Phil 5.10.17

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!!!!! figure_1Now 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_16_Exploit_32_04_14_17-08_38_48explore_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()
    for index, row in df.iterrows():
        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.

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