It contains a serious bug.

[rabintang]

In knn/classes.py UserBasedRecommender.estimate_preference, the following codes

1. prefs = prefs[~np.isnan(prefs)] # prefs = [1, 2, 4]
2. similarities = similarities[~np.isnan(prefs)] # similarities = [0.1, 0.2, 0.3], but actually it show be [0.1, 0.2, 0.4]
3. prefs_sim = np.sum(prefs[~np.isnan(similarities)] * similarities[~np.isnan(similarities)])
   have a serious bug, assuming that initial prefs = [1, 2, non, 4], similarities = [0.1, 0.2, 0.3, 0.4].

[tyutjxs]

do you have resolved it?