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Decode the Coding Interview in Scala: Real-World Examples

Feature #3: Plot and Select Path

Explore how to determine the lowest cost path from multiple Uber drivers to a user by building and traversing a city graph. Learn to apply Depth-First Search to find paths and calculate cumulative travel costs, helping optimize driver selection based on availability and route expense.

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Description

After obtaining the closest drivers and calculating the cost of traveling on different roads, we need to build a functionality to select a path from the driver’s location to the user’s location. All the drivers have to pass through multiple checkpoints to reach the user’s location. Each road between checkpoints will have a cost, which we learned how to calculate in the previous lesson. It is possible that some of the k chosen drivers might not have a path to the user due to unavailability. Unavailability can occur due to a driver already ...

In the above example,

  • G_map has the values [["a","b"],["b","c"],["a","e"],["d","e"]].

  • path_costs has the values [12,23,26,18].

  • drivers has the values ["c", "d", "e", "f"].

  • user has a value "a".

After calculating the total cost of each driver’s route to the user, we’ll select that driver that has a path to the user with the lowest cost. Here, the driver f has no path to the user due to unavailability.

Solution

The main problem comes down to finding a path between two nodes, if it exists. If the path exists, return the cumulative sums along the path as the result. Given the problem, it seems that we need to track the nodes where we come from. DFS (Depth-First Search), also known as the backtracking algorithm, will be applicable in this case.

Here is how the implementation will take place:

  1. Build the graph using the city map list G_map.

  2. Assign the cost to each edge while building the graph.

  3. Once the graph is built, evaluate each driver’s path in the drivers list by searching for a path between the driver node and the user node. ...

Scala
import scala.collection.immutable.List
import scala.collection.mutable
import scala.collection.mutable.{HashMap, HashSet}
object Main {
  def getTotalCost(GMap: List[List[String]], pathCosts: Array[Double], drivers: List[String], user: String): Array[Double] = {
    var city = new HashMap[String, HashMap[String, Double]]
    // Step 1). build the city from the GMap
    for (i <- 0 until GMap.size) {
      var checkPoints = GMap(i)
      var sourceNode = checkPoints(0)
      var destNode = checkPoints(1)
      var pathCost = pathCosts(i)
      if (!city.contains(sourceNode))
        city(sourceNode) = new HashMap[String, Double]()
      if (!city.contains(destNode))
        city(destNode) = new HashMap[String, Double]()
      (city(sourceNode))(destNode) = pathCost
      (city(destNode))(sourceNode) = pathCost
    }
    // Step 2). Evaluate each driver via bactracking (DFS)
    // by verifying if there exists a path from driver to user
    var results = new Array[Double](drivers.size)
    for (i <- 0 until drivers.size) {
      val driver = drivers(i)
      if (!city.contains(driver) || !city.contains(user)) results(i) = -1.0
      else {
        val visited = new HashSet[String]
        results(i) = backtrackEvaluate(city, driver, user, 0, visited)
      }
    }
    results
  }
  def backtrackEvaluate(city: HashMap[String, HashMap[String, Double]], currNode: String, targetNode: String, accSum: Double, visited: mutable.HashSet[String]): Double  = { // mark the visit
    visited.add(currNode)
    var ret = -1.0
    val neighbors = city(currNode)
    if (neighbors.contains(targetNode))
      ret = accSum + neighbors(targetNode)
    else {
      var keys = neighbors.keys.toList
      var i:Int = 0
      var breakit = true
      while (i<keys.size && breakit==true) {
        var nextNode = keys(i)
        if (visited.contains(nextNode)) {
          //--- do nothing
        }
        ret = backtrackEvaluate(city, nextNode, targetNode, accSum + neighbors(nextNode), visited)
        if (ret != -1.0)
          breakit = false
        i += 1
      }
    }
    // unmark the visit, for the next backtracking
    visited.remove(currNode)
    ret
  }
  def main(args: Array[String]): Unit = { // Driver code
    val GMap = List(List("a", "b"), List("b", "c"), List("a", "e"), List("d", "e"))
    val pathCosts = Array(12.0, 23.0, 26.0, 18.0)
    val drivers = List("c", "d", "e", "f")
    val user = "a"
    val allPathCosts = getTotalCost(GMap, pathCosts, drivers, user)
    print("Total cost of all paths: [")
    allPathCosts.foreach({
      el => print(el + ", ")
    })
    print("]")
  }
}
Plot and Select Path

Complexity measures

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