diff options
| -rw-r--r-- | bin/Proof_Of_Concept_PELT.py | 64 |
1 files changed, 32 insertions, 32 deletions
diff --git a/bin/Proof_Of_Concept_PELT.py b/bin/Proof_Of_Concept_PELT.py index ac32d88..cba7009 100644 --- a/bin/Proof_Of_Concept_PELT.py +++ b/bin/Proof_Of_Concept_PELT.py @@ -547,7 +547,7 @@ if __name__ == '__main__': except getopt.GetoptError as err: print(err, file=sys.stderr) sys.exit(-1) - + filepath = os.path.dirname(opt_filename) # OPENING DATA if ".json" in opt_filename: # open file with trace data from json @@ -1034,37 +1034,37 @@ if __name__ == '__main__': print_warning("Fitting(duration) for state " + state_name + " was not succesful!") new_fit_res_pow_dict[state_name] = combined_fit_power new_fit_res_dur_dict[state_name] = combined_fit_duration - for state_name in new_by_name.keys(): - model_function = str(new_fit_res_pow_dict[state_name].model_function) - model_args = new_fit_res_pow_dict[state_name].model_args - for num_arg, arg in enumerate(model_args): - replace_string = "regression_arg(" + str(num_arg) + ")" - model_function = model_function.replace(replace_string, str(arg)) - print("Power-Function for state " + state_name + ": " - + model_function) - for state_name in new_by_name.keys(): - model_function = str(new_fit_res_dur_dict[state_name].model_function) - model_args = new_fit_res_dur_dict[state_name].model_args - for num_arg, arg in enumerate(model_args): - replace_string = "regression_arg(" + str(num_arg) + ")" - model_function = model_function.replace(replace_string, str(arg)) - print("Duration-Function for state " + state_name + ": " - + model_function) - # model = PTAModel(new_by_name, param_names, dict()) - # model_json = model.to_json() - # param_model, _ = model.get_fitted() - # param_quality = model.assess(param_model) - # pprint.pprint(param_quality) - # # model = PTAModel(by_name, ...) - # # validator = CrossValidator(PTAModel, by_name, ...) - # # param_quality = validator.kfold(lambda m: m.get_fitted()[0], 10) - # validator = CrossValidator(PTAModel, new_by_name, param_names, dict()) - # param_quality = validator.kfold(lambda m: m.get_fitted()[0], 10) - # pprint.pprint(param_quality) - if not_accurate: - print_warning( - "THIS RESULT IS NOT ACCURATE. SEE WARNINGLOG TO GET A BETTER UNDERSTANDING" - " WHY.") + result_loc = os.path.join(filepath, "result.txt") + with open(result_loc, "w") as f: + f.write("Resulting Sequence: " + str(resulting_sequence)) + f.write("\n\n") + for state_name in new_by_name.keys(): + model_function = str(new_fit_res_pow_dict[state_name].model_function) + model_args = new_fit_res_pow_dict[state_name].model_args + for num_arg, arg in enumerate(model_args): + replace_string = "regression_arg(" + str(num_arg) + ")" + model_function = model_function.replace(replace_string, str(arg)) + print("Power-Function for state " + state_name + ": " + + model_function) + f.write("Power-Function for state " + state_name + ": " + + model_function + "\n") + f.write("\n\n") + for state_name in new_by_name.keys(): + model_function = str(new_fit_res_dur_dict[state_name].model_function) + model_args = new_fit_res_dur_dict[state_name].model_args + for num_arg, arg in enumerate(model_args): + replace_string = "regression_arg(" + str(num_arg) + ")" + model_function = model_function.replace(replace_string, str(arg)) + print("Duration-Function for state " + state_name + ": " + + model_function) + f.write("Duration-Function for state " + state_name + ": " + + model_function + "\n") + if not_accurate: + print_warning( + "THIS RESULT IS NOT ACCURATE. SEE WARNINGLOG TO GET A BETTER UNDERSTANDING" + " WHY.") + f.write("THIS RESULT IS NOT ACCURATE. SEE WARNINGLOG TO GET A BETTER UNDERSTANDING" + " WHY.") # TODO: removed clustering (temporarily), since it provided too much dificultys |