Building Machine Learning Systems with Python - Richert, Coelho

We are now going to use this binary matrix to make predictions of movie ratings.
The general algorithm will be (in pseudocode) as follows:
Chapter 8
1. For each user, rank every other user in terms of closeness. For this step,
we will use the binary matrix and use correlation as the measure of
closeness (interpreting the binary matrix as zeros and ones allows us
to perform this computation).
2. When we need to estimate a rating for a user-movie pair, we look at the
neighbors of the user sequentially (as defined in step 1). When we first find
a rating for the movie in question, we report it.
Implementing the code first, we are going to write a simple NumPy function.
NumPy ships with np.corrcoeff, which computes correlations. This is a very
generic function and computes n-dimensional correlations even when only a single,
traditional correlation is needed. Therefore, to compute the correlation between two
users, we need to call the following:
corr_between_user1_and_user2 = np.corrcoef(user1, user2)[0,1]
In fact, we will be wishing to compute the correlation between a user and all the
other users. This will be an operation we will use a few times, so we wrap it in
a function named all_correlations:
import numpy as np
def all_correlations(bait, target):
'''
corrs = all_correlations(bait, target)
corrs[i] is the correlation between bait and target[i]
'''
return np.array(
[np.corrcoef(bait, c)[0,1]
for c in target])
[ 167 ]