go-workshop

Welcome to the Women Who Go workshop in January challenge with fromAtoB

What does fromatob do?

fromAtoB's vision is to combine flights, trains, long distance buses and car-pooling towards your perfect trip from A to B within one booking, one payment, within less than 30 seconds.

What are we doing today?

1. Brief explanation on graphs at fromAtoB
2. Mini challenge to familiarise ourselves with our graph pkg/graph.go
3. Breadth-first-search algorithm
4. Bring in the mission

1. What are graphs?

Graphs are mathematical structures used to model pairwise relations between objects. A graph in this context is made up of nodes (also called vertices or points) which are connected by edges

https://en.wikipedia.org/wiki/Graph_theory

What kind of problems do graphs solve?

Graphs are used for lots of things, but here are some examples:

• Computer networks - Often nodes will represent end-systems or routers, while edges represent connections between these systems.
• Sequence of events - Prioritising projects, finding duration of each project. Use tree structures
• Pathing and Maps: Trying to find shortest or longest paths from some location to a destination makes use of graphs. This can include pathing like you see in an application like Google maps, or calculating paths for AI characters to take in a video game, and many other similar problems.
• Constraint Satisfaction: Find some goal that satisfies a list of constraints. For example, Matching professors to courses so classes don't clash. Constraint satisfaction problems like this are often modeled and solved using graphs.
• Molecules: Model atoms and molecules for studying their interaction and structure among other things.

How are graphs used at fromAtoB?

Our main search functions involves using an undirected graph to connect cities on a map. We use open data on public transit timetables and extract the fields we need to build network graphs. Then we create indivudual graphs for each mode of transportation. After that, we run a traversal algorithm like Dijkstra algorithm to find the shortest distance

2. Explore mini challenges

The challenges are based on a simplified graph of train connections, which is loaded from trains.txt. Take a look at trains.png for a graphical view.

• Print the Node ID for Berlin
• Print the Nodename for NodeID 7
• Print the nodes connected to Berin

Eg.

fmt.Println(g.LookupNode("Berlin"))
fmt.Println(g.NodeName(6))
fmt.Println(g.Neighbors(0))

Write forloop to get from Berlin to Leipzig

var n = g.Neighbors(0)
var j, _ = g.LookupNode("Leipzig")

for _, a := range n {
    if a == j {
        fmt.Printf("Connection exists!")
    }
}

3. BFS (10 min Jessica)

What is queueing? Step by step on how to queue and visit

https://en.wikipedia.org/wiki/Breadth-first_search https://medium.com/basecs/going-broad-in-a-graph-bfs-traversal-959bd1a09255

4. The mission

Part 1 - Connected or not?

Use the Graph structure to find out if cities a and b are connected or not Tips on how to start:

• In pkg/algorithms.go fill in the Connected function so it returns true
• Try running go run cmd/go-workshop/main.go to see the Graph structure. If your algorthm works, it should print “ok” on each line.
• There's a Queue type in pkg/queue.go which will be useful if you decide to implement BFS.

Part 2 - Shortest path

Well done on completing part 1! Now that you know if cities a to b are connected or not, let's find the shortest path (shortest number of steps in between) Tips on how to start:

• Fill in the ShortestPath function in pkg/algorithms.go
• A path is represented by a slice of int with the node IDs. For example, a path from node 4 to node 6 and then to node 2 would be represented by []int{4, 6, 2}

Bonus mission

If you've completed the mission and you're getting bored of graph algorithms, let's look at graph data structures instead!

There's more than one way to store the edges in a graph. The one in pkg/graph.go uses an “adjacency list,” which means for each node it stores a list of its neighbors -- that's the edges field in the struct.

Another way is to create a 2-D matrix of booleans. The boolean in row i, column j of the matrix will be true if there's an edge from node i to node j, otherwise it'll be false. This is called an “adjacency matrix.”

Try to change the Graph to use an adjacency matrix instead of an adjacency list! Do it in a way that the code you wrote in pkg/algorithms.go still works -- the methods that access the Graph should work as before.

However, if you want to change the way the graph is created, go ahead! You'll just need to adapt the LoadGraph function in pkg/files.go.