In this week's Sprint you explored and implemented some classic algorithmic approaches and used them to solve novel problems. You also implemented some classic and fundamental sorting algorithms and learned about how to go about evaluating their respective runtimes and performance. This Sprint Challenge aims to assess your comfort with these topics through exercises that build on the data structures you implemented and the algorithmic intuition you've started to build up.
Read these instructions carefully. Understand exactly what is expected before starting this Sprint Challenge.
This is an individual assessment. All work must be your own. Your Challenge score is a measure of your ability to work independently using the material covered throughout this sprint. You need to demonstrate proficiency in the concepts and objectives that were introduced and that you practiced in the preceding days.
You are not allowed to collaborate during the Sprint Challenge. However, you are encouraged to follow the twenty-minute rule and seek support from your PM and Instructor in your cohort help channel on Slack. Your submitted work reflects your proficiency in the concepts and topics that were covered this week.
You have three hours to complete this Sprint Challenge. Plan your time accordingly.
Commit your code regularly and meaningfully. This helps both you (in case you ever need to return to old code for any number of reasons) and it also helps your project manager to more thoroughly assess your work.
This Sprint Challenge is split into two separate parts that test your ability to write and analyze algorithms.
For this portion of the sprint challenge, you'll be answering questions posed in
the Algorithms_Questions.md document inside the Self-Study directory. Write
down your answer and also write down a justification for why you put down that
answer. This could net you some partial credit if your justification is sound
but the answer you put down turns out to not be correct. Add your answers to the
questions in the Algorithms_Answers.md file.
Inside the task1 directory you'll find the heap.py file with a working
implementation of the heap class--the heap is already written for you.
A heap is a tree-like data structure that can rapidly tell you what the largest or smallest number is within that set of data.
You have to figure out how to use the heap to implement heapsort.
Some hints:
-
Initially when you make a new
Heapdata structure, it is empty. -
You can insert values into the heap with its
insert()method. -
This is a max heap. That is, the
get_max()anddelete()methods will return the maximum value stored in the heap. In additiondelete()also removes the value from the heap. -
Pseudocode in Wikipedia or elsewhere will be of little use here. Think conceptually at first; how could you use this information to sort a list of numbers? Then code.
Your heapsort function should return a new list containing all of the sorted
data.
Run python test_heap.py to run the tests for your heapsort function to
ensure that your implementation is correct.
Open up the Analysis_Answers.md file. This is where you'll jot down your
answers for the runtimes of the functions you just implemented.
The heap presented in the code is called a max heap, because delete() always
returns the maximum value in the heap.
Modify it to be a min heap, so that delete() always returns the minimum
value in the heap. Also change get_max() to get_min().
Your heapsort probably broke after you did this. What did you have to modify to fix it?
How does the time complexity change?
In terms of wall clock time, is the min heap version faster or slower?
Modify the Heap class and your heapsort() algorithm to run in O(1) space.
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