After creating *recommendations.py* and running the commands on page 9 of “Collective Intelligence”, I got an error about *recommendations* not existing. I then re-read the page and moved *recommendations.py* to the Lib directory in Python. That fixed it right away. I love how easy Python makes it to use data structures like dictionaries and lists!

**Euclidean Distance**

Plugging in the Euclidean distance right into the Python interpreter (using IDLE) gave me the same answers as the example in the book with Toby and LaSalle. However, when I added the function *sim_distance* to *recommendations.py* I got a different answer for Lisa Rose and Gene Seymour. I added the squares of the differences by hand and got the same answer as my function. I think the general consensus is the book is wrong!

**Pearson Coefficient**

The Pearson coefficient worked correctly and yielded the same results as the book. It took me a while to understand how the function *sim_pearson* was operating like the formula we discussed in class but I worked through it.

**Manhattan Distance**

Implementing the Manhattan distance was pretty simple. I followed the same format as the *sim_distance* and *sim_pearson* functions. The formula for the Manhattan distance is |X1-X2|+|Y1-Y2|+…+|Z1-Z2|. I had to look up the syntax for an absolute value function in Python and it was what I thought it would be: *abs(x)*. Below is my *sim_manhattan* function.

from math import sqrt

# Returns a distance-based similarity score for personA and personB

def sim_manhattan(prefs, personA, personB):

# Get the list of shared_items

si={}

for item in prefs[personA]:

if item in prefs[personB]:

si[item]=1

# if they have no ratings in common, return 0

if len(si)==0: return 0

# Add up the absolute values of all the differences

sum_of_abs=sum([abs(prefs[personA][item]-prefs[personB][item]) for item in si])

return sum_of_abs

When tested in the Python interpretor with the critics Lisa Rose and Gene Seymour, I got the following, correct result:

>>> reload(recommendations)

<module ‘recommendations’ from ‘C:\Python26\lib\recommendations.py’>

>>>recommendations.sim_manhattan(recommendations.critics,’Lisa Rose’, ‘Gene Seymour’)

4.5