Phil 2.20.17

7:00 – 11:00 Research

  • PathNet article and paper. Using genetic techniques to produce better NN systems. GAs are treated like gradient descent. Which makes sense, as gradient descent and hillclimbing are pretty much the same thing
    • “Since scientists started building and training neural networks, Transfer Learning has been the main bottleneck. Transfer Learning is the ability of an AI to learn from different tasks and apply its pre-learned knowledge to a completely new task. It is implicit that with this precedent knowledge, the AI will perform better and train faster than de novo neural networks on the new task.”
  • Adding angle and mean deltas. Interesting results, but still not sure on the best approach to classify…
  • Newest version is at philfeldman.com/GroupPolarization
  • So here’s a pretty typical population. It’s 10% Explorer, 90% Exploiter. Exploit social influence radius is 0.2. These settings produce an orbiting flock. Between-group interaction is allowed, so This is a grid where the accumulated relationship of each agent to every other agent is shown. Red is closest, green is farthestcolorizedpositions You can see the different populations pretty well. One thing that isn’t that obvious is that exploiters are on average slightly closer to each other than to exploiters.
  • A more extreme example is where the Exploit influence distance is 10: colorizedpositions2 These tables show just relative position when compared to the origin.
  • Although I can’t figure out how to classify using this data, clustering works pretty well. This is Canopy (WEKA) on the top dataset above: 
    === Run information ===

Scheme: weka.clusterers.Canopy -N -1 -max-candidates 100 -periodic-pruning 10000 -min-density 2.0 -t2 -1.0 -t1 -1.25 -S 1
Relation: ORIGIN_POSITION_DELTA
Instances: 100
Attributes: 102
[list of attributes omitted]
Test mode: Classes to clusters evaluation on training data

=== Clustering model (full training set) ===

Canopy clustering
=================

Number of canopies (cluster centers) found: 2
T2 radius: 3.137
T1 radius: 3.922

Cluster 0: 0.283631,0.443357,0.240249,0.280277,0.396611,0.258673,0.28608,0.27558,0.312295,0.215801,0.249255,0.25779,0.280719,0.273191,0.58818,0.258901,0.196191,0.240405,0.201927,0.273491,0.271862,0.266807,0.249377,0.269756,0.265874,0.252873,0.299417,0.244208,0.284257,0.253868,0.234348,0.213578,0.242031,0.248292,0.215259,0.236993,0.301843,0.245444,0.282464,0.290885,0.216585,0.375846,0.223493,0.278251,0.375965,0.764462,0.338657,0.280672,0.316447,0.261622,0.265026,0.436098,0.246442,0.246887,0.289306,0.470806,0.43541,0.209845,0.220971,0.21506,0.247576,0.249173,0.468053,0.28907,0.418987,0.293851,0.452858,0.267638,0.243671,0.248868,0.242674,0.371534,0.29843,0.221506,0.25575,0.242182,0.335877,0.28386,0.303986,0.235298,0.282083,0.427425,0.26635,0.251009,0.304134,0.281157,0.212644,0.367693,0.222213,0.247862,0.780248,0.894699,0.713413,0.865287,0.826024,0.868741,0.757008,0.807287,0.785141,0.756071,{88}
Cluster 1: 0.919922,0.669721,0.908035,0.73578,0.591465,0.752733,0.774358,0.826861,0.84364,0.884803,0.939301,0.958981,0.629587,0.76459,0.545587,0.715267,0.853073,0.803545,0.851979,0.693952,0.954557,0.703606,0.897206,0.698297,0.926263,0.91898,0.733686,0.818759,0.763319,0.776199,0.843167,0.811708,0.903011,0.814435,0.804113,0.916336,0.639919,0.779399,0.663897,0.754696,0.77482,0.682512,0.832556,0.764008,0.703999,0.513612,0.693526,0.734279,0.723504,0.903016,0.777757,0.597915,0.86509,0.900357,0.724636,0.648915,0.577278,0.883327,0.828117,0.813873,0.860062,0.915821,0.684886,0.979451,0.556747,0.667678,0.556487,0.941671,0.898276,0.902846,0.686763,0.664381,0.709607,0.706246,0.890753,0.898794,0.588379,1.001214,0.625244,0.761188,0.828436,0.661864,0.759379,0.944355,0.728272,0.764909,0.761139,0.65028,0.845547,0.87213,0.586679,0.500194,0.498893,0.513267,0.493026,0.58192,0.620756,0.469854,0.540532,0.496272,{12}

Time taken to build model (full training data) : 0.03 seconds

=== Model and evaluation on training set ===

Clustered Instances

0 88 ( 88%)
1 12 ( 12%)

Class attribute: AgentBias_
Classes to Clusters:

0 1 -- assigned to cluster
0 10 | EXPLORER
88 2 | EXPLOITER

Cluster 0 -- EXPLOITER
Cluster 1 -- EXPLORER

Incorrectly clustered instances : 2.0 2 %
  • The next analyses is on the second dataset. They are essentially the same, even though the differences are more dramatic (the tight clusters are very tight 
    === Run information ===

Scheme:       weka.clusterers.Canopy -N -1 -max-candidates 100 -periodic-pruning 10000 -min-density 2.0 -t2 -1.0 -t1 -1.25 -S 1
Relation:     ORIGIN_POSITION_DELTA
Instances:    100
Attributes:   102
              [list of attributes omitted]
Test mode:    Classes to clusters evaluation on training data

=== Clustering model (full training set) ===


Canopy clustering
=================

Number of canopies (cluster centers) found: 2
T2 radius: 3.438     
T1 radius: 4.297     

Cluster 0: 0.085848,0.050964,0.0513,0.053288,0.05439,0.054653,0.21758,0.057725,0.058775,0.050894,0.053768,0.130821,0.051098,0.050923,0.051115,0.050893,0.051012,0.051009,0.060649,0.051454,0.051089,0.051032,0.050894,0.053364,0.276684,0.051857,0.050984,0.050942,0.0509,0.050952,0.051025,0.056953,0.050914,0.050962,0.050903,0.052129,0.128196,0.051023,0.054222,0.274438,0.053978,0.050934,0.051124,0.054563,0.050995,0.074289,0.051077,0.05094,0.053644,0.050941,0.051343,0.050967,0.062704,0.052333,0.050936,0.051013,0.050922,0.051007,0.051038,0.050899,0.501239,0.051574,0.051005,0.050898,0.050944,0.204398,0.06076,0.050947,0.050904,0.408553,0.051263,0.0511,0.051574,0.069173,0.050997,0.162314,0.051353,0.096523,0.498648,0.339103,0.051125,0.050888,0.051002,0.051124,0.080711,0.05105,0.051024,0.050988,0.100492,0.132793,0.630178,0.882598,0.832132,0.86452,0.55151,0.729317,0.755526,0.513822,0.782104,0.768836,{92} 
Cluster 1: 0.799117,0.793729,0.79643,0.7929,0.797843,0.797642,0.709935,0.78817,0.805937,0.794095,0.7972,0.76062,0.793743,0.79418,0.794846,0.794247,0.794677,0.793599,0.800359,0.794787,0.793849,0.793805,0.793613,0.784762,0.774656,0.79547,0.794308,0.793527,0.794406,0.793292,0.793513,0.800151,0.793775,0.793652,0.794123,0.793645,0.73331,0.794506,0.788542,0.710244,0.793332,0.793313,0.794184,0.801119,0.79448,0.802416,0.793669,0.7947,0.794813,0.794533,0.796484,0.794512,0.797614,0.794607,0.793716,0.793642,0.793548,0.794789,0.793551,0.793989,0.539133,0.79391,0.793443,0.793969,0.794472,0.715896,0.790956,0.794494,0.794293,0.678147,0.79434,0.793611,0.794221,0.802197,0.793753,0.759132,0.794164,0.798071,0.55929,0.698333,0.79444,0.79424,0.793585,0.793581,0.779958,0.79394,0.793567,0.794795,0.764686,0.754727,0.482214,0.518683,0.434538,0.501648,0.790616,0.4855,0.464554,0.691735,0.405411,0.496892,{8} 



Time taken to build model (full training data) : 0.01 seconds

=== Model and evaluation on training set ===

Clustered Instances

0       88 ( 88%)
1       12 ( 12%)


Class attribute: AgentBias_
Classes to Clusters:

  0  1  -- assigned to cluster
  0 10 | EXPLORER
 88  2 | EXPLOITER

Cluster 0 -- EXPLOITER
Cluster 1 -- EXPLORER

Incorrectly clustered instances :	2.0	  2      %
  • Online clustering, fear and uncertainty in Egypt’s transition (Published today). Wow. Downloaded

11:00 – 6:00 BRC

  • Spent the rest of the day working on the CHIMERA paper with Aaron