请给出一个整体的代码

当然可以，这里是将上述优化空调布局的代码整合成一个整体的示例，包括导入库、定义模拟函数、优化器选择以及结果分析：

import numpy as np
import matplotlib.pyplot as plt
from skopt import gp_minimize, Optimizer
from sklearn.model_selection import cross_val_score

# 定义辅助函数
def evaluate_objective(params, simulate_function, target_temps, cv=5):
    summer_temp = simulate_function(params, 'summer')
    winter_temp = simulate_function(params, 'winter')
    summer_deviation = np.mean((summer_temp - target_temps['summer']) ** 2)
    winter_deviation = np.mean((winter_temp - target_temps['winter']) ** 2)
    return sum(deviation for deviation in [summer_deviation, winter_deviation])

# 假设房间尺寸
room_length = 5
room_width = 8
room_height = 3

# 初始化其他参数
initial_inlet_positions = np.array([(1, 1, 1), (4, 7, 1)])
initial_outlet_positions = np.array([(4, 1, 1), (1, 7, 1)])
initial_directions = np.array([(0, 0, -1), (0, 0, -1)])
initial_angles = np.array([0, 0])
initial_velocities = np.array([2, 2])
initial_flow_rates = np.array([1000, 1000])
target_temps = {'summer': 25, 'winter': 20}

# 转换为Skopt接受的维度
dimensions = [
    ('inlet_pos_{}'.format(i), 'real', (0, room_length * room_width * 3)) for i in range(len(initial_inlet_positions))
] + [
    ('outlet_pos_{}'.format(i), 'real', (0, room_length * room_width * 3)) for i in range(len(initial_outlet_positions))
] + [
    ('direction_{}'.format(i), 'real', (-np.pi, np.pi)) for i in range(len(initial_directions))
] + [('angle_{}'.format(i), 'real', (-np.pi / 180, np.pi / 180)) for i in range(len(initial_angles))]
dimensions += [('velocity_{}'.format(i), 'real', (0, 10)) for i in range(len(initial_velocities))]
dimensions += [('flow_rate_{}'.format(i), 'real', (0, 5000)) for i in range(len(initial_flow_rates))]

# 创建优化器
optimizer = Optimizer(dimensions, "GP", base_estimator="RF")

# 初始参数
initial_params = np.concatenate([
    initial_inlet_positions.flatten(), initial_outlet_positions.flatten(),
    initial_directions.flatten(), initial_angles, initial_velocities, initial_flow_rates
])

# 开始优化
for _ in range(100):  # 更改循环次数以适应实际需求
    res = optimizer.ask()
    x = res.reshape(optimizer.space.n_dims, -1)
    objective_value = evaluate_objective(x, simulate_temperature_distribution, target_temps)
    optimizer.tell(res, objective_value)

# 获取最佳参数
best_params = optimizer.X[np.argmin(optimizer.Y)]

# 反馈优化结果
print(f"最优参数：{best_params}")
print("优化后的进风口位置：")
print(best_params[:len(initial_inlet_positions)*3].reshape(len(initial_inlet_positions), 3))
print("优化后的出风口位置：")
print(best_params[len(initial_inlet_positions)*3:2*len(initial_inlet_positions)*3].reshape(len(initial_outlet_positions), 3))
print("优化后的进风口方向：")
print(best_params[2*len(initial_inlet_positions)*3:3*len(initial_inlet_positions)*3].reshape(len(initial_directions), 3))
print("优化后的进风口角度：")
print(best_params[3*len(initial_inlet_positions)*3:].reshape(len(initial_angles),))
print("优化后的进风口风速：")
print(best_params[3*len(initial_inlet_positions)*3+len(initial_angles):3*len(initial_inlet_positions)*3+2*len(initial_angles)])
print("优化后的进风口风量：")
print(best_params[3*len(initial_inlet_positions)*3+2*len(initial_angles):])

# 结合交叉验证评估性能
scores = cross_val_score(evaluate_objective, best_params, None, cv=cv, scoring="neg_mean_squared_error")
print(f"交叉验证得分：{scores.mean()} ± {scores.std()}")

请注意，这里的代码没有包含真实的热力学模拟部分，因为那会涉及到详细的物理计算或数值求解。在这个简化版本中，我们使用了一个简单的评估函数来表示理想状态下的温度偏差。实际应用中，您需要替换`simulate_temperature_distribution`函数以完成物理模型的模拟。同时，为了更好地评估优化效果，您可以考虑添加更多的评价指标或使用其他的优化方法。