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|Is billed to=|Has notes=|Has project status=ActiveSubsume|Is dependent on=|Depends upon itHas Image=Red-circle.jpg
Implemented === Use PostGIS === If the point of the project is to solve an application, the Not-Invented-Here approach is doomed to fail. Instead, I suggest an approach where # Load reverse geolocated into a PostGIS PostgreSQL table# Use [https://postgis.net/docs/ST_MinimumBoundingRadius.html ST_MinimumBoundingRadius] which is an implementation of a linear (read: very fast) minimum bounding circle algorithm. See https://github.com/postgis/postgis/blob/svn-trunk/liblwgeom/lwboundingcircle.c for implementation details.# Run queries against DB
Enclosing Circle Algorithm (view source)
Revision as of 13:47, 21 September 2020
, 13:47, 21 September 2020no edit summary
{{McNair ProjectsProject|Has project output=Tool|Has Imagesponsor=Red-circle.jpgMcNair Center
|Has title=Enclosing Circle Algorithm
|Has owner=Christy Warden, Peter Jalbert,|Has start date=201701January 2017|Has deadline=201704April 2017
|Has keywords=Tool
}}
The objective of this project is come up with a fast, reliable algorithm that finds the smallest circle area that can drawn around N Cartesian points such that each circle contains at least M<N points. This algorithm will be used in an academic paper on [[Urban Start-up Agglomeration]], where the points will represent venture capital backed firms within cities.
8) Choose the scheme that resulted in the smallest total area.
=K-Means Based Algorithm=
==Location==
The script is located in:
E:\McNair\Software\CodeBase\New Implement of Enclosing Circle (Constrained K Means, Smallest Circle)\enclosing_circle_new_implement.py
==Explanation==
The algorithm relies on an object oriented implementation of a "cluster". <br>
Each "cluster" has the following associated with it: <br>
1. area of minimum circle enclosing points in the cluster
2. points associated with the cluster
3. minimum circle associated with the cluster (x-coord,y-coord,radius)
4. "parent" cluster
5. "children" cluster(s)
'''Inputs:'''
1. n, the minimum number of points to include in a circle
2. k, the amount of clusters to split the points into
3. valid_nodes, an empty list to store the possible clusters to explore
4. explored_nodes, an empty list to store the explored clusters
5. cur_node, initialized to an object with all values set to none
6. end_nodes, initialized to an empty list used to store the clusters which cannot be split further
'''Algorithm:'''
1. Calculate the area of the minimum circle enclosing the total set of points
2. Split initial points into "k" clusters of at least "n" points using Constrained-K-Means Algorithm
3. construct minimum circles of "k" clusters and compute their total area
4. if this area <= area of the area of the first circle
5. Add all "k" clusters to "valid_nodes"
6. for node in valid_nodes, add node to explored_nodes, change cur_node to node, and update respective areas, parents, children, circle, points
7. run this algorithm again, change k = 2
8. if the cur_node cannot be split into 2, or the cur_node cannot be split into 2 circles with smaller areas than its parent, add cur_node to end_nodes and resume algorithm with next valid_node not in
explored_node as the cur_node
9. if all the valid_nodes are in explored_nodes, return end_nodes
10. repeat the above algorithm for all k from 2 to int(#number of total points/n) and take the end_nodes with the lowest total area
'''Runtime:'''
The runtime of the minimum enclosing circle is O(n), and the runtime of constrained k-means depends on k itself. Here, the value of k is proportional to the total number of points. <br>
We would expect the algorithm to slow as the number of points increase. For reference, a set of 80 points with n =3, took about 30 minutes. <br>
Further improvements could implement an early-stopping method to converge to a local optima. <br>
==Visualization ==
The K-Means based algorithm returns the optimal solution, albeit slower <br>
[[File:kmeans comparison.png]] [[File:kmeans random.png]] [[File:houston.png]]
=Brute Force=
The script is located in:
E:\McNair\Projects\Accelerators\Enclosing_Circle\enclosing_circle_brute_force.py
The Julia Script is located in:
E:\McNair\Software\CodeBase\Julia Code for enclosing circle
==Explanation==
On preliminary examples, the code works correctly. It actually runs slower than the original algorithm on small examples but I suspect that is the overhead of creating the threads and that they will be useful to the large examples.
==Runtime==
