--- a
+++ b/stay_admission/stay_pretraining.py
@@ -0,0 +1,49 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+from sklearn import preprocessing
+import numpy as np
+import model 
+import torch
+from torch import nn, optim
+from torch.utils.data import DataLoader
+import torch.nn.functional as F
+import sparse
+from tqdm import tqdm
+import pickle
+
+
+
+MODEL_PATH = "model/"
+device = torch.device("cuda:0")
+EPOCHS = 200
+
+BiAE = model.Bimodal_AE(seq_len = 48, n_features = 1318, ts_embedding_dim = 256, tb_embedding_dim = 256).to(device)
+criterion = torch.nn.MSELoss()
+optimizer = torch.optim.Adam(BiAE.parameters(),
+                              lr = 5e-4,
+                              weight_decay = 1e-8) 
+
+
+data = pickle.load(open("data/stay_pretrain_data.p", 'rb'))
+
+for epoch in tqdm(range(EPOCHS)):
+    
+    loss = 0
+ 
+    
+    data__ = DataLoader(data, batch_size = 128, shuffle = True)
+    for batch_idx, batch_data in enumerate(data__):
+        
+
+        X = batch_data[0].to(torch.float32).to(device)
+        S = batch_data[1].to(torch.float32).to(device)
+        optimizer.zero_grad()
+        outputs = BiAE(X,S).to(device)
+        train_loss = criterion(outputs, X)
+        train_loss.backward()
+        optimizer.step()
+        loss += train_loss.item()
+    print("Loss = ", loss)
+    torch.save(BiAE,MODEL_PATH + 'stay_pretrained.p')
+