Phil 12.23.16

7:00 – 8:00 Research

8:30 – 4:00 BRC

  • Reading in a spreadsheet to GPM -done. Ok results, not great clustering
  • Tried adjusting the threshold and adding antibelief, but other than helping the refresh rate, no joy. I think I need a different distance/similarity calculation
    • Hops. Every agent is connected over the network by going through flag nodes. We could literally draw the node/agent network, and count the hops in an adjacency matrix
    • N-dimensional cartesian. As I recall, this is close to what I have already. It’s closely related to n-dimensional flocking, so I’m going to get that running anyway so that I can measure distance between/within flocks
    • Cosine similarity. I think that this is a good approach since it decomposes the dimensions in a useful way, particularly for sparse matrices.
    • There is similarity there, but not enough distance to make anything emerge. Pretty pix though.
  • integrityagents

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