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Ceis295 final project_b_cooper
- 1. Abstract data types WHAT THEY CAN DO FOR US BRIAN COOPER
- 2. Abstract data types algorithm speeds INTRODUCTION
- 3. Table of contents Week 1 • ADTs • Array list Week 2 • Linked List • Doubly linked list Week 3 • Stacks • Queues Week 4 • Big O Notation • Recursion • Binary search • Selection sort • Insertion sort • Shell sort
- 4. Table of contents Week 5 • Hash Table • Mid-square hash table • Binary mid-square hash table • Multiplicative string hash Week 6 • Binary trees • Binary search trees Week 7 • Topological graph • Weighted graph • Digraph • Minimum spanning tree
- 5. What is an ADT(Abstract Data Type) This Photo by Unknown Author is licensed under CC BY-SA
- 6. Common ADT types List Dynamic array Queue Deque (prn: “Deck”) Bag Set Priority Queue Dictionary(map) This Photo by Unknown Author is licensed under CC BY-NC
- 7. Quantifying algorithms # Brian Cooper #03/05/2021--8:00 pm # this is meant to determine how long a program takes to run # first import time libraray import time #Start time by setting the timer and setting vaule to start_time start_time = time.time() # next run code for i in range(1000): print("Hello everyone!") # Set end time to end_time varia ble end_time = time.time() #determine totla time by subtracting end time from start time # print time # The {:.6F} is the way to format in this case to 6 floating point places after the "" add .format(var to format) print("nSeconds run 1000 times:{:.6F}".format(Total_time)) input("Press any key")
- 9. Actual Speed Your speeds may vary!
- 10. Big O Notation First let's stop and talk about how we can decide which is the fastest of the algorithms we are learning about.
- 11. Lists Linked List Doubly linked list This Photo by Unknown Author is licensed under CC BY This Photo by Unknown Author is licensed under CC BY-SA
- 12. Actual Speed Linked lists Memory saving Faster for certain types of situations Speed
- 13. Stacks Queues Stack: Add to the top. Pull from the top Queue: Add to the bottom pull from the top.
- 14. Real world application How would this really work?
- 15. Sorting arrays For many of the algorithms to work we must have the array in order Example… Selection sort Insertion sort Shell sort This Photo by Unknown Author is licensed under CC BY-SA
- 16. Since we need to sort how quickly can we sort?
- 17. Searches
- 18. Hash tables How a hash table works Buckets Open addressing chaining This Photo by Unknown Author is licensed under CC BY-SA
- 19. Hash Functions Mid-square Binary mid-square Multiplicative This Photo by Unknown Author is licensed under CC BY
- 20. Recursion This Photo by Unknown Author is licensed under CC BY-NC-ND
- 21. Tree structures Binary Tree Binary search tree Speed
- 22. Graphs
- 23. Adjacency matrix and list
- 25. Challenge's slide Sort code not working properly Stop and test, a lot! Implementation Real world size!
- 26. Final Analysis It all depends. There are many different ways to deal with data, but which one is best depends on how your needs.
- 27. Thank you for watching!
- 28. Refrences Big O Complexity chart: https://www.digitalocean.com/community/tutorials/js-big-o-notation Word search puzzle: https://en.wikipedia.org/wiki/Word_search Array: https://www.toolsqa.com/data-structures/array-in-programming/
Editor's Notes
- What is the point of this whole power point….
- Give quick example of each kind of ADT
- Big O(N)
- First talk about constant time then-Discuss each curve and how that applies
- Describe the differences in each kind of linked list
- Describe the differences of they work and key terms specific to each
- For many ADTs/ algorithms to work properly they must be sorted. These are the different ways of doing just that.
- Remember to talk about speed Big O(N)
- Searches are the hardest thing for an algorithm to preform. The thing you are looking for could be anywhere so how do we do that quickly?
- Basics of hashing, talk about collisions
- Different ways of doing the same thing, only better with less collisions!
- Explain recursion and how can benefit us