fix clustering ranks when all dists are equal

quail/analysis/clustering.py

@@ -72,7 +72,11 @@ def _get_weight_exact(egg, feature, distdict, permute, n_perms):
             dists = distmat[pres.index(c),:]
             di = dists[pres.index(n)]
             dists_filt = np.array([dist for idx, dist in enumerate(dists) if idx not in past_idxs])
-            ranks.append(np.mean(np.where(np.sort(dists_filt)[::-1] == di)[0]+1) / len(dists_filt))
+
+            if len(np.unique(dists_filt)) == 1:
+                ranks.append(0.5)
+            else:
+                ranks.append(np.mean(np.where(np.sort(dists_filt)[::-1] == di)[0]+1) / len(dists_filt))
             past_idxs.append(pres.index(c))
             past_words.append(c)
     return np.nanmean(ranks)
@@ -97,7 +101,10 @@ def _get_weight_best(egg, feature, distdict, permute, n_perms, distance):
         dists = distmat[cdx, :]
         di = dists[ndx]
         dists_filt = np.array([dist for idx, dist in enumerate(dists)])
-        ranks.append(np.mean(np.where(np.sort(dists_filt)[::-1] == di)[0]+1) / len(dists_filt))
+        if len(np.unique(dists_filt)) == 1:
+            ranks.append(0.5)
+        else:
+            ranks.append(np.mean(np.where(np.sort(dists_filt)[::-1] == di)[0] + 1) / len(dists_filt))
     return np.nanmean(ranks)
 
 def _get_weight_smooth(egg, feature, distdict, permute, n_perms, distance):
@@ -120,7 +127,10 @@ def _get_weight_smooth(egg, feature, distdict, permute, n_perms, distance):
         dists = distmat[cdx, :]
         di = dists[ndx]
         dists_filt = np.array([dist for idx, dist in enumerate(dists)])
-        ranks.append(np.mean(np.where(np.sort(dists_filt)[::-1] == di)[0]+1) / len(dists_filt))
+        if len(np.unique(dists_filt)) == 1:
+            ranks.append(0.5)
+        else:
+            ranks.append(np.mean(np.where(np.sort(dists_filt)[::-1] == di)[0] + 1) / len(dists_filt))
     return np.nanmean(ranks)
 
 def get_distmat(egg, feature, distdict):