Begin with recommending items section of PCI

filter.py: Added getWeightedItemScore and stubbed out getRecommnedations
similarityscorealgorithms.py:  Added EuclidPCI to compare to PCI scores
__init__.py: added a line to see the results of getWeightedItemScore
Recommend items for a user

filter.py:  Implemented getRecommendations
Fixed spelling in comment and commented out some old prints in main

filter.py:  usvalider changed to user
Updated test cases, EuclidianDistance algorithm

__init__.py: removed some old print statements
filter.py:   computeScoreTwo --> computeSimilarityScore and corrected
             return value
similarityscorealgorithms.py:  changed range of Euclid to (0,1]
testScorers.py:  removed some value tests, added a test to check a
                 recommendation exists when it should
Testing a pull and branch switching

pullTest:  added this file
Removed pullTest

pullTest:  removed file
MovieLens data set

Added MovieLens data set to data directory
Bigger Data Set

Added functionality in the DataReader to use the MovieLens data set.
Fixed a bug where getRecommendations would break if users' similarity score
was None.
.gitignore:  Added *.out.
Beginning of Item based filtering.  Created method to transpose a data set.

dataReader.py:  added transposeUsersWithItems.
__init__.py  :  ran a test to see if transpose worked.  Returned top 20 users
                who should see Toy Story.
Add k nearest neighbors method.
Item Based Filtering / Interface

Added methods for building the item similarity dictionary and getting
recommendations for a user from the item sim dict.  Also added a basic
interface for getting recs for any user using item based filtering.
Number of Recommendations

__init__.py: Commented out User based block.  added logic to interface to get number of recs from user.
filter.py:   Notice at beginning of creation of item sim dict.  Number of recs from getItemBasedRecs.