7:00 – 8:00

• Systematic exploration of unsupervised methods for mapping behavior
• Thinking about the stories I can tell with the GP sim.
  • Start together with same settings.
  • Disconnect
  • Slide exploit to max
• Need to download blog entries
• Working on graphing. Success!!!!! Now I need to discriminate agents from clusters, and exploit from explore. But this shows polarized vs. diverse clustering. I’m pretty sure I can get all kinds of statistics out of this too!
• Better version. Ran all the permutations:
• explore_1.6_exploit_3.2_ran 04_14_17-08_38_48. Green are clusters, Red are Exploit, Blue are Explore
• Need to make the line width based on the time spent in the cluster, and the cluster size a function of its lifespan

9:00 – 5:00 BRC

• Working on showing where the data broke. Looks like Talend
• For future referrence, How to turn a dict of rows into a DataFrame and then how to access all the parts:

  import pandas as pd
  
  d1 = {'one':1.1, 'two':2.1, 'three':3.1}
  d2 = {'one':1.2, 'three':3.2}
  d3 = {'one':1.3, 'two':2.3, 'three':3.3}
  rows = {'row1':d1, 'row2':d2}
  rows['row3'] = d3
  df = pd.DataFrame(rows)
  df = df.transpose()
  print(df)
  
  for index, row in df.iterrows():
      print(index)
      for key, val in row.iteritems():
          print("{0}:{1}".format(key, val))

• Helped Aaron with the writeups
• And it turns out that all the work I did “could be done in an hour”. So back to clustering and AI work. If there is a problem with the data, I know that it works with the test data. Others can figure out where the problem is, since they can handle it so quickly.