Update classes.py in knn

[JoyceYuen]

Hi,
I like your code. It's concise and efficient.
But when i read the recommenders part, that's the "class UserBasedRecommender(UserRecommender)", i found the code in the method named estimated_preference can not guarantee that one neighbor's preference will multiple the his similarity rather than others.

It is the previous code:
prefs = prefs[~np.isnan(prefs)]
similarities = similarities[~np.isnan(prefs)]

    prefs_sim = np.sum(prefs[~np.isnan(similarities)] *
                         similarities[~np.isnan(similarities)])
    total_similarity = np.sum(similarities)

I take a simple example:

import numpy as np
p = np.array([np.nan, 3,4,5,np.nan,5,6,np.nan,9,10])
p
array([ nan, 3., 4., 5., nan, 5., 6., nan, 9., 10.])
s = np.array([1,np.nan,4,6,np.nan,6,7,8,9,10])
s
array([ 1., nan, 4., 6., nan, 6., 7., 8., 9., 10.])
p = p[~np.isnan(p)]
p
array([ 3., 4., 5., 5., 6., 9., 10.])
s = s[~np.isnan(p)]
s
array([ 1., nan, 4., 6., nan, 6., 7.])
p[~np.isnan(s)]
array([ 3., 5., 5., 9., 10.])
s[~np.isnan(s)]
array([ 1., 4., 6., 6., 7.])
p[~np.isnan(s)]*s[~np.isnan(s)]
array([ 3., 20., 30., 54., 70.])

it follows the steps as the code. as you can see, it gets a wrong result.

my code is like this:
temp_prefs = [~np.isnan(prefs)]
temp_similarities = [~np.isnan(similarities)]
noNaN_indices = np.logical_and(temp_prefs, temp_similarities)

    prefs_sim = np.sum(prefs[noNaN_indices[0] == True] *
                         similarities[noNaN_indices[0] == True])

    similarities = similarities[~np.isnan(similarities)]
    total_similarity = np.sum(similarities)

with the same example:

pp = np.array([np.nan,3,4,5,np.nan,5,6,np.nan,9,10])
pp
array([ nan, 3., 4., 5., nan, 5., 6., nan, 9., 10.])
ss = np.array([1,np.nan,4,6,np.nan,6,7,8,9,10])
ss
array([ 1., nan, 4., 6., nan, 6., 7., 8., 9., 10.])
tss = [~np.isnan(ss)]
tss
[array([ True, False, True, True, False, True, True, True, True, True], dtype=bool)]
tpp = [~np.isnan(pp)]
tpp
[array([False, True, True, True, False, True, True, False, True, True], dtype=bool)]
nonNaN = np.logical_and(tss,tpp)
nonNaN
array([[False, False, True, True, False, True, True, False, True,
True]], dtype=bool)
ss[nonNaN[0] == True] * pp[nonNaN[0] == True]
array([ 16., 30., 30., 42., 81., 100.])

as you can see, it gets the right answer.

if i misunderstood, please let me know. Thank you in advance.

Best Wishes