def set_model(sem_input_file, sem_name, sem_input_macro = True, semantics_name='TCG_semantics_main',
grammar_name='TCG_grammar_VB_main', model_params = {}):
"""
Sets up a TCG production model.
Args:
- sem_input_file (STR): Semantic input file name
- sem_name (STR): Semantic input name
- sem_input_macro (BOOL): True is the input is an ISRF macro
- semantics_name (STR): Name of the semantic file containing the perceptual, world, and conceptualization knowledge.
- grammar_name (STR): Name of the grammar file to use.
- model_prams (dict): Dictionary defining the model parameters (if different than default)
Returns: (model, semantic input genereator)
"""
SEM_INPUT_PATH = './data/sem_inputs/'
model = TCG_production_system(grammar_name=grammar_name, semantics_name=semantics_name)
if model_params:
model.update_params(model_params)
# Set up semantic input generator
conceptLTM = model.schemas['Concept_LTM']
if not(sem_input_macro):
sem_inputs = TCG_LOADER.load_sem_input(sem_input_file, SEM_INPUT_PATH)
sem_input = {sem_name:sem_inputs[sem_name]}
sem_gen = ls.SEM_GENERATOR(sem_input, conceptLTM, speed_param=1)
if sem_input_macro:
sem_inputs = TCG_LOADER.load_sem_macro(sem_name, sem_input_file, SEM_INPUT_PATH)
sem_gen = ls.SEM_GENERATOR(sem_inputs, conceptLTM, speed_param=1)
return (model, sem_gen)
def set_inputs(model, input_name, sem_input_file='diagnostic.json', sem_input_macro=False, speed_param=10):
"""
Sets up a TCG ISRF inputs generator for TCG production model.
Args:
- model (): model to which the inputs will be sent
- input_name (STR): name of the input to be used.
- sem_input_file (STR): Semantic input file name. For non-macro input, set to 'ALL' to load all inputs from file.
- sem_input_macro (BOOL): True is the input is an ISRF macro.
- speed_param (INT): multiplier of the rate defined in the ISRF input (by default the ISFR rate is 1.)
Returns:
- input SEM_GENERATOR object.
"""
SEM_INPUT_PATH = './data/sem_inputs/'
conceptLTM = model.schemas['Concept_LTM']
if not(sem_input_macro):
sem_inputs = TCG_LOADER.load_sem_input(sem_input_file, SEM_INPUT_PATH)
if input_name == 'ALL':
sem_gen = ls.SEM_GENERATOR(sem_inputs, conceptLTM, speed_param=speed_param, is_macro=sem_input_macro)
sem_gen.ground_truths = TCG_LOADER.load_ground_truths(sem_input_file, SEM_INPUT_PATH)
else:
sem_input = {input_name:sem_inputs[input_name]}
sem_gen = ls.SEM_GENERATOR(sem_input, conceptLTM, speed_param=speed_param,is_macro=sem_input_macro)
ground_truths = TCG_LOADER.load_ground_truths(sem_input_file, SEM_INPUT_PATH)
sem_gen.ground_truths = ground_truths.get(input_name, None)
if sem_input_macro:
sem_inputs = TCG_LOADER.load_sem_macro(input_name, sem_input_file, SEM_INPUT_PATH)
sem_gen = ls.SEM_GENERATOR(sem_inputs, conceptLTM, speed_param=speed_param, is_macro=sem_input_macro)
ground_truths = TCG_LOADER.load_ground_truths(sem_input_file, SEM_INPUT_PATH)
sem_gen.ground_truths = ground_truths.get(input_name, None)
return sem_gen