examples: fix typos (#18229)

examples/graphs/dijkstra.v

@@ -22,7 +22,7 @@ $ ./an_executable.EXE
 Code based from : Data Structures and Algorithms Made Easy: Data Structures and Algorithmic Puzzles, Fifth Edition (English Edition)
 pseudo code written in C
 This idea is quite different: it uses a priority queue to store the current
-shortest path evaluted
+shortest path evaluated
 The priority queue structure built using a list to simulate
 the queue. A heap is not used in this case.
 */
@@ -38,17 +38,17 @@ mut:
 // The "push" always sorted in pq
 fn push_pq[T](mut prior_queue []T, data int, priority int) {
 	mut temp := []T{}
-	lenght_pq := prior_queue.len
+	pq_len := prior_queue.len
 
 	mut i := 0
-	for i < lenght_pq && priority > prior_queue[i].priority {
+	for i < pq_len && priority > prior_queue[i].priority {
 		temp << prior_queue[i]
 		i++
 	}
 	// INSERTING SORTED in the queue
 	temp << NODE{data, priority} // do the copy in the right place
 	// copy the another part (tail) of original prior_queue
-	for i < lenght_pq {
+	for i < pq_len {
 		temp << prior_queue[i]
 		i++
 	}
@@ -59,16 +59,16 @@ fn push_pq[T](mut prior_queue []T, data int, priority int) {
 // Change the priority of a value/node ... exist a value, change its priority
 fn updating_priority[T](mut prior_queue []T, search_data int, new_priority int) {
 	mut i := 0
-	mut lenght_pq := prior_queue.len
+	mut pq_len := prior_queue.len
 
-	for i < lenght_pq {
+	for i < pq_len {
 		if search_data == prior_queue[i].data {
-			prior_queue[i] = NODE{search_data, new_priority} // do the copy in the right place	
+			prior_queue[i] = NODE{search_data, new_priority} // do the copy in the right place
 			break
 		}
 		i++
 		// all the list was examined
-		if i >= lenght_pq {
+		if i >= pq_len {
 			print('\n This data ${search_data} does exist ... PRIORITY QUEUE problem\n')
 			exit(1) // panic(s string)
 		}
@@ -126,7 +126,7 @@ fn dijkstra(g [][]int, s int) {
 	mut n := g.len
 
 	mut dist := []int{len: n, init: -1} // dist with -1 instead of INIFINITY
-	mut path := []int{len: n, init: -1} // previous node of each shortest paht
+	mut path := []int{len: n, init: -1} // previous node of each shortest path
 
 	// Distance of source vertex from itself is always 0
 	dist[s] = 0
@@ -223,13 +223,13 @@ fn main() {
 		[5, 15, 4, 0],
 	]
 
-	// To find number of coluns
+	// To find number of columns
 	// mut cols := an_array[0].len
 	mut graph := [][]int{} // the graph: adjacency matrix
 	// for index, g_value in [graph_01, graph_02, graph_03] {
 	for index, g_value in [graph_01, graph_02, graph_03] {
 		graph = g_value.clone() // graphs_sample[g].clone() // choice your SAMPLE
-		// allways starting by node 0
+		// always starting by node 0
 		start_node := 0
 		println('\n\n Graph ${index + 1} using Dijkstra algorithm (source node: ${start_node})')
 		dijkstra(graph, start_node)