interpret difference is dist as odds

app.py

@@ -15,8 +15,8 @@ from numpy.random import RandomState, SeedSequence
 clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") 
 clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
 
-HYPERDIMS = 2048
-VALUE_BITS = 10
+HYPERDIMS = 1024
+VALUE_BITS = 8
 POS_BITS = 9 # CLIP features are 512 dims
 val_bins = np.linspace(start=-1., stop=1., num=2**VALUE_BITS)
 print(val_bins.shape, val_bins.min(), val_bins.max(), 'val bins')
@@ -187,11 +187,14 @@ def predict(embeds, exemplars, lut):
     dist_to_ex0 = np.abs(hd_embeds - exemplars[0][np.newaxis, ...]).sum(axis=-1)
     dist_to_ex1 = np.abs(hd_embeds - exemplars[1][np.newaxis, ...]).sum(axis=-1)
     print('dists', dist_to_ex0, dist_to_ex1)
-    preds = np.array([1. - ( dist_to_ex0 / HYPERDIMS ), 1 - ( dist_to_ex1 / HYPERDIMS )])
+    odds = abs(dist_to_ex0 - dist_to_ex1)
+    if dist_to_ex1 < dist_to_ex0:
+        preds = [1., odds]
+    else:
+        preds = [odds, 1.]
     print(preds)
     # preds = np.array([-1. * dist_to_ex0, -1. * dist_to_ex1])
-    preds = np.exp(preds)/sum(np.exp(preds))
-    # preds = preds / preds.sum()
+    preds = preds / preds.sum()
     # print(preds.shape)
     print(preds)
     return {"👍": preds[1], "👎": preds[0]}