def get_standard_design(preprocess = False, proba = False, small = False):
save_name = 'st_design'
if proba:
if preprocess:
save_name = 'st_design_proba'
elif small:
save_name = 'small_st_design_proba'
else:
save_name = 'st_design_all_proba'
try:
design, labels, ids, explanation = load_variables(save_name)
except FileNotFoundError:
design, labels, ids, explanation = get_standard_design(
preprocess = preprocess, proba = False, small = small)
design = proba_design(design)
save_variables(save_name, [design, labels, ids, explanation])
else:
if preprocess:
save_name = 'st_design'
try:
design, labels, ids, explanation = load_variables(save_name)
except FileNotFoundError:
design, labels, ids, explanation = get_standard_design(
preprocess = False, proba = False, small = False)
design,explanation=preprocess_design(design,labels,explanation)
save_variables(save_name, [design, labels, ids, explanation])
elif small:
save_name = 'small_st_design'
try:
design, labels, ids, explanation = load_variables(save_name)
except FileNotFoundError:
design, labels, ids, explanation = get_standard_design(
preprocess = False, proba = False, small = False)
design, explanation = small_design(design, labels, explanation)
save_variables(save_name, [design, labels, ids, explanation])
else:
save_name = 'st_design_all'
try:
design, labels, ids, explanation = load_variables(save_name)
except FileNotFoundError:
names = ['second_layer', 'motor_behaviors']
designs, explanations = [], []
for name in names:
design, labels, ids, explanation = get_design(name)
designs.append(design)
explanations.append(explanation)
design, explanation = concatenate_design(designs, explanations)
save_variables(save_name, [design, labels, ids, explanation])
return design, labels, ids, explanation
def get_center(event):
name = event[1]
try:
center, mean, std = load_variables(name)
except FileNotFoundError:
try:
st_data, _, _, _ = load_variables('preprocess')
except FileNotFoundError:
extractor = preprocess_extractor
explanation = FACE_PARAMETERIZATION
st_data, labels, ids = homogenized_description(extractor)
save_variables('preprocess', [st_data, labels, ids, explanation])
center, mean, std = perform_clustering(st_data, event)
save_variables(name, [center, mean, std])
return center, mean, std
def get_histogram_bins(nb_bins=NB_BINS):
name = 'all_bins'
try:
bins = get_histogram_bins.BINS
except AttributeError:
try:
print('Loading histogram bins.')
bins = load_variables(name)
except FileNotFoundError:
print('Computing histogram bins.')
ext = first_layer_extractor
st_desc, _, _ = homogenized_description(ext, homogenize=False)
all_val = concatenate_all(st_desc)
all_val[np.isnan(all_val)] = 0
length = all_val.shape[0]
if type(nb_bins) == int:
nb_bins = nb_bins * np.ones(length)
bins = [[] for k in range(length)]
for k in range(length):
val = all_val[k, :]
bins[k] = empirical_bins(val, nb_bins=nb_bins[k])
save_variables(name, bins)
get_histogram_bins.BINS = bins
return bins
def get_small_design(name):
save_name = 'small_design_' + name
try:
design, labels, ids, explanation = load_variables(save_name)
except FileNotFoundError:
design, labels, ids, explanation = get_design(name)
design, explanation = small_design(design, labels, explanation)
save_variables(save_name, [design, labels, ids, explanation])
return design, labels, ids, np.array(explanation)
def get_design(name):
save_name = 'design_' + name
try:
design, labels, ids, explanation = load_variables(save_name)
except FileNotFoundError:
st_data, labels, ids, explanation = get_layer(name)
design, explanation = cast_design(st_data, explanation)
save_variables(save_name, [design, labels, ids, explanation])
return design, labels, ids, np.array(explanation)
def get_layer(name):
try:
data, labels, ids, explanation = load_variables(name)
except FileNotFoundError:
if name == 'face_parameterization':
extractor = face_parameterization_extractor
explanation = FACE_PARAMETERIZATION
elif name == 'preprocess':
extractor = preprocess_extractor
explanation = FACE_PARAMETERIZATION
elif name == 'event':
extractor = event_extractor
explanation = EVENT
elif name == 'video_response':
extractor = video_response_extractor
explanation = VIDEO_RESPONSE
elif name == 'pattern_response':
extractor = pattern_response_extractor
explanation = PATTERN_RESPONSE
elif name == 'patterns_response':
extractor = patterns_response_extractor
explanation = PATTERNS_RESPONSE
elif name == 'first_layer':
extractor = first_layer_extractor
explanation = FIRST_LAYER
elif name == 'second_layer':
extractor = second_layer_extractor
explanation = SECOND_LAYER
elif name == 'assignation':
extractor = assignation_extractor
explanation = ASSIGNATION
elif name == 'histogram':
extractor = histogram_extractor
explanation = HISTOGRAM
elif name == "motor_behaviors":
extractor = motor_behaviors_extractor
explanation = MOTOR_BEHAVIORS
else:
print(name, 'not implemented yet')
raise ValueError
data, labels, ids = homogenized_description(extractor)
save_variables(name, [data, labels, ids, explanation])
return data, labels, ids, explanation
def __init__(self, config_filename):
self.config_filename = config_filename
self.config_dictionary = helper.load_variables(config_filename)
self.load_dictionary()