Monthly Archives: February 2017

Aaron 2.28.17

9:00 – BRC

  • TensorFlow
    • Installed following TF installation guide.
    • Found issues with the install instructions almost immediately. Found this link  with a suggestion that I followed to get it installed.
    • Almost immediately found that the Hello World example succeeded with a list of errors. Apparently its a known issue for the release candidate which was just fixed in the nightly build as per this link.
    • I haven’t had a chance to try it yet, but found a good Reddit link for a brief TF tutorial.
    • I went through the process of trying to get my IntelliJ project to connect and be happy with the Python interpreter in my Anaconda install, and although I was able to RUN the TF tutorials, it was still acting really wacky for features like code completion. Given Phil was able to get up and running with no problems doing a direct pip install to local Python, I scrapped my intent to run through Anaconda and did the local install. Tada! Everything is working fine now.
  • Unsupervised Learning (Clustering)
    • Our plan is to implement our unsupervised learning for the IH customer in an automated fashion by writing a MR app dispatched by MicroService that populates a Protobuf matrix for TensorFlow.
    • The trick about this is that there is no built in density-based clustering algorithm native for TF like the DBSCAN we used on last sprint’s deliverable. TF supports K-Means “out of the box” but with the high number of dimensions in our data set this isn’t ideal. Here is a great article explaining why.
    • However, one possible method of successfully utilizing K-Means (or improving the scalability of DBSCAN is to convert our high dimensional data to polar coordinates. We’ll be investigating this once we’ve comfortable with TensorFlow’s matrix math operations.
  • Proposal Work
    • Spent a fun hour of my day converting a bunch of content from previous white-papers and RFI documents into a one-page write-up of our Cognitive Computing capabilities. Ironically the more we have to write these the easier it gets because I’ve already written it all before. Also more importantly as time goes by more and more of the content describes things we’ve actually done instead of things we have in mind to do.
Advertisements

Phil 2.28.17

7:00 – 8:30 Research

  • Sent a note to Don about getting together on Thursday
  • Added to the list of journals and conferences. Included the Journal of Political Philosophy, which is where The Law of GP was originally published

9:00 – 4:30 BRC

  • Installing Tensorflow as per the instructions for CUDA and Anaconda
    • May need to install Python 2.7 version due to TF incompatibility with prebuilt python distros
    • Looks like I need visual studio for CUDA support, which is excessive. Going to try the CPU-only version
    • Installing Anaconda3-4.3.0.1-Windows-x86_64.exe.  Nope, based on Aaron’s experiences, I’m going to install natively
  • Tensorflow native pip installation
    • Uninstalled old Python
    • Installed python-3.5.2-amd64.exe from here
    • Did the cpu install:
       pip3 install --upgrade tensorflow
    • Ran the ‘hello world’ program
      import tensorflow as tf
      hello = tf.constant('Hello, TensorFlow!')
      sess = tf.Session()
      print(sess.run(hello))
    • Success!!!
      E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "BestSplits" device_type: "CPU"') for unknown op: BestSplits
      E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "CountExtremelyRandomStats" device_type: "CPU"') for unknown op: CountExtremelyRandomStats
      E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "FinishedNodes" device_type: "CPU"') for unknown op: FinishedNodes
      E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "GrowTree" device_type: "CPU"') for unknown op: GrowTree
      E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "ReinterpretStringToFloat" device_type: "CPU"') for unknown op: ReinterpretStringToFloat
      E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "SampleInputs" device_type: "CPU"') for unknown op: SampleInputs
      E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "ScatterAddNdim" device_type: "CPU"') for unknown op: ScatterAddNdim
      E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "TopNInsert" device_type: "CPU"') for unknown op: TopNInsert
      E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "TopNRemove" device_type: "CPU"') for unknown op: TopNRemove
      E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "TreePredictions" device_type: "CPU"') for unknown op: TreePredictions
      E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "UpdateFertileSlots" device_type: "CPU"') for unknown op: UpdateFertileSlots
      b'Hello, Tensorflow!'
      >>>
    • The errors are some kind of cruft that has been fixed in the nightly build as per this thread
    • Got my new Python running in IntelliJ
    • Working through the tutorials. So far so good, and the support for matrices is very nice
    • Some Tensorflow stuff from O’Reilly
    • Some proposal work slinging text on cognitive computing

Phil 2.27.17

7:00 – 8:30 Research

  • Call Senate, house about CBP, SCOTUS, etc
  • Add Antibelief, Leader, etc. mentions to Future work

9:00 – 6:00 BRC

Phil 2.24.17

7:00 – 8:00 Research

8:30 – 4:30 BRC

Phil 2.23.17

7:00 – 8:00 Research

  • Working on paper. Telling the story about how clustering needs to be done both as snapshot and over time using DTW

8:30 – 4:00 BRC

Phil 2.22.17

7:00 – 2:00 Research

  • Starting full paper
  • Finished porting abstract into gdocs
  • Working on adding the DTW work. Building charts. Lots of charts.

2:00 – 6:00 BRC

  • Worked with Aaron on accessing the classifier microservice
  • Writing up DTW as a mechanism for predicting behaviors
  • Found my old scripting engine code. Need to download and check

Phil 2.21.17

7:00 – 12:00 Research

import net.sf.javaml.distance.fastdtw.dtw.FastDTW;
import net.sf.javaml.distance.fastdtw.timeseries.TimeSeries;
import net.sf.javaml.distance.fastdtw.timeseries.TimeSeriesPoint;

TimeSeries tsI = new TimeSeries(1);
TimeSeries tsJ = new TimeSeries(1);

TimeSeriesPoint tspI;
TimeSeriesPoint tspJ;

double t = 0;
double offset = 0.0;
double amplitude = 2.0;
double step = 0.1;
while(t < 10) {
    double[] v1 = {Math.sin(t)};
    double[] v2 = {Math.sin(t+offset)*amplitude};
    tspI = new TimeSeriesPoint(v1);
    tspJ = new TimeSeriesPoint(v2);
    tsI.addLast(t, tspI);
    tsJ.addLast(t, tspJ);

    t += step;
}

System.out.println("FastDTW.getWarpDistBetween(tsI, tsJ) = "+FastDTW.getWarpDistBetween(tsI, tsJ));
FastDTW.getWarpDistBetween(tsI, tsJ) = 46.33334518229166
  • Note that the measure can be through all of the dimensions, so this may take some refactoring
  • Next step is to add this to the FlockRecorder class and output to excel and ARFF. I think this should replace the ‘deltas’ outputs. Done!
  • Running DBSCAN clustering in WEKA on the outputs
    • All Exploit – Social Radius = 0: All NOISE
    • All Exploit – Social Radius = 0.1 ALL NOISE
    • All Exploit – Social Radius = 0.2 (32 NOISE)
      === Model and evaluation on training set ===
      
      Clustered Instances
      
      0       68 (100%)
      
      Unclustered instances : 32
      
      Class attribute: AgentBias_
      Classes to Clusters:
      
        0  -- assigned to cluster
       68 | EXPLOITER
      
      Cluster 0 -- EXPLOITER
      
      Incorrectly clustered instances :	0.0	  0      %
    • All Exploit – Social Radius = 0.4 (86 NOISE)
      == Model and evaluation on training set ===
      
      Clustered Instances
      
      0       14 (100%)
      
      Unclustered instances : 86
      
      Class attribute: AgentBias_
      Classes to Clusters:
      
        0  -- assigned to cluster
       14 | EXPLOITER
      
      Cluster 0 -- EXPLOITER
      
      Incorrectly clustered instances :	0.0	  0      %
    • All Exploit – Social Radius = 0.8 (41 NOISE)
      === Model and evaluation on training set ===
      
      Clustered Instances
      
      0       45 ( 76%)
      1        7 ( 12%)
      2        7 ( 12%)
      
      Unclustered instances : 41
      
      Class attribute: AgentBias_
      Classes to Clusters:
      
        0  1  2  -- assigned to cluster
       45  7  7 | EXPLOITER
      
      Cluster 0 -- EXPLOITER
      Cluster 1 -- No class
      Cluster 2 -- No class
      
      Incorrectly clustered instances :	14.0	 14      %
    • All Exploit – Social Radius = 1.6 (51 NOISE)
      === Model and evaluation on training set ===
      
      Clustered Instances
      
      0       49 (100%)
      
      Unclustered instances : 51
      
      Class attribute: AgentBias_
      Classes to Clusters:
      
        0  -- assigned to cluster
       49 | EXPLOITER
      
      Cluster 0 -- EXPLOITER
      
      Incorrectly clustered instances :	0.0	  0      %
    • All Exploit – Social Radius = 3.2 (9 NOISE)
      === Model and evaluation on training set ===
      
      Clustered Instances 
      
      0       91 (100%)
      
      Unclustered instances : 9
      
      Class attribute: AgentBias_
      Classes to Clusters:
      
        0  -- assigned to cluster
       91 | EXPLOITER
      
      Cluster 0 -- EXPLOITER
      
      Incorrectly clustered instances :	0.0	  0      %
    • All Exploit – Social Radius = 6.4 (8 NOISE)
      === Model and evaluation on training set ===
      
      Clustered Instances
      
      0       86 ( 93%)
      1        6 (  7%)
      
      Unclustered instances : 8
      
      Class attribute: AgentBias_
      Classes to Clusters:
      
        0  1  -- assigned to cluster
       86  6 | EXPLOITER
      
      Cluster 0 -- EXPLOITER
      Cluster 1 -- No class
      
      Incorrectly clustered instances :	6.0	  6      %
      
    • All Exploit – Social Radius = 10
      === Model and evaluation on training set ===
      
      Clustered Instances
      
      0       82 ( 91%)
      1        8 (  9%)
      
      Unclustered instances : 10
      
      Class attribute: AgentBias_
      Classes to Clusters:
      
        0  1  -- assigned to cluster
       82  8 | EXPLOITER
      
      Cluster 0 -- EXPLOITER
      Cluster 1 -- No class
      
      Incorrectly clustered instances :	8.0	  8      %
  • So what this all means is that the DTW produces reasonable data that can be used for clustering. The results seem to match the plots. I think I can write this up now…

12:00 – 5:00 BRC

  • Clustering discussions with Aaron
  • GEM Meeting

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

Phil 2.17.17

7:00 – 8:00 research

  • I think I want to navigate the information space of Trump’s tweets
  • Still working on how to classify an agent. After struggling a bit, I can classify very well if I eliminate extraneous infor from mean angle stats, leaving only bias and variance

8:30 – 10:30, 4:00 – 5:00

  • Working on creating, extracting and classifying cluster membership from flocks.
  • Had to leave early to help Barbara with Buck
  • Discussed exec summary with Aaron. Will write on Monday

Phil 2.16.17

7:00 – 8:00 Research

  • Had a great time NOT DOING ANY THINKING yesterday
  • Rechecking the velocity comparison matrix. It’s correct. Looking at multiplying or adding relative position vs relative velocity
  • Sent a few charts to Don to see if he can make anything pretty
  • Uploaded new version

8:30 – 5:00 BRC

Phil 2.14.17

7:00 – 8:00 Research

  • Based on the charts from yesterday, I think I’m going to build two matrices to point WEKA at. Essentially, theses matrices will be filled with meta-cluster information
    • Average distance from agent to agent. Tightly clustered agents should have low average distances. DBSCAN should also work on this, as well as bootstrapping. That should cover this case: exploit_vs_exploit
    • Average velocity from agent to agent. I’m not sure what I’ll get from this, but in looking at the explore-explore case and the explore-exploit case, it strikes me that there may be some difference that is meaningful. And in the exploit-exploit case, the velocities should be near zero explore_vs_exploit Explore-exploitexplore_vs_explore Explore-explore
    • Start with Excel, and then add an ARFF
      • Got most of the methods built. Might finish this morning at work.
      • Indeed, you can get a lot done when you’re sitting in on a Skype meeting and they’re not talking about your part…
      • Ok, so I’ve added comparison matrices as Excel and ARFF output. In this case WEKA does better charting, so here goes. The first chart is exploit-exploit. Note that the majority of points are at 0,0: explit-exploit-deltas Next, an explore-exploit. In this case, there’s a cluster on the left side of the chart: exploit-explore-deltasLast, is the explore-explore chart, which has a cluster towards the middle: explore-explore-deltas
      • This data also seems to be good to train a NaiveBayes Classifier. Here’s the result of an initial run:
        === Stratified cross-validation ===
        === Summary ===
        
        Correctly Classified Instances          97               97      %
        Incorrectly Classified Instances         3                3      %
        Kappa statistic                          0.94  
        Mean absolute error                      0.03  
        Root mean squared error                  0.1732
        Relative absolute error                  6      %
        Root relative squared error             34.6337 %
        Total Number of Instances              100     
        
        === Detailed Accuracy By Class ===
        
                         TP Rate  FP Rate  Precision  Recall   F-Measure  MCC      ROC Area  PRC Area  Class
                         1.000    0.059    0.942      1.000    0.970      0.942    0.971     0.942     EXPLORER
                         0.941    0.000    1.000      0.941    0.970      0.942    1.000     1.000     EXPLOITER
        Weighted Avg.    0.970    0.029    0.972      0.970    0.970      0.942    0.986     0.972     
        
        === Confusion Matrix ===
        
          a  b   -- classified as
         49  0 |  a = EXPLORER
          3 48 |  b = EXPLOITER
      • Velocity also works, the plots aren’t as crisp, but the classifier accuracy is about the same: exploit-exploit-velocity Exploit-Exploit exploit-explore-velocity Explore-Exploit explore-explore-velocity Explore-Explore
      • Again, classification looks good:
        === Stratified cross-validation ===
        === Summary ===
        
        Correctly Classified Instances          99               99      %
        Incorrectly Classified Instances         1                1      %
        Kappa statistic                          0.98  
        Mean absolute error                      0.01  
        Root mean squared error                  0.1   
        Relative absolute error                  2      %
        Root relative squared error             19.9957 %
        Total Number of Instances              100     
        
        === Detailed Accuracy By Class ===
        
                         TP Rate  FP Rate  Precision  Recall   F-Measure  MCC      ROC Area  PRC Area  Class
                         1.000    0.020    0.980      1.000    0.990      0.980    1.000     1.000     EXPLORER
                         0.980    0.000    1.000      0.980    0.990      0.980    1.000     1.000     EXPLOITER
        Weighted Avg.    0.990    0.010    0.990      0.990    0.990      0.980    1.000     1.000     
        
        === Confusion Matrix ===
        
          a  b   -- classified as
         49  0 |  a = EXPLORER
          1 50 |  b = EXPLOITER
    • Uploaded new version of the tool to philfeldman.com/GroupPolarization/GroupPloarizationModel.jar

8:30 – 3:30. BRC

  • Either start on the ResearchBrowser or continue with meta-clustering.
  • Grooming and sprint planning today – done! And good progress while hanging out on the phone.

Phil 2.13.17

7:00 – 8:00, 3:00 – 5:30 Research

  • Getting the toArff methods working with the WEKA date format: The default format string accepts the ISO-8601 combined date and time format: yyyy-MM-dd'T'HH:mm:ss
  • Going to go with Milliseconds format, and then set the ARFF to default to 1 sec increments. Nope, that didn’t work. Going with the default fromat and incrementing by seconds.
  • Fika
  • Meeting with Wayne. Basically catching up, but I also got to show him the sim. And for a numerical model of community behavior based on sociophysics, we got into a very involved conversation. Visualization helps tell stories.

8:30 – 2:30 BRC

  • Testing periodicity and time series classifiers
  • I can forecast in a bunch of different ways, but I can’t seem to classify. There are several filters, but no real explanation
  • Redoing some simulations, to see the best way of checking for Explorer/exploiter behavior. Here are some screenshots:
  • Explore-Explore explore_vs_explore
  • Explore-Exploit explore_vs_exploit
  • Exploit-Exploit exploit_vs_exploit

Phil 2.10.17

7:00 – 8:30 Research

  • Adding the ability to set maxSlew and slew Variance on init. Can comment out particle selection. Done!
  • Commenting out particle to save GUI space Done!
  • Using ATLAS.ti 8 Windows in Literature Reviews
  • Uploaded the newest version and pinged Don.

10:00 – 5:00 BRC

  • The Porsche didn’t start, so I had to bike out and get the Honda. Brrr!
  • Submit for travel expenses
  • Learning how to do time series in WEKA
  • Looks like I need to rotate the matrix so that each column is an agent, and each row is a time step. Yep. Done. Had to add a case to toArff() where there are no row names
  • Discovered the arff viewer, which is awesome
  • Discovered the plugin manager. Nice. wrappers for other AI packages!
  • Predictive Learning. You just need to read in the file, then use the Forecast tabselect the time column and then the items you want to track: flockingprediction

Phil 2.9.17

7:00 – 8:30, 4:00 – 5:00  Research

8:30 – 3:30 BRC

  • Assembling notes from the trip – done
  • Starting to work on automated clustering. Nope Jira tasking. Done enough…?

Phil 2.8.17

8:00 – 8:00 BRC