Update app.py

Neuron training. You now need a train.py and a dataset.

app.py

@@ -27,18 +27,34 @@ class Neuron:
         self.output = output
         self.inp = inp
     def train(self, inpr):
-        cid = random.randint(0, len(inpr)-1)
-        c2id = random.randint(0, len(inpr[cid])-1)
-        if inpr[cid][c2id] not in self.inp:
-            self.inp.append(inpr[cid][c2id])
-        cid = random.randint(0, len(self.inp)-1)
-        if not check(self.inp[cid], inpr):
-            del self.inp[cid]
+        lir = len(inpr)
+        lirm = lir-1
+        linp = len(inp)
+        linpm = linp-1
+        cid = random.randint(0, lirm)
+        if cid < linpm:
+            if random.choice([True, True, False]):
+                del self.inp[linpm]
+            else:
+                self.inp[cid] = inpr[cid]
+        elif cid == linpm:
+            self.inp[cid] = inpr[cid]
+        else:
+            self.inp.append(inpr[cid])
+#        cid = random.randint(0, len(inpr)-1)
+#        c2id = random.randint(0, len(inpr[cid])-1)
+#        if inpr[cid][c2id] not in self.inp:
+#            self.inp.append(inpr[cid][c2id])
+#        cid = random.randint(0, len(self.inp)-1)
+#        if not check(self.inp[cid], inpr):
+#            del self.inp[cid]
 
 def check(word, list2d):
     for i in list2d:
         if word in i: return True
-    
+
+outs1 = []
+
 # Work
 def get_matches(text):
     # prepare text