Perfect Numbers in Standard ML
Determine if a number is perfect, abundant, or deficient based on Nicomachus' (60  120 CE) classification scheme for natural numbers.
1  exercism fetch sml perfectnumbers

Perfect Numbers
Determine if a number is perfect, abundant, or deficient based on Nicomachus' (60  120 CE) classification scheme for natural numbers.
The Greek mathematician Nicomachus devised a classification scheme for natural numbers, identifying each as belonging uniquely to the categories of perfect, abundant, or deficient based on their aliquot sum. The aliquot sum is defined as the sum of the factors of a number not including the number itself. For example, the aliquot sum of 15 is (1 + 3 + 5) = 9

Perfect: aliquot sum = number
 6 is a perfect number because (1 + 2 + 3) = 6
 28 is a perfect number because (1 + 2 + 4 + 7 + 14) = 28

Abundant: aliquot sum > number
 12 is an abundant number because (1 + 2 + 3 + 4 + 6) = 16
 24 is an abundant number because (1 + 2 + 3 + 4 + 6 + 8 + 12) = 36

Deficient: aliquot sum < number
 8 is a deficient number because (1 + 2 + 4) = 7
 Prime numbers are deficient
Implement the classify
function, it should return one of Abundant
, Deficient
or Perfect
. If the input param is not a positive integer, raise the exception NotAPositiveInteger
.
Loading your exercise implementation in PolyML
1 
$ poly use {exercise}.sml

Or:
Note: You have to replace {exercise}.
Running the tests
1 
$ poly q use test.sml

Feedback, Issues, Pull Requests
The exercism/sml repository on GitHub is the home for all of the Standard ML exercises.
If you have feedback about an exercise, or want to help implementing a new one, head over there and create an issue. We'll do our best to help you!
Source
Taken from Chapter 2 of Functional Thinking by Neal Ford. http://shop.oreilly.com/product/0636920029687.do
Submitting Incomplete Solutions
It's possible to submit an incomplete solution so you can see how others have completed the exercise.