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