def run(self):
# Register the structure in the database, so it is stored and use in the future.
if not __database__.has_structure(Group_of_Markov_Models_1().get_name()):
print_info("The structure is not registered.")
__database__.set_new_structure(Group_of_Markov_Models_1())
else:
main_dict = __database__.get_new_structure(Group_of_Markov_Models_1())
self.set_main_dict(main_dict)
# List general help. Don't modify.
def help():
self.log("info", self.description)
# Run
super(Group_of_Markov_Models_1, self).run()
if self.args is None:
return
# Process the command line
if self.args.list:
self.list_markov_models(self.args.filter)
elif self.args.generate:
try:
self.create_new_model(self.args.generate, self.args.numberofflows)
except AttributeError:
numberofflows = 3
self.create_new_model(self.args.generate, numberofflows)
elif self.args.printmatrix:
self.print_matrix(self.args.printmatrix)
elif self.args.simulate:
self.simulate(self.args.simulate)
elif self.args.delete:
self.delete(self.args.delete)
elif self.args.printstate:
self.printstate(self.args.printstate)
elif self.args.regenerate:
self.regenerate(self.args.regenerate)
elif self.args.train:
if "-" in self.args.train:
# There is a range of ids to train
first = int(self.args.train.split("-")[0])
last = int(self.args.train.split("-")[1])
train_ids = range(first, last + 1)
else:
train_ids = [self.args.train]
for train_id in train_ids:
self.train(int(train_id), self.args.filter, self.args.train_ids, self.args.verbose)
elif self.args.generateall:
try:
self.create_new_model(self.args.generate, self.args.numberofflows)
except AttributeError:
numberofflows = 3
self.generate_all_models(numberofflows)
elif self.args.export and self.args.exportpath:
self.export_model(self.args.export, self.args.exportpath)
elif self.args.threshold:
if not self.args.train_ids:
print_error("You must specify some markov model id to apply the threshold.")
return False
self.assign_threshold_to_id(self.args.train_ids, self.args.threshold)
def run(self):
######### Mandatory part! don't delete ########################
# Register the structure in the database, so it is stored and use in the future.
if not __database__.has_structure(Visualizations().get_name()):
print_info('The structure is not registered.')
__database__.set_new_structure(Visualizations())
else:
main_dict = __database__.get_new_structure(Visualizations())
self.set_main_dict(main_dict)
# List general help. Don't modify.
def help():
self.log('info', self.description)
# Run
super(Visualizations, self).run()
if self.args is None:
return
######### End Mandatory part! ########################
# Process the command line and call the methods. Here add your own parameters
if self.args.visualize:
self.visualize_dataset(int(self.args.visualize), self.args.multiplier, self.args.filter)
else:
print_error('At least one of the parameter is required in this module')
self.usage()
def run(self):
######### Mandatory part! don't delete ########################
# Register the structure in the database, so it is stored and use in the future.
if not __database__.has_structure(
Group_of_Template_Objects().get_name()):
print_info('The structure is not registered.')
__database__.set_new_structure(Group_of_Template_Objects())
else:
main_dict = __database__.get_new_structure(
Group_of_Template_Objects())
self.set_main_dict(main_dict)
# List general help. Don't modify.
def help():
self.log('info', self.description)
# Run
super(Group_of_Template_Objects, self).run()
if self.args is None:
return
######### End Mandatory part! ########################
# Process the command line and call the methods. Here add your own parameters
if self.args.list:
self.list_objects()
elif self.args.generate:
self.create_new_object(self.args.generate)
else:
print_error('At least one parameter is required in this module')
self.usage()
def run(self):
######### Mandatory part! don't delete ########################
# Register the structure in the database, so it is stored and use in the future.
if not __database__.has_structure(Group_of_Template_Objects().get_name()):
print_info('The structure is not registered.')
__database__.set_new_structure(Group_of_Template_Objects())
else:
main_dict = __database__.get_new_structure(Group_of_Template_Objects())
self.set_main_dict(main_dict)
# List general help. Don't modify.
def help():
self.log('info', self.description)
# Run
super(Group_of_Template_Objects, self).run()
if self.args is None:
return
######### End Mandatory part! ########################
# Process the command line and call the methods. Here add your own parameters
if self.args.list:
self.list_objects()
elif self.args.generate:
self.create_new_object(self.args.generate)
else:
print_error('At least one parameter is required in this module')
self.usage()
def run(self):
######### Mandatory part! don't delete ########################
# Register the structure in the database, so it is stored and use in the future.
if not __database__.has_structure(Visualizations().get_name()):
print_info('The structure is not registered.')
__database__.set_new_structure(Visualizations())
else:
main_dict = __database__.get_new_structure(Visualizations())
self.set_main_dict(main_dict)
# List general help. Don't modify.
def help():
self.log('info', self.description)
# Run
super(Visualizations, self).run()
if self.args is None:
return
######### End Mandatory part! ########################
# Process the command line and call the methods. Here add your own parameters
if self.args.visualize:
self.visualize_dataset(int(self.args.visualize), self.args.multiplier, self.args.filter)
else:
print_error('At least one of the parameter is required in this module')
self.usage()
def run(self):
# Register the structure in the database, so it is stored and use in the future.
if not __database__.has_structure(Group_of_Markov_Models_1().get_name()):
print_info('The structure is not registered.')
__database__.set_new_structure(Group_of_Markov_Models_1())
else:
main_dict = __database__.get_new_structure(Group_of_Markov_Models_1())
self.set_main_dict(main_dict)
# List general help. Don't modify.
def help():
self.log('info', self.description)
# Run
super(Group_of_Markov_Models_1, self).run()
if self.args is None:
return
# Process the command line
if self.args.list:
self.list_markov_models(self.args.filter)
elif self.args.generate:
try:
self.create_new_model(self.args.generate, self.args.numberofflows)
except AttributeError:
numberofflows = 3
self.create_new_model(self.args.generate, numberofflows)
elif self.args.printmatrix:
self.print_matrix(self.args.printmatrix)
elif self.args.simulate:
self.simulate(self.args.simulate)
elif self.args.delete:
self.delete(self.args.delete)
elif self.args.printstate:
self.printstate(self.args.printstate)
elif self.args.regenerate:
self.regenerate(self.args.regenerate)
elif self.args.train:
if '-' in self.args.train:
# There is a range of ids to train
first = int(self.args.train.split('-')[0])
last = int(self.args.train.split('-')[1])
train_ids = range(first,last + 1)
else:
train_ids = [self.args.train]
for train_id in train_ids:
self.train(int(train_id), self.args.filter, self.args.train_ids, self.args.verbose)
elif self.args.generateall:
try:
self.create_new_model(self.args.generate, self.args.numberofflows)
except AttributeError:
numberofflows = 3
self.generate_all_models(numberofflows)
elif self.args.export and self.args.exportpath:
self.export_model(self.args.export, self.args.exportpath)
elif self.args.threshold:
if not self.args.train_ids:
print_error('You must specify some markov model id to apply the threshold.')
return False
self.assign_threshold_to_id(self.args.train_ids, self.args.threshold)
def run(self):
######### Mandatory part! don't delete ########################
# Register the structure in the database, so it is stored and use in the future.
if not __database__.has_structure(Group_of_Detections().get_name()):
print_info('The structure is not registered.')
__database__.set_new_structure(Group_of_Detections())
else:
main_dict = __database__.get_new_structure(Group_of_Detections())
self.set_main_dict(main_dict)
# List general help. Don't modify.
def help():
self.log('info', self.description)
# Run
super(Group_of_Detections, self).run()
if self.args is None:
return
######### End Mandatory part! ########################
# Process the command line and call the methods. Here add your own parameters
if self.args.list:
self.list_distances(self.args.filter)
elif self.args.new:
self.create_new_distance(self.args.amount, self.args.trainid, self.args.testid, self.args.verbose)
elif self.args.delete:
self.delete_distance(self.args.delete)
elif self.args.letterbyletter:
self.detect_letter_by_letter(self.args.letterbyletter, self.args.amount, self.args.verbose)
elif self.args.regenerate:
self.regenerate(self.args.regenerate, self.args.filter)
elif self.args.print_comparison:
self.print_comparison(self.args.print_comparison)
elif self.args.compareall:
self.compare_all(self.args.compareall, self.args.amount, self.args.verbose)
elif self.args.deleteall:
if self.args.filter:
self.delete_all(self.args.filter)
else:
print_error('Must provide a filter with -f')
else:
print_error('At least one of the parameter is required in this module')
self.usage()
def run(self):
# Register the structure in the database, so it is stored and use in the future.
if not __database__.has_structure(Group_of_Markov_Models_2().get_name()):
print_info('The structure is not registered.')
__database__.set_new_structure(Group_of_Markov_Models_2())
else:
main_dict = __database__.get_new_structure(Group_of_Markov_Models_2())
self.set_main_dict(main_dict)
# List general help. Don't modify.
def help():
self.log('info', self.description)
# Run
super(Group_of_Markov_Models_2, self).run()
if self.args is None:
return
# Process the command line
if self.args.list:
self.list_markov_models(self.args.filter)
elif self.args.generate:
self.create_new_model(self.args.generate)
elif self.args.printmatrix:
self.print_matrix(self.args.printmatrix)
elif self.args.simulate:
self.simulate(self.args.simulate)
elif self.args.delete:
self.delete(self.args.delete)
elif self.args.printstate:
self.printstate(self.args.printstate)
elif self.args.regenerate:
self.regenerate(self.args.regenerate)
elif self.args.generateall:
self.generate_all_models()
else:
print_error('At least one of the parameter is required in this module')
self.usage()
