![```python
import os
import re
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
import torch
from torch.utils.data import Dataset, DataLoader
from tqdm import tqdm
import joblib
import matplotlib.pyplot as plt
# =========================
# 配置
# =========================
class Config:
# ✅ 新版本：积水点Excel（新增累计降雨）
points_xlsx = r'F:\实验数据\CA_积水点融合\深度置信网络\样本输出_final\积水点_整合_step3_研究区内_新增累计降雨_719-723.xlsx'
# CA模拟结果 & 地理数据
ca_data_dir = r'F:\实验数据\CA_积水点融合\深度置信网络\CA模拟结果'
geo_data_dir = r'F:\实验数据\CA_积水点融合\深度置信网络\地理数据' # building_density.npy, DEM.npy, road_density.npy, Slope.npy, Landuse.npy
# 图像块 & 采样
patch_size = 64
ca_dry_thr_cm = 1.0 # 负样本水深阈值：< 1cm
tile_size = 200 # 负样本空间均匀采样瓦片大小（单位：栅格单元）
# 土地利用独热编码类型（可根据实际数据调整）
landuse_types = [7, 8, 10, 50, 60, 80, 100]
# 训练相关
output_dir = r'F:\实验数据\CA_积水点融合\深度置信网络\DBN训练输出_64patch_mu'
```](/_next/image?url=https%3A%2F%2Fpub-8c0ddfa5c0454d40822bc9944fe6f303.r2.dev%2Fai-drawings%2FBFJVaELXylgOOK0WatKO6tw6EfizLTx7%2F8a5d501e-e43d-4dfe-b168-5609ff4c3ef1%2F03d366b1-042e-4669-8cfb-aa5952cc4cb1.png&w=3840&q=75)
提示词描述
```python import os import re import numpy as np import pandas as pd from sklearn.model_selection import train_test_split from sklearn.preprocessing import MinMaxScaler import torch from torch.utils.data import Dataset, DataLoader from tqdm import tqdm import joblib import matplotlib.pyplot as plt # ========================= # 配置 # ========================= class Config: # ✅ 新版本：积水点Excel（新增累计降雨） points_xlsx = r'F:\实验数据\CA_积水点融合\深度置信网络\样本输出_final\积水点_整合_step3_研究区内_新增累计降雨_719-723.xlsx' # CA模拟结果 & 地理数据 ca_data_dir = r'F:\实验数据\CA_积水点融合\深度置信网络\CA模拟结果' geo_data_dir = r'F:\实验数据\CA_积水点融合\深度置信网络\地理数据' # building_density.npy, DEM.npy, road_density.npy, Slope.npy, Landuse.npy # 图像块 & 采样 patch_size = 64 ca_dry_thr_cm = 1.0 # 负样本水深阈值：< 1cm tile_size = 200 # 负样本空间均匀采样瓦片大小（单位：栅格单元） # 土地利用独热编码类型（可根据实际数据调整） landuse_types = [7, 8, 10, 50, 60, 80, 100] # 训练相关 output_dir = r'F:\实验数据\CA_积水点融合\深度置信网络\DBN训练输出_64patch_mu' ```
