def fromJSONDict(cls, json):
return cls(segment_start = cond_get(json, 'segment_start'),
labels = cond_get(json, 'labels'),
filename = cond_get(json, 'filename'),
learning = cond_get(json, 'learning'),
window_size = cond_get(json, 'window_size'),
data_index = cond_get(json, 'data_index'),
tau = cond_get(json, 'tau'))
def fromJSONDict(cls, json):
return cls(None,
config=Configuration.fromJSONDict(json['config']),
kernel_module=cond_get(json, 'kernel_module'),
kernel_arg=cond_get(json, 'kernel_arg'),
distances_module=cond_get(json, 'distances_module'),
distances_arg=cond_get(json, 'distances_arg'),
learning_module=cond_get(json, 'learning_module'),
learning_arg=cond_get(json, 'learning_arg'),
partitions=cond_get(json, 'partitions'))
def ArgsIter(args_l):
for args in args_l :
# make all the variable entries lists if they aren't already
iterargs = dict(map(lambda x: (x, cond_get_list(args,x)),
['segment_size', 'segment_stride', 'window_size', 'window_stride', \
'max_simplices', 'persistence_epsilon', 'post_process_arg']))
if not ('data_index' in args.keys()) or args['data_index'] == None :
iterargs['data_index'] = [None]
elif not isinstance(args['data_index'], list) :
iterargs['data_index'] = [args['data_index']]
else :
if isinstance(args['data_index'][0], list) :
iterargs['data_index'] = args['data_index']
else :
iterargs['data_index'] = [args['data_index']]
for (data_index, segment_size, segment_stride, window_size,
window_stride, max_simplices, persistence_epsilon, post_process_arg) in \
itertools.product(iterargs['data_index'],
iterargs['segment_size'],
iterargs['segment_stride'],
iterargs['window_size'],
iterargs['window_stride'],
iterargs['max_simplices'],
iterargs['persistence_epsilon'],
iterargs['post_process_arg']) :
yield Configuration(max_simplices,
persistence_epsilon,
segment_stride,
segment_size,
window_size,
window_stride,
cond_get(args,'kernel_scale'),
cond_get(args,'kernel_gamma'),
cond_get(args,'invariant_epsilon'),
cond_get(args, 'data_file'),
data_index,
cond_get(args,'label_index'),
cond_get(args,'out_directory'),
cond_get(args,'threads'),
cond_get(args,'learning_split'),
cond_get(args,'learning_iterations'),
cond_get(args,'learning_C'),
cond_get(args,'cv_iterations'),
cond_get(args,'persistence_degree'),
cond_get(args,'reevaluate'),
cond_get(args,'data_type'),
cond_get(args,'post_process'),
post_process_arg,
cond_get(args,'stop_after'),
cond_get(args,'status'))
def fromJSONDict(cls, json) :
return cls( cond_get(json,'max_simplices'),
cond_get(json,'persistence_epsilon'),
cond_get(json,'segment_stride'),
cond_get(json,'segment_size'),
cond_get(json,'window_size'),
cond_get(json,'window_stride'),
cond_get(json,'kernel_scale'),
cond_get(json,'kernel_gamma'),
cond_get(json,'invariant_epsilon'),
cond_get(json,'data_file'),
cond_get(json,'data_index'),
cond_get(json,'label_index'),
cond_get(json,'out_directory'),
cond_get(json,'threads'),
cond_get(json,'learning_split'),
cond_get(json,'learning_iterations'),
cond_get(json,'learning_C'),
cond_get(json,'cv_iterations'),
cond_get(json,'persistence_degree'),
cond_get(json,'reevaluate'),
cond_get(json,'data_type'),
cond_get(json,'post_process'),
cond_get(json,'post_process_arg'),
cond_get(json,'stop_after'),
cond_get(json,'status'))
def fromJSONDict(cls, json):
return cls(cond_get(json,'min'),
cond_get(json,'mean'),
cond_get(json,'max'),
cond_get(json,'std'))
def ArgsIter(args_l):
for args in args_l:
# make all the variable entries lists if they aren't already
iterargs = dict(map(lambda x: (x, cond_get_list(args,x)),
['segment_size', 'segment_stride', 'window_size', 'window_stride', \
'max_simplices', 'persistence_epsilon', 'post_process_arg']))
if not ('data_index' in args.keys()) or args['data_index'] == None:
iterargs['data_index'] = [None]
elif not isinstance(args['data_index'], list):
iterargs['data_index'] = [args['data_index']]
else:
if isinstance(args['data_index'][0], list):
iterargs['data_index'] = args['data_index']
else:
iterargs['data_index'] = [args['data_index']]
for (data_index, segment_size, segment_stride, window_size,
window_stride, max_simplices, persistence_epsilon, post_process_arg) in \
itertools.product(iterargs['data_index'],
iterargs['segment_size'],
iterargs['segment_stride'],
iterargs['window_size'],
iterargs['window_stride'],
iterargs['max_simplices'],
iterargs['persistence_epsilon'],
iterargs['post_process_arg']) :
yield Configuration(max_simplices, persistence_epsilon,
segment_stride, segment_size, window_size,
window_stride, cond_get(args, 'kernel_scale'),
cond_get(args, 'kernel_gamma'),
cond_get(args, 'invariant_epsilon'),
cond_get(args, 'data_file'), data_index,
cond_get(args, 'label_index'),
cond_get(args, 'out_directory'),
cond_get(args, 'threads'),
cond_get(args, 'learning_split'),
cond_get(args, 'learning_iterations'),
cond_get(args, 'learning_C'),
cond_get(args, 'cv_iterations'),
cond_get(args, 'persistence_degree'),
cond_get(args, 'reevaluate'),
cond_get(args, 'data_type'),
cond_get(args,
'post_process'), post_process_arg,
cond_get(args, 'stop_after'),
cond_get(args, 'status'))
def fromJSONDict(cls, json):
return cls(cond_get(json, 'max_simplices'),
cond_get(json, 'persistence_epsilon'),
cond_get(json, 'segment_stride'),
cond_get(json,
'segment_size'), cond_get(json, 'window_size'),
cond_get(json, 'window_stride'),
cond_get(json, 'kernel_scale'),
cond_get(json, 'kernel_gamma'),
cond_get(json, 'invariant_epsilon'),
cond_get(json, 'data_file'), cond_get(json, 'data_index'),
cond_get(json, 'label_index'),
cond_get(json, 'out_directory'), cond_get(json, 'threads'),
cond_get(json, 'learning_split'),
cond_get(json, 'learning_iterations'),
cond_get(json, 'learning_C'),
cond_get(json, 'cv_iterations'),
cond_get(json, 'persistence_degree'),
cond_get(json, 'reevaluate'), cond_get(json, 'data_type'),
cond_get(json, 'post_process'),
cond_get(json, 'post_process_arg'),
cond_get(json, 'stop_after'), cond_get(json, 'status'))
def fromJSONDict(cls, json):
return cls(train=cond_get(json, 'train'),
test=cond_get(json, 'test'),
state=cond_get(json, 'state'))
def fromJSONDict(cls, json):
return cls(Configuration.fromJSONDict(json['config']),
cond_get_obj_list(json, 'results', LearningResult),
cond_get(json, 'kernel_files'))
def fromJSONDict(cls, json) :
return cls(cond_get(json, 'seed'),
json['train_labels'],
json['test_labels'],
json['test_results'],
cond_get(json, 'mkl_weights'))
def fromJSONDict(cls, json):
return cls(train = cond_get(json, 'train'),
test = cond_get(json, 'test'),
state = cond_get(json, 'state'))
def fromJSONDict(cls, json):
return cls(SegmentInfo.fromJSONDict(json['segment_info'])
if 'segment_info' in json else None,
points=cond_get(json, 'points'))