init(model_path: str)
- Initialize the adaptive model with a TFLite model path.
predict_next_task(task_sequence: list, sensor_data: list)
- Predict the next task based on the current task and sensor data.
save_interpretation(interpretation: dict, output_path: str = "interpretation.json")
- Save the interpretation result to a JSON file.
init(tasks_file: str, output_model_path: str)
- Initialize the trainer with tasks and an output model path.
train_and_save_model(epochs: int, batch_size: int)
- Train the model and save it as a TFLite file.
set_max_length(max_length: int)
- Set the maximum sequence length for task encoding and padding.
init(model_path: str)
• Initialize the interpreter with the path to a TFLite model.
interpret(task_sequence: list, sensor_data: list, max_length: int = 5)
• Perform interpretation of the model based on task sequence and sensor data. Arguments: • task_sequence: List of tasks encoded as integers.
• sensor_data: List of sensor data values.
• max_length: Maximum length of the task sequence (default: 5).
Returns:*
• dict: Contains the predicted task index and output scores.
save_interpretation(interpretation: dict, output_path: str = "interpretation.json")
• Save the interpretation result to a JSON file.
Arguments:
• interpretation: A dictionary containing the interpretation results.
• output_path: Path to save the JSON file (default: "interpretation.json").