Instructions

We are starting up a cheese club, which will use ML to select new cheeses to offer our cheese-loving customers based on their histories and tastes.

New members are required to fill out an initial survey so we can gather some basic data to start with. Through this, it's been found that there is a subset of emphatic clients who lack nuance in their critiques. As this can end up irrevocably biasing a more nuanced algorithm, there is a separate algorithm set up to handle their needs. You are asked to provide some helper functions to wrangle their data.

1. Classify customers

The rating system has a five star basis, which is simply consists of integers 1:5. The emphatic customers will only give ratings of 1 or 5, and we want to know if a customer exhibits this behavior.

Implement all_15() which takes a vector of ratings and returns true if all ratings are either 1 or 5, and false otherwise.

2. Separate out emphatic customers

We need to separate the more emphatic customers from the others.

Implement emphatics() which takes a dictionary of customers and ratings. Returns a similar dictionary with those who only use only 1 or 5 star ratings.

3. Change ratings to binary

Since the emphatic customers only use 1 and 5 ratings, it will more computationally convenient if these are changed these to 0 and 1.

Implement tobinary() which takes vector of emphatic ratings. Returns binary ratings, where 1 has been changed to 0 and 5 has been changed to 1.

4. Make ratings into a matrix

Our algorithms use Matrix inputs, so we will need to transform the data into one.

Implement tobinarymatrix() which takes a vector of emphatic rating vectors. Returns a Matrix of the transformed data, with each rating vector a row in the matrix.