameters_dir,
model_filename_postfix) for
parameters_file, parameters_dir, model_filename_postfix in spec_list
]
def collect_parameter_spec_list_cocktail16_w0(parameters_path):
"""
cp **NO** weight learning (w0), 1500 iterations, D=16, and J=600 for hmm
works with: cocktail_s16_m12
:return:
"""
return [ # experiment_tools.ParameterSpec('cocktail16_inference_BFact_HMM_W0.config', parameters_path),
# experiment_tools.ParameterSpec('cocktail16_inference_LT_HMM_W0-J600.config', parameters_path),
experiment_tools.ParameterSpec('cocktail16_inference_noLT_HMM_W0-J600.config', parameters_path)
]
def collect_parameter_spec_list_cocktail16_w0_all_models():
return \
(experiment_tools.ParameterSpec(
parameters_file='cocktail16_inference_BFact_HMM_W0.config',
parameters_dir='experiment/parameters',
model_filename_postfix=''),
experiment_tools.ParameterSpec(
parameters_file='cocktail16_inference_LT_HMM_W0-J200.config',
parameters_dir='experiment/parameters',
model_filename_postfix=''),
experiment_tools.ParameterSpec(
parameters_file='cocktail16_inference_noLT_HMM_W0-J200.config',
parameters_dir='experiment/parameters',
model_filename_postfix=''),
experiment_tools.ParameterSpec(
parameters_file='cocktail16_inference_sticky_HMM_W0-J200.config',
parameters_dir='experiment/parameters',
model_filename_postfix=''),
experiment_tools.ParameterSpec(
parameters_file='cocktail16_inference_stickyLT_HMM_W0-J200.config',
parameters_dir='experiment/parameters',
model_filename_postfix=''))
def collect_parameter_spec_list_music_bach_StickyLT_lambda_epsilon_D3():
"""
parameter_spec list for bach StickyLT *ONLY* lambda_epsilon D3 experiment
:return:
"""
spec_list = generate_parameter_spec_lambda_epsilon_bach_StickyLT_D3(
gen_param_files_p=False, verbose=False)
pspec_list = [
experiment_tools.ParameterSpec(parameters_file, parameters_dir,
model_filename_postfix) for
parameters_file, parameters_dir, model_filename_postfix in spec_list
]
return pspec_list
def collect_parameter_spec_list_cocktail16_w0_hyper_regression(
param_var='alpha'):
"""
REGRESSION TEST Version of get parameter_spec_list for hyper
sets a/b_<hyper_param_var>
:param param_var:
:return:
"""
spec_list = generate_parameter_spec_ab_product_hyper_regression_test(
param_var=param_var, gen_param_files_p=False)
return [
experiment_tools.ParameterSpec(parameters_file, parameters_dir,
model_filename_postfix) for
parameters_file, parameters_dir, model_filename_postfix in spec_list
]
def collect_parameter_spec_list_music_bach_Sticky_StickyLT_lambda_epsilon():
"""
parameter_spec list for bach Sticky and StickyLT lambda_epsilon experiment
also includes music_bach_major_noLT.config
:return:
"""
spec_list = generate_parameter_spec_lambda_epsilon_bach_stickyLT(
gen_param_files_p=False, verbose=False)
pspec_list = [
experiment_tools.ParameterSpec(parameters_file, parameters_dir,
model_filename_postfix) for
parameters_file, parameters_dir, model_filename_postfix in spec_list
]
pspec_list += \
[experiment_tools.ParameterSpec \
(parameters_file='music_bach_major_Sticky.config',
parameters_dir='experiment/parameters',
model_filename_postfix='')]
return pspec_list
