SER (Small Efficient Retrieval) init commit

ser.py

@@ -0,0 +1,67 @@
+class SearchMood:
+    def __init__(self, mood_prompt, prior_init):
+        self.prior_init = prior_init
+        self.mood_prompt = mood_prompt
+        self.model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+        self.embeding = lambda mood_prompt, mood_state: (self.model.encode(mood_prompt, convert_to_tensor=True), self.model.encode(mood_state, convert_to_tensor=True))
+        self.similar = lambda similarx, similary: util.pytorch_cos_sim(similarx, similary)
+        self.cx_sample = shelve.open('cx_sample.db')['sample']
+        self.database = shelve.open('database.db')
+        SearchMood.prior_component = torch.tensor([0.,1.])
+        self.prior_sample = torch.normal(self.prior_component[0], self.prior_component[1], size=(5,))
+        self.sample_losses = None
+        
+        
+
+
+
+    def embedings(self, samplex, sampley):
+        emb = self.embeding(samplex, sampley)
+        similarity = self.similar(emb[0], emb[1])
+        return(similarity)
+
+
+
+    def mood_dist(self, data_sample=False, mood_prompt=False, search=True):
+        cx_index = []
+        if search == True:
+           for mood_state in self.cx_sample:
+               index_sample = []
+               max_sample  = 0
+               index_sample = 0
+               for index, mood_prompts in enumerate(self.database['database']):
+                   simemb  = self.embedings(mood_state, mood_prompts)
+                   if max_sample < simemb:
+                       max_sample = simemb
+                       index_sample = index
+                      
+                   
+               cx_index.append((float(index_sample)))
+
+
+        else:
+            cx_index.append(self.embedings(mood_prompt, data_sample))
+
+        
+        
+        return(torch.tensor(cx_index))
+
+
+
+    def loss_fn(self):
+        
+        for sample in self.prior_sample:
+            sample = sample.item()
+            data_sample = self.database['database'][round(sample)]
+            samp_loss = self.mood_dist(data_sample, self.mood_prompt, search=False)
+            print(samp_loss)
+            if samp_loss.item() >= 1.:
+                print('test')
+                break
+
+
+
+        return(torch.tensor([samp_loss*-1]))
+ 
+
+