164 lines
6.0 KiB
C++
164 lines
6.0 KiB
C++
/*##############################################################################
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## Author: Shaun Reed ##
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## Legal: All Content (c) 2022 Shaun Reed, all rights reserved ##
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## About: Driver program to test templated object graph implementation ##
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## Algorithms in this example are found in MIT Intro to Algorithms ##
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## ##
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## Contact: shaunrd0@gmail.com | URL: www.shaunreed.com | GitHub: shaunrd0 ##
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################################################################################
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*/
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#include "lib-graph.hpp"
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int main (const int argc, const char * argv[])
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{
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// We could initialize the graph with some localNodes...
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std::vector<Node<char>> localNodes{
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{'a', {{'b', 0}, {'e', 0}}}, // Node a
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{'b', {{'a', 0}, {'f', 0}}}, // Node b
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{'c', {{'d', 0}, {'f', 0}, {'g', 0}}},
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{'d', {{'c', 0}, {'g', 0}, {'h', 0}}},
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{'e', {{'a', 0}}},
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{'f', {{'b', 0}, {'c', 0}, {'g', 0}}},
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{'g', {{'c', 0}, {'d', 0}, {'f', 0}, {'h', 0}}},
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{'h', {{'d', 0}, {'f', 0}}},
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};
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Graph<char> bfsGraphInit(localNodes);
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std::cout << "\n\n##### Breadth First Search #####\n";
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// Or we could use an initializer list...
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// Initialize a example graph for Breadth First Search
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Graph<char> bfsGraph(
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{
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{'a', {{'b', 0}, {'e', 0}}}, // Node a
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{'b', {{'a', 0}, {'f', 0}}}, // Node b
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{'c', {{'d', 0}, {'f', 0}, {'g', 0}}},
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{'d', {{'c', 0}, {'g', 0}, {'h', 0}}},
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{'e', {{'a', 0}}},
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{'f', {{'b', 0}, {'c', 0}, {'g', 0}}},
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{'g', {{'c', 0}, {'d', 0}, {'f', 0}, {'h', 0}}},
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{'h', {{'d', 0}, {'f', 0}}},
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}
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);
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// The graph traversed in this example is seen in MIT Intro to Algorithms
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// + Chapter 22, Figure 22.3 on BFS
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bfsGraph.BFS(bfsGraph.GetNodeCopy('b'));
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std::cout << "\nTesting finding a path between two nodes using BFS...\n";
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// Test finding a path between two nodes using BFS
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auto path = bfsGraph.PathBFS(
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bfsGraph.GetNodeCopy('a'), bfsGraph.GetNodeCopy('g')
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);
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// If we were returned an empty path, it doesn't exist
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if (path.empty()) std::cout << "No valid path found!\n";
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else {
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// If we were returned a path, print it
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std::cout << "\nValid path from " << path.front().GetData()
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<< " to " << path.back().GetData() << ": ";
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for (const auto &node : path) {
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std::cout << node.GetData() << " ";
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}
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std::cout << std::endl;
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}
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std::cout << "\n\n##### Depth First Search #####\n";
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// Initialize an example graph for Depth First Search
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Graph<char> dfsGraph(
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{
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{'a', {{'b', 0}, {'d', 0}}},
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{'b', {{'e', 0}}},
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{'c', {{'e', 0}, {'f', 0}}},
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{'d', {{'b', 0}}},
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{'e', {{'d', 0}}},
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{'f', {{'f', 0}}},
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}
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);
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// The graph traversed in this example is seen in MIT Intro to Algorithms
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// + Chapter 22, Figure 22.4 on DFS
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dfsGraph.DFS();
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std::cout << "\n\n##### Topological Sort #####\n";
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// Initialize an example graph for Depth First Search
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// + The order of initialization is important
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// + To produce the same result as seen in the book
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// ++ If the order is changed, other valid topological orders will be found
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// The book starts on the 'shirt' node (with the number 6, in this example)
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Graph<std::string> topologicalGraph (
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{
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{"undershorts", {{"pants", 0}, {"shoes", 0}}},
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{"socks", {{"shoes", 0}}},
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{"watch", {}},
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{"pants", {{"shoes", 0}, {"belt", 0}}},
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{"shoes", {}},
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{"shirt", {{"tie", 0}, {"belt", 0}}},
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{"belt", {{"jacket", 0}}},
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{"tie", {{"jacket", 0}}},
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{"jacket", {}},
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}
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);
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// The graph traversed in this example is seen in MIT Intro to Algorithms
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// + Chapter 22, Figure 22.4 on DFS
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// Unlike the simple-graph example, this final result matches MIT Algorithms
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// + Aside from the placement of the watch node, which is not connected
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// + This is because the node is visited after all other nodes are finished
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std::vector<Node<std::string>> order =
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topologicalGraph.TopologicalSort(topologicalGraph.GetNodeCopy("shirt"));
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std::cout << "\nTopological order: ";
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while (!order.empty()) {
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std::cout << order.back().GetData() << " ";
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order.pop_back();
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}
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std::cout << std::endl << std::endl;
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// If we want the topological order to match what is seen in the book
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// + We have to initialize the graph carefully to get this result -
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Graph<std::string> topologicalGraph2 (
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{
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{"shirt", {{"tie", 0}, {"belt", 0}}},
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{"tie", {{"jacket", 0}}},
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{"belt", {{"jacket", 0}}},
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{"jacket", {}},
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{"watch", {}},
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{"undershorts", {{"pants", 0}, {"shoes", 0}}},
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{"pants", {{"shoes", 0}, {"belt", 0}}},
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{"shoes", {}},
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{"socks", {{"shoes", 0}}},
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}
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);
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auto order2 = topologicalGraph2.TopologicalSort(*topologicalGraph2.NodeBegin());
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std::cout << "\nTopological order: ";
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while (!order2.empty()) {
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std::cout << order2.back().GetData() << " ";
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order2.pop_back();
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}
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std::cout << std::endl;
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std::cout << "\n\n##### Minimum Spanning Trees #####\n";
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// This example graph is seen in MIT Algorithms chapter 23, figure 23.4
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// + The result we produce is the same in total weight
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// + Differs only in the connection of nodes (b->c) *instead of* (h->a)
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// ++ Both of these edges have the same weight, and we do not create a cycle
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Graph<char> graphMST(
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{
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{'a', {{'b', 4}}},
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{'b', {{'c', 8}}},
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{'c', {{'d', 7}}},
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{'d', {{'e', 9}}},
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{'e', {{'f', 10}}},
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{'f', {{'c', 4}, {'d', 14}, {'g', 2}}},
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{'g', {{'h', 1}}},
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{'h', {{'a', 8}, {'b', 11}, {'i', 7}}},
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{'i', {{'c', 2}, {'g', 6}}}
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}
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);
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InfoMST<char> resultMST = graphMST.KruskalMST();
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std::cout << "Finding MST using Kruskal's...\n\n";
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resultMST.Print();
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}
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