06 Feb Trees for Modeling Real-World Situations
I need a initial post and 2 responses to classmates.
I'll add my unit 7 discussion post and also 2 classmates posts.
Trees for Modeling Real-World Situations
In this discussion, you will continue considering the real-world contexts presented by you and your classmates in the Unit 7 Discussion Board.
Post 1: Initial Response
Using your graph from the Unit 7 Discussion, start this discussion by addressing the following to explore the modeling of your context even further:
- Update your Unit 7 Discussion graph by adding a weight to each of your edges. Present your updated graph with all weighted edges.
- Based on the real-world context of your graph, briefly explain what these weights represent.
- Present a second illustration where you have identified a spanning tree and its total weight within your weighted graph. Describe how you know this subgraph meets the requirements of a spanning tree.
View Unit 8 Discussion Post 1 example.
Post 2: Reply to a Classmate
Review a classmate’s graph and address all of the following items completely.
- Apply either Prim’s algorithm or Kruskal’s algorithm (not presented in the text so you would need to look this up elsewhere) to find a minimum spanning tree for your classmate’s weighted graph. Explain the steps taken and present the minimum spanning tree with a visual.
- In the context of your classmate’s real-world context:
- What is the total weight of this spanning tree?
- What is the difference between your minimum spanning tree and your classmate’s spanning tree (from their initial response)?
- How can you interpret the total weight for this spanning tree within the real-world context?
View Unit 8 Discussion Post 2 example.
Post 3: Reply to Another Classmate
Review a different classmate’s graph and address all of the following items completely.
- Suppose your objective has been updated from spanning the graph. Now you only need to find an efficient path between any two vertices on your classmate’s weighted graph. Write your own step-by-step process (i.e., in pseudocode or a list of steps) which you propose will find the shortest path between any two vertices. Provide enough detail about the steps so that someone else would be able to apply your idea.
- Select a starting and ending vertex (which are not adjacent vertices) on your classmate’s weighted graph. Apply your algorithm and present the path with a visual.
- In the context of your classmate’s real-world context:
- What is the total weight of your proposed shortest path?
- How could the shortest path be useful to your classmate, given the real-world context?
- What steps could you take to test whether or not your algorithm works for determining the shortest paths in all graphs (e.g., for graphs other than this one and for different starting/ending vertices)?
MY Unit 7 DB POST
In my professional world, I manage a team of ten people. Each team member has a unique set of skills and responsibilities. To ensure that our team is working cohesively, I have created a graph to model the relationships between each team member. The graph includes the names of each team member, their respective roles, and the relationships between them. For example, one team member may be responsible for providing technical support to another team member, while another team member may be responsible for providing guidance and mentorship. By visualizing the relationships between each team member, I am able to better understand how each team member contributes to the success of the team as a whole. This graph also helps me to identify any areas of improvement, such as communication or collaboration, that need to be addressed in order to ensure that our team is working efficiently and effectively. An angle's vertex is the point where two of its rays (edges) or two of a polygon's edges meet. In the first case, the vertex stands in for the amount of gas money spent during a trip. Edges are the lines that link nodes in a network. Distance traveled along the road as measured by the perimeter of the image.
Matthew Goetzke posted Feb 4, 2023 9:06 PM
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My post last week was a graph of the 6 major workouts I do where I connected muscle groups that did not conflict in during workouts, and allowing for my physical therapy workouts, to allow for maximum rest between workouts. My physical therapy is mostly leg training, so just assume that physical therapy is a general leg workout. I now weighted them to represent what muscle groups were best to strengthen together, meaning opposing muscle groups weighed less than muscle groups that did not interact at all.
A = Upper Chest
B = Lower Chest
C = Upper Back
D = Lower Back
E= Biceps and Triceps
F = Core
G = Physical Therapy
H = Shoulders
Next I created a spanning tree. I know that this sub-graph is spanning tree because it connects all vertices, does not have a cycle, and does not repeat any edges. The total weight of this graph is 1+1+2+3+1+2+5 = 15.
My week 7 DB was a graph of a set of tasks I needed to complete which had several different variations on how I could do it. It looked like this:
A = Pay Bills(From Home)
B = Buy Groceries
C = Pick up Dry Cleaning
D = Go to the Post Office
E = Visit the Doctor
F = Car Serviced
The total weight of completing all my tasks would be: 5+4+2+5+5 = 21 Miles.
It would look like this:
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