![```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
# =========================
# Configuration
# =========================
class Config:
# ✅ New version: Water accumulation point Excel (added cumulative rainfall)
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 simulation results & geographic data
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
# Patch & Sampling
patch_size = 64
ca_dry_thr_cm = 1.0 # Negative sample water depth threshold: < 1cm
tile_size = 200 # Negative sample spatial uniform sampling tile size (unit: grid cell)
# Landuse one-hot encoding types (can be adjusted according to your actual data)
landuse_types = [7, 8, 10, 50, 60, 80, 100]
# Training related
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)
Prompt Description
```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 # ========================= # Configuration # ========================= class Config: # ✅ New version: Water accumulation point Excel (added cumulative rainfall) 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 simulation results & geographic data 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 # Patch & Sampling patch_size = 64 ca_dry_thr_cm = 1.0 # Negative sample water depth threshold: < 1cm tile_size = 200 # Negative sample spatial uniform sampling tile size (unit: grid cell) # Landuse one-hot encoding types (can be adjusted according to your actual data) landuse_types = [7, 8, 10, 50, 60, 80, 100] # Training related 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' ```
