Improving sliding window calculation

app.py

@@ -325,38 +325,39 @@ def diversity_inter(text):
 
 
 def sliding_window(text):
-    wind_preds = []
-    windows = []
-    new_values = []
-    heat_map = []
     words = word_tokenize(text)
+    improved_window = []
+    improved_wind_preds = []
     for idx, text in enumerate(words):
         if idx <= len(words) - 26:
             x = ' '.join(words[idx: idx + 25])
-            windows.append(x)
-
-    for text in windows:
-        prediction = -(predict(text).item() * 1.786 + 6.4) + 10
-        wind_preds.append(prediction)
-
-    size = 25
-    for i in wind_preds:
-        for j in range(size):
-            new_values.append(i)
-
-    heat_map = []
-    for idx, i in enumerate(new_values):
-        window = new_values[idx:idx + size]
-        heat_map.append(np.mean(window))
-    compressed_map = []
-    for idx, i in enumerate(heat_map):
-        if idx % size == 0:
-            window = heat_map[idx:idx + size]
-            compressed_map.append(np.mean(window))
-
-    inter_scores = compressed_map
+            throw_away = []
+            score = 0
+            for idx, i in enumerate(range(idx, idx + 25)):
+                if idx == 0:
+                    better_prediction = -(predict(x).item() * 1.786 + 6.4) + 10
+                    score = better_prediction
+                    throw_away.append((better_prediction, i))
+                else:
+                    throw_away.append((score, i))
+
+            improved_window.append(throw_away)
+    average_scores = {k: 0 for k in range(len(words) - 1)}
+    total_windows = {k: 0 for k in range(len(words) - 1)}
+    for idx, i in enumerate(improved_window):
+        for score, idx in i:
+            average_scores[idx] += score
+            total_windows[idx] += 1
+
+    for k, v in total_windows.items():
+        if v != 0:
+            average_scores[k] /= v
+
+    inter_scores = [v for v in average_scores.values()]
+    copy_list = inter_scores.copy()
+    print(inter_scores)
     while len(inter_scores) <= len(words) - 1:
-        inter_scores.append(compressed_map[-1])
+        inter_scores.append(copy_list[-1])
 
     x = list(range(len(inter_scores)))
     y = inter_scores