phantonull / minimum-spanning-trees-prolog

Minimum Spanning Tree in Prolog

Minumum Spanning Trees in Prolog

Introduction

A problem that often appears in various guises is connecting different "points" in an "equivalent" way, for example connecting them with threads but without creating loops.

Another typical problem is to calculate the shortest route in a map point to point.

There are several algorithms capable of solving these known problems as the "Mininum Spanning Tree".

The purpose of this project is to implement Prim's algorithm for the solution of the MST problem for non-directed and connected graphs with non-negative weights.

To proceed with the implementation of these algorithms it is necessary produce an implementation of a MINHEAP (or MIN-PRIORITY-QUEUE).

My solution to the problem strives to maximize memory and time efficiency while trying to offer a solid structure of predicates.

An example of this effort are predicates as graph_arcs, graph_vertices or adjs. To grant their robustness these predicates evaluate permutations of lists passed as arguments. To maximize efficiency permutation are taken into account only when lists are passed as non-var.

Instructions

This program outputs a Minimum Spanning Tree (or more) for a given graph. Works with unconnected graphs.

[ Loading a graph ]

Before any operation can be done you need to have a graph to work on. You can load a graph in memory by providing a .csv file with tab separator. Each row represent an arc: vertex1 vertex2 weight

An example of .csv graph file is loaded in the repo.

Another randomly generated graph (prim_benchmark.csv) is available for efficiency benchmarks.

Alternatively it is possible to create and edit a graph manually through graph structure's predicates new_graph, new_vertex & new_arc. In this case, pre-existing conditions should be met for vertices or arcs to be declared. (Es. a graph should exist before declaring his vertex)

If an arc is already present in knowledgebase, the weight will be simply updated with the last value given.

Load a graph from file in interpreter by using read_graph predicate:

  read_graph(G, 'FilePath.csv').

[ Saving a graph ]

You may want to save a graph loaded in memory or after making changes to it. It is possible to save a graph to a file in two ways:

1. PASSING AN EXISTING GRAPH (graph mode)

   write_graph(graphname, 'FilePath.csv', 'graph').

Note: 3rd parameter is optional and 'graph' is its default value.

2. PASSING A LIST OF ARCS AS [arc(G, U, V, W), ...] (edge mode)

   write_graph(LIST, 'FilePath.csv', 'edge').

The file will be created in the enviroment working directory if absolute path is not provided.

[ Calculating MST ]

MST will be given as a list of arcs obtained by pre-order visiting the MST with radix Source (vertexname) and sorted first by weight and then by lexicographical order for nodes on same depth with a common parent. Source is the starting vertex of computation. An example of normal system interrogation may be:

   mst_get(graph_name, source, LIST).

It is also possible to obtain all the possible mst from all loaded graphs in memory starting from different vertices by passing only variables:

   mst_get(G, S, LIST).

It is possible to combine VAR and NONVAR parameter as wished to obtain any kind of combinational results.

[ Memory management ]

When a graph is no more needed, you may delete it with predicate:

   delete_graph(graphname).

This will delete delete everything related to the graph in memory.

[ HINT: Consulting long mst arc list ]

Consulting a long list of arcs in the interpreter may be problematic. If you want to have access to a more readable format you may save mst_get and mst_get_nested result list to a .csv file:

   mst_get(g, v, L), write_graph(L, 'preorder.csv', 'edge').

Credits

OA: https://github.com/PhantoNull

Feel free to use under MIT licensing.