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This function takes clustering data and plots it, if the data is 2d or 3d. The inputs are:
An m-by-d matrix of data points, where d is the number of dimension and m the number of data points
An m-by-1 vector of indices that associate each data point with a cluster.
A c-by-d matrix with c cluster centers. If not given, the function calculates them.
A c-by-3 matrix of colors, as generated by the hsv...
Finds a (near) optimal solution to a variation of the M-TSP (that has a variable number of salesmen) by setting up a GA to search for the shortest route (least distance needed or the salesmen to travel to each city exactly once and return to their...
The function is based on the paper by Held and Karp from 1962. The DP is guaranteed to provide the accurate (optimal) result to the TSP, but the time complexity of this algorithm is O(2^n n^2), which limits the use of this algorithm to 15 cities...
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