Reflecting on Trees and Binary Trees

In an earlier post, I wrote that I preferred working with binary trees and binary search trees than with trees, because I could make assumptions about the number of children of each node, rather than dealing with a tree with a random number or arrangement of children per node.

Although my view hasn't really changed, after finishing Assignment 3, I have a better understanding of how trees work and how to implement functions that use them.  Even though I still prefer binary trees, I feel much more comfortable now working with trees than I did when I wrote the earlier post.   The grow function in Assignment 3 especially helped my understanding of trees because it required the construction of a tree from scratch.  Similar to when I wrote my first recursive function, writing the grow function with my group members allowed me to explore the construction of trees which made it easier to implement the remaining functions.

I decided to ask my group members what they thought of binary trees vs. trees.  One preferred binary trees because of the assumptions you can make when accessing the items in the tree, while the other preferred working with trees because they were more versatile as you are not limited in the branching factor of the tree. 

Week 9

This week we learned about deleting and modifying binary trees.  Originally, the code was a bit confusing to me, but after the professor outlined the algorithm step-by-step in english it became more clear.

We only had one lecture this week because the second term-test was on Wednesday.  Despite having done well on the first test, I was still nervous for this one.  As I expected, this test was harder than the first one, although I still think I did fairly well.  My aid sheet was very helpful as it provided me with hints for two of the questions on the test. 

Week 8

This week we learned about linked lists, which are a special type of list made up of nodes that reference other nodes.  In this way, each of the nodes are linked together.  I originally found the concept confusing, but after drawing pictures and practising some of the lab exercises I had a better understanding of the order in which to perform the operations in my code so as not to lose any essential data from the list.

Assignment 2 was also due this week.  My group and I had only three functions left to complete: minimax, winner, and rough_outcome.  Minimax worked with subtract square, but we had yet to test it with a game of tippy.  I realized that our original solution for checking for a tippy was inefficient and would take too long to write and debug. Therefore, I suggested that we only check for horizontal tippys and then rotate the board four times and return whether a tippy is found.  Using this code, we only had to deal with the two cases of horizontal tippys rather than the four cases we had to account for prior to my suggestion. We worked out a few errors in the code, such as where it would not detect a tippy in a large compact group of X's or O's, or where it would only detect the first tippy in a row and not all the tippy's. For example, before debugging, our code would incorrectly return on the following boards:

X|X|X|X|
X|X|X|X|
X|X|X|X|
O|O|O|O|

and

X|X|O|O|X|X|O|
O|X|X|O|O|X|X|
X|O|X|O|X|O|X|
O|X|O|X|O|X|O|
X|O|X|O|X|O|X|
O|X|O|X|O|X|O|
X|O|X|O|X|O|X|

After completing winner, we found out, much to our dismay, that minimax did not work correctly for a game of tippy.  Luckily, we had lots of time to find the errors and fix them, and we did so fairly quickly.  We then implemented rough_outcome without any major errors, although we were not concerned about that function as it wasn't used in the assignment.  After fixing some more minor errors, we ran our code multiple times with a game of tippy and it correctly picked the middle square on a 3X3 grid every time. 

Week 7

Last week we learned about binary trees and binary search trees.  Binary trees are a special type of tree where each node only has two children, a left value and a right value.  Binary search trees are a special type of binary tree where the left child of the node contains a value that is less than that of the node and the right child of the node contains a value that is greater than the value of the node.  With a tree in this configuration, it is easy and efficient to search for values as the computer does not have to look at all of the nodes, only the ones that are greater than the value being searched for.

I found it easier to implement methods for binary trees and binary search trees than for regular trees because I could assume that each node has only two children and I could directly access each node's left and right values, instead of having only a list of the children on the main node to work with, as is the case with trees. 

My group members and I also began work on assignment 2 this week and managed to write most of the tippy game state as well as a version of minimax that worked with subtract square.  However, we were having trouble with figuring out an efficient way to detect all the possible tippy arrangements on any board size, and thus couldn't test our minimax function with a game of tippy. 

Summary Topic to be Graded

As I have already written a summary of Object-Oriented Programming and Abstract Data Types I would like my summary of Object-Oriented Programming to be Graded.  It is paragraphs one to three of the post entitled: A Summary of Object-Oriented Programming and Abstract Data Type, which can be found here.