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examples: add 2 more graph search examples (DFS and BFS), move them into `examples/graphs` (#14131)

Claudio Cesar de Sá 2022-04-22 06:01:29 -03:00 committed by Jef Roosens
parent e0963381ec
commit 7325727e38
Signed by: Jef Roosens
GPG Key ID: B75D4F293C7052DB
3 changed files with 210 additions and 15 deletions
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30
examples/bfs.v → examples/graphs/bfs.v
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@ -1,3 +1,18 @@
fn main() {
graph := {
'A': ['B', 'C']
'B': ['A', 'D', 'E']
'C': ['A', 'F']
'D': ['B']
'E': ['B', 'F']
'F': ['C', 'E']
}
println('Graph: $graph')
path := breadth_first_search_path(graph, 'A', 'F')
println('The shortest path from node A to node F is: $path')
assert path == ['A', 'C', 'F']
}
// Breadth-First Search (BFS) allows you to find the shortest distance between two nodes in the graph. // Breadth-First Search (BFS) allows you to find the shortest distance between two nodes in the graph.
fn breadth_first_search_path(graph map[string][]string, vertex string, target string) []string { fn breadth_first_search_path(graph map[string][]string, vertex string, target string) []string {
mut path := []string{} mut path := []string{}
@ -24,18 +39,3 @@ fn breadth_first_search_path(graph map[string][]string, vertex string, target st
} }
return path return path
} }
fn main() {
graph := {
'A': ['B', 'C']
'B': ['A', 'D', 'E']
'C': ['A', 'F']
'D': ['B']
'E': ['B', 'F']
'F': ['C', 'E']
}
println('Graph: $graph')
path := breadth_first_search_path(graph, 'A', 'F')
println('The shortest path from node A to node F is: $path')
assert path == ['A', 'C', 'F']
}

92
examples/graphs/bfs2.v 100644
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@ -0,0 +1,92 @@
// Author: ccs
// I follow literally code in C, done many years ago
fn main() {
// Adjacency matrix as a map
graph := {
'A': ['B', 'C']
'B': ['A', 'D', 'E']
'C': ['A', 'F']
'D': ['B']
'E': ['B', 'F']
'F': ['C', 'E']
}
println('Graph: $graph')
path := breadth_first_search_path(graph, 'A', 'F')
println('\n The shortest path from node A to node F is: $path.reverse()')
}
// Breadth-First Search (BFS) allows you to find the shortest distance between two nodes in the graph.
fn breadth_first_search_path(graph map[string][]string, start string, target string) []string {
mut path := []string{} // ONE PATH with SUCCESS = array
mut queue := []string{} // a queue ... many paths
// all_nodes := graph.keys() // get a key of this map
n_nodes := graph.len // numbers of nodes of this graph
// a map to store all the nodes visited to avoid cycles
// start all them with False, not visited yet
mut visited := a_map_nodes_bool(n_nodes) // a map fully
// false ==> not visited yet: {'A': false, 'B': false, 'C': false, 'D': false, 'E': false}
queue << start // first arrival
for queue.len != 0 {
mut node := departure(mut queue) // get the front node and remove it
if visited[node] == false { // check if this node is already visited
// if no ... test it searchinf for a final node
visited[node] = true // means: visit this node
if node == target {
path = build_path_reverse(graph, start, node, visited)
return path
}
// Expansion of node removed from queue
print('\n Expansion of node $node (true/false): ${graph[node]}')
// take all nodes from the node
for vertex in graph[node] { // println("\n ...${vertex}")
// not explored yet
if visited[vertex] == false {
queue << vertex
}
}
print('\n QUEUE: $queue (only not visited) \n Visited: $visited')
}
}
path = ['Path not found, problem in the Graph, start or end nodes! ']
return path
}
// Creating a map for VISITED nodes ...
// starting by false ===> means this node was not visited yet
fn a_map_nodes_bool(size int) map[string]bool {
mut my_map := map[string]bool{} // look this map ...
base := u8(65)
mut key := base.ascii_str()
for i in 0 .. size {
key = u8(base + i).ascii_str()
my_map[key] = false
}
return my_map
}
// classical removing of a node from the start of a queue
fn departure(mut queue []string) string {
mut x := queue[0]
queue.delete(0)
return x
}
// Based in the current node that is final, search for its parent, already visited, up to the root or start node
fn build_path_reverse(graph map[string][]string, start string, final string, visited map[string]bool) []string {
print('\n\n Nodes visited (true) or no (false): $visited')
array_of_nodes := graph.keys()
mut current := final
mut path := []string{}
path << current
for (current != start) {
for i in array_of_nodes {
if (current in graph[i]) && (visited[i] == true) {
current = i
break // the first ocurrence is enough
}
}
path << current // update the path tracked
}
return path
}

103
examples/graphs/dfs.v 100644
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@ -0,0 +1,103 @@
// Author: ccs
// I follow literally code in C, done many years ago
fn main() {
// Adjacency matrix as a map
// Example 01
graph_01 := {
'A': ['B', 'C']
'B': ['A', 'D', 'E']
'C': ['A', 'F']
'D': ['B']
'E': ['F', 'B', 'F']
'F': ['C', 'E']
}
// Example 02
graph_02 := {
'A': ['B', 'C', 'D']
'B': ['E']
'C': ['F']
'D': ['E']
'E': ['H']
'F': ['H']
'G': ['H']
'H': ['E', 'F', 'G']
}
// println('Graph: $graph')
path_01 := depth_first_search_path(graph_01, 'A', 'F')
println('\n Graph_01: a first path from node A to node F is: $path_01.reverse()')
path_02 := depth_first_search_path(graph_02, 'A', 'H')
println('\n Graph_02: a first path from node A to node F is: $path_02.reverse()')
}
// Depth-First Search (BFS) allows you to find a path between two nodes in the graph.
fn depth_first_search_path(graph map[string][]string, start string, target string) []string {
mut path := []string{} // ONE PATH with SUCCESS = array
mut stack := []string{} // a stack ... many nodes
// all_nodes := graph.keys() // get a key of this map
n_nodes := graph.len // numbers of nodes of this graph
mut visited := a_map_nodes_bool(n_nodes) // a map fully
// false ... not visited yet: {'A': false, 'B': false, 'C': false, 'D': false, 'E': false}
stack << start // first push on the stack
for stack.len > 0 {
mut node := stack.pop() // get the top node and remove it from the stack
// check if this node is already visited
if visited[node] == false {
// if no ... test it searchin for a final node
visited[node] = true // means: node visited
if node == target {
path = build_path_reverse(graph, start, node, visited)
return path
}
// Exploring of node removed from stack and add its relatives
print('\n Exploring of node $node (true/false): ${graph[node]}')
// graph[node].reverse() take a classical choice for DFS
// at most os left in this case.
// use vertex in graph[node] the choice is right
// take all nodes from the node
for vertex in graph[node].reverse() {
// println("\n ...${vertex}")
// not explored yet
if visited[vertex] == false {
stack << vertex
}
}
print('\n Stack: $stack (only not visited) \n Visited: $visited')
}
}
path = ['Path not found, problem in the Graph, start or end nodes! ']
return path
}
// Creating a map for nodes not VISITED visited ...
// starting by false ===> means this node was not visited yet
fn a_map_nodes_bool(size int) map[string]bool {
mut my_map := map[string]bool{} // look this map ...
for i in 0 .. size {
my_map[u8(65 + i).ascii_str()] = false
}
return my_map
}
// Based in the current node that is final, search for his parent, that is already visited, up to the root or start node
fn build_path_reverse(graph map[string][]string, start string, final string, visited map[string]bool) []string {
print('\n\n Nodes visited (true) or no (false): $visited')
array_of_nodes := graph.keys()
mut current := final
mut path := []string{}
path << current
for current != start {
for i in array_of_nodes {
if (current in graph[i]) && (visited[i] == true) {
current = i
break // the first ocurrence is enough
}
}
path << current // updating the path tracked
}
return path
}