Exponential growth
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README.md
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README.md

Array Lists

By now you should have a Fixed Array class. Fixed Arrays are a fundamental building block of data structures, but they're not always the most convenient. From here on out, if you read the term "array" take it to mean "an array with a fixed size."

The Array List

In this challenge we're going to create an ArrayList class. An Array List isn't a special kind of array, it's a list of things that uses an array underneath. Lists are different from arrays because lists can grow.

Note: ArrayList is a different data structure than a FixedArray, there is no inheritance relationship between them. It's unlikely you'll be using inheritance in any of challenges.

The ArrayList will be a data structure that allows you to create and add to a List of "infinite" size. You won't need to tell the Array List how many elements it will contain up front.

That said, your ArrayList will use your FixedArray class under the hood. You'll need to figure out how to make your ArrayList grow given this restriction.

Why is this important?

Sometimes we can't know how big a collection of things will be up front, but a standard array forces us to choose a set size when we're writing our code, which presents us with a conundrum. We could try and allocate an array that's just really big, but we'd be using memory inefficiently, and we still might out grow it.

If the size of our collection can't be determined at compile time, it's impossible for us to just use a standard fixed-size array.

Again, your ArrayList will use FixedArray underneath, but it will be able to grow to any number of elements.

Release 1: ArrayList

Implement and write RSpec tests for the ArrayList class, supporting the following interface:

Interface

  • ArrayList#new(size): Instantiate a new dynamic array with initial size size.
  • ArrayList#add(element): Add element to the end of the list. Return the element added.
  • ArrayList#get(index): Retrieve an element at index. If no element exists at that index, throw an OutOfBoundsException
  • ArrayList#set(index, element): Replace an existing element at index. Return the element. If no element exists at that index, throw an OutOfBoundsException
  • ArrayList#length: Return the number of items in the list

Release 2: Insert

Sometimes we want to be able to inject an element into the middle of a list. Implement and test ArrayList#insert(index, element). #insert should insert the value element in the List at position index. If there isn't an element at that position in the list, throw an OutofBoundsException

Release 3: Complexity

A crucial part of knowing a data structure is the big-O of its operations. Knowing how to make one isn't enough.

By now you have the following methods on your ArrayList class:

  • ArrayList#new(size)
  • ArrayList#add(element)
  • ArrayList#get(index)
  • ArrayList#set(index, element)
  • ArrayList#size(element)
  • ArrayList#insert(index, element)

For each of these methods, determine the big-O complexity of the method. Create a file complexity.md and write the big-O for each method, explaining why.

For example, ArrayList#new is O(1) — whether our list ends up containing 0 elements or 1000, #new(size) will always take the same amount of time.

Be sure to note the best case and worst case complexity for each method. Depending on your growth strategy, certain methods may take much longer depending on certain circumstances.

Stretch: Growth strategies

There are many strategies to grow your ArrayList, but they're not all created equal. Think about your own strategy, and whether it's optimal. You may interested to know that there is a growth strategy that will allow for amortized constant time.

Amortized?

In plain English, amortized analysis of an algorithm's complexity considers the time taken in the context of many operations, not just one. If an operation is very costly sometimes, and not costly other times, we average out the cost in an amortized analysis.

If, say, the average cost of our list growth stays constant despite the size of the list we can say it's got a constant-time amortized complexity (O(1)) even if the worse-case scenario might be worse than O(1).

If you improve your growth strategy, your existing tests should prove your ArrayList is still working the same as it did before the refactor. If there are un-caught bugs, or if you see holes in your tests, add those tests. Ensure the tests fail before you fix your code and make them pass.