def run_command(feature_type, missing_data, data_object_base_path_name, base_out_path, input_dims, tone_list, dur_position, num_sampling, d1, d2):
deltas = [
[d1, d2]
]
output_name_paths = []
for i, d in enumerate(deltas):
outp = '{}/input_dims_{}/delta-{}_delta-delta-{}/'.format(base_out_path, input_dims, d[0], d[1])
output_name_paths.append(outp)
print 'Missing Data : {}'.format(missing_data)
print 'Inducing points : 10 percent'
for idx, output_name in enumerate(output_name_paths):
delta_bool=deltas[idx][0]
delta2_bool=deltas[idx][1]
if missing_data:
method_name = 'BayesianGPLVMMiniBatch_Missing'
else :
method_name = 'BGP_LVM'
for tone in tone_list:
print 'Delta : {}, Delta-Dealta : {}'.format(delta_bool, delta2_bool)
data_object_path = '{}{}.pickle'.format(data_object_base_path_name, tone)
print 'data path ',data_object_path
syllable_management = Utility.load_obj(data_object_path)
if len(syllable_management.syllables_list) == 0:
print 'No syllable in this object database : {}'.format(tone)
print '-----------------------------------------------------------------'
continue
output_path = '{}/{}_Tone_{}/'.format(output_name, method_name, tone)
Utility.make_directory(output_path)
print output_path
Latent_variable_model_Training.execute_Bayesian_GPLVM_training(
syllable_management,
feature_type,
input_dims,
output_path,
num_sampling=num_sampling,
dur_position=dur_position,
delta_bool=delta_bool,
delta2_bool=delta2_bool,
missing_data=missing_data,
num_inducing=int(len(syllable_management.syllables_list)*0.1),
max_iters=500)
pass
def run_for_voice_data():
dropbox_path = '/home/h1/decha/Dropbox/'
output_name,delta_bool,delta2_bool = '02_delta_delta-delta', True, True
# output_name,delta_bool,delta2_bool = '03_delta', True, False
# output_name,delta_bool,delta2_bool = '04_no_delta', False, False
input_dims = 3
for tone in ['0','1','2','3','4', '01234']:
# for tone in ['01234']:
print 'Running Tone : {}'.format(tone)
if tone is '01234':
data_object_path = '{}/Inter_speech_2016/Syllable_object/01_manual_labeling_object/syllable_all.pickle'.format(dropbox_path)
syllable_management = Utility.load_obj(data_object_path)
else :
data_object_path = '{}/Inter_speech_2016/Syllable_object/01_manual_labeling_object/syllable_{}.pickle'.format(dropbox_path,tone)
syllable_management = Utility.load_obj(data_object_path)
print 'Delta : {}, Delta-Dealta : {}'.format(delta_bool, delta2_bool)
output_path = '{}/Inter_speech_2016/Syllable_object/{}/BGP_LVM/{}_dimentionality/Tone_{}/'.format(dropbox_path,output_name,input_dims,tone)
print output_path
Latent_variable_model_Training.execute_Bayesian_GPLVM_training(
syllable_management,
Syllable.TRAINING_FEATURE_POLYNOMIAL_2_DEGREE_VOICE,
input_dims,
output_path,
delta_bool=delta_bool,
delta2_bool=delta2_bool)
pass
def run_command(feature_type, missing_data, data_object_base_path_name, base_out_path, input_dims, tone_list, dur_position):
deltas = [
[False, False],
[True, False],
[True, True]
]
output_name_paths = []
for i, d in enumerate(deltas):
outp = '{}/input_dims_{}/{}_delta-{}_delta-delta-{}/'.format(base_out_path, input_dims, Utility.fill_zero(i+1,2), d[0], d[1])
output_name_paths.append(outp)
print 'Missing Data : {}'.format(missing_data)
for idx, output_name in enumerate(output_name_paths):
delta_bool=deltas[idx][0]
delta2_bool=deltas[idx][1]
if missing_data:
method_name = 'BayesianGPLVMMiniBatch_Missing'
else :
method_name = 'BGP_LVM'
for tone in tone_list:
print 'Delta : {}, Delta-Dealta : {}'.format(delta_bool, delta2_bool)
data_object_path = '{}{}.pickle'.format(data_object_base_path_name, tone)
print 'data path ',data_object_path
syllable_management = Utility.load_obj(data_object_path)
if len(syllable_management.syllables_list) == 0:
print 'No syllable in this object database : {}'.format(tone)
print '-----------------------------------------------------------------'
continue
output_path = '{}/{}_Tone_{}/'.format(output_name, method_name, tone)
Utility.make_directory(output_path)
print output_path
Latent_variable_model_Training.execute_Bayesian_GPLVM_training(
syllable_management,
feature_type,
input_dims,
output_path,
dur_position=dur_position,
subtract_typical_contour=False,
exp=False,
delta_bool=delta_bool,
delta2_bool=delta2_bool,
missing_data=missing_data)
pass
def run_for_missing_data():
dropbox_path = '/home/h1/decha/Dropbox/'
output_name = '05_missing_data_no_delta'
delta_bool=False
delta2_bool=False
input_dims = 3
for tone in ['01234']:
print 'Delta : {}, Delta-Dealta : {}'.format(delta_bool, delta2_bool)
print 'Missing data'
data_object_path = '{}/Inter_speech_2016/Syllable_object/01_manual_labeling_object/syllable_{}.pickle'.format(dropbox_path,tone)
if tone is '01234':
data_object_path = '{}/Inter_speech_2016/Syllable_object/01_manual_labeling_object/syllable_all.pickle'.format(dropbox_path)
syllable_management = Utility.load_obj(data_object_path)
output_path = '{}/Inter_speech_2016/Syllable_object/{}/BGP_LVM/Tone_{}/'.format(dropbox_path,output_name,tone)
print output_path
Latent_variable_model_Training.execute_Bayesian_GPLVM_training(
syllable_management,
Syllable.MISSING_VALUES,
input_dims,
output_path,
delta_bool=delta_bool,
delta2_bool=delta2_bool,
missing_data=True)
pass
pass
def run_command(feature_type, missing_data, data_object_base_path_name,
base_out_path, input_dims, tone_list, dur_position,
num_sampling, d1, d2):
deltas = [[d1, d2]]
output_name_paths = []
for i, d in enumerate(deltas):
outp = '{}/input_dims_{}/delta-{}_delta-delta-{}/'.format(
base_out_path, input_dims, d[0], d[1])
output_name_paths.append(outp)
print 'Missing Data : {}'.format(missing_data)
print 'Inducing points : 10 percent'
for idx, output_name in enumerate(output_name_paths):
delta_bool = deltas[idx][0]
delta2_bool = deltas[idx][1]
if missing_data:
method_name = 'BayesianGPLVMMiniBatch_Missing'
else:
method_name = 'BGP_LVM'
for tone in tone_list:
print 'Delta : {}, Delta-Dealta : {}'.format(
delta_bool, delta2_bool)
data_object_path = '{}{}.pickle'.format(data_object_base_path_name,
tone)
print 'data path ', data_object_path
syllable_management = Utility.load_obj(data_object_path)
if len(syllable_management.syllables_list) == 0:
print 'No syllable in this object database : {}'.format(tone)
print '-----------------------------------------------------------------'
continue
print 'Syllable all : {} Syllables'.format(
len(syllable_management.syllables_list))
output_path = '{}/{}_Tone_{}/'.format(output_name, method_name,
tone)
Utility.make_directory(output_path)
print output_path
print 'Feature Key : {}'.format(feature_type)
Y_full, names, tone, stress, syllable_short_long_type, syllalbe_position, phoneme, syllable_type = syllable_management.get_GP_LVM_training_data(
feature_key=feature_type,
dur_position=dur_position,
delta_bool=delta_bool,
delta2_bool=delta2_bool,
missing_data=missing_data,
num_sampling=num_sampling,
)
print 'Y full : {}'.format(np.array(Y_full).shape)
Y, names, tone, stress, syllable_short_long_type, syllalbe_position, phoneme, syllable_type = syllable_management.get_GP_LVM_training_data(
feature_key=feature_type,
dur_position=dur_position,
delta_bool=delta_bool,
delta2_bool=delta2_bool,
missing_data=missing_data,
num_sampling=num_sampling,
no_short_duration=True,
get_only_stress=False,
exp=False,
subtract_typical_contour=False,
non_unlabelled_stress=False,
get_only_gpr_data=False)
Latent_variable_model_Training.execute_Bayesian_GPLVM_training_with_Y_names(
Y,
names,
input_dims,
output_path,
missing_data=False,
max_iters=500,
num_inducing=int(len(Y) * 0.1),
BayesianGPLVMMiniBatch=False)
pass
def run_for_missing_data_with_delta_and_interpolate():
dropbox_path = '/home/h1/decha/Dropbox/'
# 01
output_name_paths = [
# '/work/w13/decha/Inter_speech_2016_workplace/gpr-projection-additional/01_gpr_a-5dims_BayesianGPLVMMiniBatch_data_no_delta/',
# '/work/w13/decha/Inter_speech_2016_workplace/gpr-projection-additional/01_gpr_b-5dims_BayesianGPLVMMiniBatch_data_delta/',
'/work/w13/decha/Inter_speech_2016_workplace/gpr-projection-additional/01_gpr_c-5dims_BayesianGPLVMMiniBatch_data_delta_deltadelta/'
]
missing_data = True
subtract_typical_contour = True
# 02
# subtract_typical_contour = False
# output_name_paths = [
# '/work/w13/decha/Inter_speech_2016_workplace/gpr-projection-additional/02_gpr_a-5dims_BayesianGPLVMMiniBatch_data_no_delta/',
# '/work/w13/decha/Inter_speech_2016_workplace/gpr-projection-additional/02_gpr_b-5dims_BayesianGPLVMMiniBatch_data_delta/',
# '/work/w13/decha/Inter_speech_2016_workplace/gpr-projection-additional/02_gpr_c-5dims_BayesianGPLVMMiniBatch_data_delta_deltadelta/'
# ]
print 'Missing Data : {}'.format(missing_data)
print 'Subtract_typical_contour : {}'.format(subtract_typical_contour)
deltas = [
# [False, False],
# [True, False],
[True, True]
]
# input_dims = 3
input_dims = 5
for idx, output_name in enumerate(output_name_paths):
delta_bool = deltas[idx][0]
delta2_bool = deltas[idx][1]
if missing_data:
method_name = 'BayesianGPLVMMiniBatch_Missing'
else:
method_name = 'BGP_LVM'
# for tone in ['0','1','2','3','4','01234']:
for tone in ['0', '1', '2', '3', '4']:
# for tone in ['01234']:
print 'Delta : {}, Delta-Dealta : {}'.format(
delta_bool, delta2_bool)
print 'Missing data'
# data_object_path = '{}/Inter_speech_2016/Syllable_object/01_manual_labeling_object/syllable_{}.pickle'.format(dropbox_path,tone)
data_object_path = '/home/h1/decha/Dropbox/Inter_speech_2016/Syllable_object/GPR-data-work-space/gpr_syllable_obj/Syllable_object_a_to_d_tone_{}.pickle'.format(
tone)
if tone is '01234':
# data_object_path = '{}/Inter_speech_2016/Syllable_object/01_manual_labeling_object/syllable_all.pickle'.format(dropbox_path)
data_object_path = '/home/h1/decha/Dropbox/Inter_speech_2016/Syllable_object/GPR-data-work-space/gpr_syllable_obj/Syllable_object_a_to_d.pickle'
syllable_management = Utility.load_obj(data_object_path)
output_path = '{}/{}/Tone_{}/'.format(output_name, method_name,
tone)
Utility.make_directory(output_path)
print output_path
Latent_variable_model_Training.execute_Bayesian_GPLVM_training(
syllable_management,
Syllable.TRAINING_FEATURE_POLYNOMIAL_2_DEGREE_VOICE,
input_dims,
output_path,
subtract_typical_contour=subtract_typical_contour,
delta_bool=delta_bool,
delta2_bool=delta2_bool,
missing_data=missing_data)
pass
def run_for_missing_data_with_delta_and_interpolate(max_iters):
dropbox_path = '/home/h1/decha/Dropbox/'
# output_name = '05_missing_data_no_delta'
# output_name_paths = [
# '/work/w13/decha/Inter_speech_2016_workplace/Data/07a_missing_data_no_delta/',
# '/work/w13/decha/Inter_speech_2016_workplace/Data/07b_missing_data_delta/',
# '/work/w13/decha/Inter_speech_2016_workplace/Data/07c_missing_data_delta_deltadelta/'
# ]
output_name_paths = [
# '/work/w13/decha/Inter_speech_2016_workplace/Data/07_max_iters_{}/07a-5dims_BayesianGPLVMMiniBatch_data_no_delta/'.format(max_iters) ,
# '/work/w13/decha/Inter_speech_2016_workplace/Data/07_max_iters_{}/07b-5dims_BayesianGPLVMMiniBatch_data_delta/'.format(max_iters) ,
'/work/w13/decha/Inter_speech_2016_workplace/Data/07_max_iters_{}/07c-5dims_BayesianGPLVMMiniBatch_data_delta_deltadelta/'.format(max_iters)
]
missing_data = True
subtract_typical_contour = False
print 'Missing Data : {}'.format(missing_data)
deltas = [
# [False, False],
# [True, False],
[True, True]
]
# input_dims = 3
input_dims = 5
for idx, output_name in enumerate(output_name_paths):
delta_bool=deltas[idx][0]
delta2_bool=deltas[idx][1]
if missing_data:
method_name = 'BayesianGPLVMMiniBatch_Missing'
else :
method_name = 'BGP_LVM'
# for tone in ['0','1','2','3','4','01234']:
for tone in ['01234']:
print 'Delta : {}, Delta-Dealta : {}'.format(delta_bool, delta2_bool)
print 'Missing data'
data_object_path = '{}/Inter_speech_2016/Syllable_object/01_manual_labeling_object/syllable_{}.pickle'.format(dropbox_path,tone)
if tone is '01234':
data_object_path = '{}/Inter_speech_2016/Syllable_object/01_manual_labeling_object/syllable_all.pickle'.format(dropbox_path)
syllable_management = Utility.load_obj(data_object_path)
output_path = '{}/{}/Tone_{}/'.format(output_name, method_name, tone)
Utility.make_directory(output_path)
print output_path
Latent_variable_model_Training.execute_Bayesian_GPLVM_training(
syllable_management,
# Syllable.TRAINING_FEATURE_POLYNOMIAL_2_DEGREE,
Syllable.TRAINING_FEATURE_POLYNOMIAL_2_DEGREE_VOICE,
input_dims,
output_path,
subtract_typical_contour=subtract_typical_contour,
delta_bool=delta_bool,
delta2_bool=delta2_bool,
missing_data=missing_data,
max_iters=max_iters)
pass
def run_for_mix(input_dims):
dropbox_path = '/home/h1/decha/Dropbox/'
missing_data = True
subtract_typical_contour = False
deltas = [
[False, False],
[True, False],
[True, True]
]
print 'This running aims to show the unlabelled data on the labeled data in distribution.'
print 'Do not use subtract contour'
data_object = '/home/h1/decha/Dropbox/Inter_speech_2016/Syllable_object/GPR-data-work-space/mix_syllable_obj/mix_syllable_'
output_name_paths = [
'/work/w13/decha/Inter_speech_2016_workplace/mix-projection-addtional/02_mix_a-{}dims_data_no_delta_missing_data_No-subtract_typical_contour/'.format(input_dims),
'/work/w13/decha/Inter_speech_2016_workplace/mix-projection-addtional/02_mix_b-{}dims_data_delta-{}_delta-delta-{}_missing_data_No-subtract_typical_contour/'.format(input_dims,deltas[1][0],deltas[1][1]),
'/work/w13/decha/Inter_speech_2016_workplace/mix-projection-addtional/02_mix_c-{}dims_data_delta-{}_delta-delta-{}_missing_data_No-subtract_typical_contour/'.format(input_dims,deltas[2][0],deltas[2][1]),
]
print 'Missing Data : {}'.format(missing_data)
print 'Subtract_typical_contour : {}'.format(subtract_typical_contour)
for idx, output_name in enumerate(output_name_paths):
delta_bool=deltas[idx][0]
delta2_bool=deltas[idx][1]
if missing_data:
method_name = 'BayesianGPLVMMiniBatch_Missing'
else :
method_name = 'BGP_LVM'
for tone in ['0','1','2','3','4','01234']:
# for tone in ['01234']:
print 'Delta : {}, Delta-Dealta : {}'.format(delta_bool, delta2_bool)
print 'Missing data'
data_object_path = '{}{}.pickle'.format(data_object, tone)
if tone is '01234':
data_object_path = '{}all.pickle'.format(data_object)
print data_object_path
syllable_management = Utility.load_obj(data_object_path)
output_path = '{}/{}/Tone_{}/'.format(output_name, method_name, tone)
Utility.make_directory(output_path)
print output_path
Latent_variable_model_Training.execute_Bayesian_GPLVM_training(
syllable_management,
Syllable.TRAINING_FEATURE_POLYNOMIAL_2_DEGREE_VOICE,
input_dims,
output_path,
subtract_typical_contour=subtract_typical_contour,
delta_bool=delta_bool,
delta2_bool=delta2_bool,
missing_data=missing_data)
pass
