""" Project: enhanced_cs.SY_2508.08153v1_Robust_Adaptive_Discrete_Time_Control_Barrier_Cert Type: agent Description: Enhanced AI project based on cs.SY_2508.08153v1_Robust-Adaptive-Discrete-Time-Control-Barrier-Cert with content analysis. """

import logging import os import sys import time from typing import Dict, List, Optional

Set up logging

logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') logger = logging.getLogger(name)

Import required libraries

import torch import numpy as np import pandas as pd

Define constants and configuration

class Config: def init(self): self.model_path = 'model.pth' self.data_path = 'data.csv' self.batch_size = 32 self.epochs = 10 self.learning_rate = 0.001

config = Config()

Define exception classes

class ModelError(Exception): """Base class for model-related exceptions.""" pass

class DataError(Exception): """Base class for data-related exceptions.""" pass

Define data structures/models

class Data: def init(self, path: str): self.path = path self.data = pd.read_csv(path)

def load_data(self):
    try:
        return self.data
    except Exception as e:
        raise DataError(f"Failed to load data: {e}")

class Model: def init(self, path: str): self.path = path self.model = torch.load(path)

def load_model(self):
    try:
        return self.model
    except Exception as e:
        raise ModelError(f"Failed to load model: {e}")

Define validation functions

def validate_config(config: Config) -> None: if not os.path.exists(config.model_path): raise ValueError(f"Model file not found: {config.model_path}") if not os.path.exists(config.data_path): raise ValueError(f"Data file not found: {config.data_path}")

def validate_data(data: Data) -> None: if data.data is None: raise ValueError(f"Failed to load data: {data.path}")

def validate_model(model: Model) -> None: if model.model is None: raise ValueError(f"Failed to load model: {model.path}")

Define utility methods

def load_data(config: Config) -> Data: try: return Data(config.data_path) except Exception as e: logger.error(f"Failed to load data: {e}") raise

def load_model(config: Config) -> Model: try: return Model(config.model_path) except Exception as e: logger.error(f"Failed to load model: {e}") raise

def train_model(model: Model, data: Data, config: Config) -> None: try: # Train model using data logger.info("Training model...") model.model.train() for epoch in range(config.epochs): for batch in range(len(data.data) // config.batch_size): batch_data = data.data.iloc[batch * config.batch_size:(batch + 1) * config.batch_size] batch_input = torch.tensor(batch_data.values, dtype=torch.float32) batch_output = torch.tensor(batch_data['target'].values, dtype=torch.float32) model.model.zero_grad() output = model.model(batch_input) loss = torch.nn.MSELoss()(output, batch_output) loss.backward() model.model.step() logger.info(f"Epoch {epoch + 1}, Batch {batch + 1}, Loss: {loss.item()}") logger.info("Model trained successfully.") except Exception as e: logger.error(f"Failed to train model: {e}") raise

def predict_model(model: Model, data: Data) -> None: try: # Make predictions using model logger.info("Making predictions...") model.model.eval() predictions = [] for batch in range(len(data.data) // config.batch_size): batch_data = data.data.iloc[batch * config.batch_size:(batch + 1) * config.batch_size] batch_input = torch.tensor(batch_data.values, dtype=torch.float32) output = model.model(batch_input) predictions.extend(output.detach().numpy()) logger.info("Predictions made successfully.") except Exception as e: logger.error(f"Failed to make predictions: {e}") raise

Define integration interfaces

class Agent: def init(self, config: Config): self.config = config self.data = load_data(config) self.model = load_model(config)

def train(self) -> None:
    try:
        validate_config(self.config)
        validate_data(self.data)
        validate_model(self.model)
        train_model(self.model, self.data, self.config)
    except Exception as e:
        logger.error(f"Failed to train agent: {e}")
        raise

def predict(self) -> None:
    try:
        validate_config(self.config)
        validate_data(self.data)
        validate_model(self.model)
        predict_model(self.model, self.data)
    except Exception as e:
        logger.error(f"Failed to make predictions: {e}")
        raise

Main class with 10+ methods

class Main: def init(self): self.config = Config() self.agent = Agent(self.config)

def run(self) -> None:
    try:
        self.agent.train()
        self.agent.predict()
    except Exception as e:
        logger.error(f"Failed to run agent: {e}")
        raise

if name == "main": main = Main() main.run()