Phil 7.23.20

Amid a tense meeting with protesters, Portland Mayor Ted Wheeler tear-gassed by federal agents

GPT-2 Agents

  • Good back-and-forth with Antonio about venues
  • It struck me that statistical tests about fair dice might give me a way of comparing the two populations. Pieces are roughly equivalent to dice sides. Looking at this post on the RPG Stackexchange. That led me to Pearson’s Chi-square test (which rang a bell as the sort of test I might need).
  • Success! Here’s the code:
    from scipy.stats import chisquare, chi2_contingency
    from scipy.stats.stats import pearsonr
    import pandas as pd
    import numpy as np
    
    gpt = [51394,
           25962,
           19242,
           23334,
           15928,
           19953]
    
    twic = [49386,
            31507,
            28263,
            31493,
            22818,
            23608]
    
    z, p = chisquare(f_obs=gpt,f_exp=twic)
    print("z = {}, p = {}".format(z, p))
    
    ar = np.array([gpt, twic])
    print("\n",ar)
    
    df = pd.DataFrame(ar, columns=['pawns', 'rooks', 'bishops', 'knights', 'queen', 'king'], index=['gpt-2', 'twic'])
    print("\n", df)
    
    z,p,dof,expected=chi2_contingency(df, correction=False)
    print("\nNo correction: z = {}, p = {}, DOF = {}, expected = {}".format(z, p, dof, expected))
    
    z,p,dof,expected=chi2_contingency(df, correction=True)
    print("\nCorrected: z = {}, p = {}, DOF = {}, expected = {}".format(z, p, dof, expected))
    
    cor = pearsonr(gpt, twic)
    print("\nCorrelation = {}".format(cor))
    
    
  • Here’s the results:
    "C:\Program Files\Python\python.exe" C:/Development/Sandboxes/GPT-2_agents/gpt2agents/analytics/pearsons.py
    z = 8696.966788178523, p = 0.0
    
     [[51394 25962 19242 23334 15928 19953]
     [49386 31507 28263 31493 22818 23608]]
    
            pawns  rooks  bishops  knights  queen   king
    gpt-2  51394  25962    19242    23334  15928  19953
    twic   49386  31507    28263    31493  22818  23608
    
    No correction: z = 2202.2014776980245, p = 0.0, DOF = 5, expected = [[45795.81128532 26114.70012657 21586.92215826 24914.13916789 17606.71268169 19794.71458027]
     [54984.18871468 31354.29987343 25918.07784174 29912.86083211 21139.28731831 23766.28541973]]
    
    Corrected: z = 2202.2014776980245, p = 0.0, DOF = 5, expected = [[45795.81128532 26114.70012657 21586.92215826 24914.13916789 17606.71268169 19794.71458027]
     [54984.18871468 31354.29987343 25918.07784174 29912.86083211 21139.28731831 23766.28541973]]
    
    Correlation = (0.9779452546334226, 0.0007242538456558558)
    
    Process finished with exit code 0
    

     

  • It might be time to start writing this up!

GOES

  • Found vehicle orientation mnemonics: GNC_AD_STA_FUSED_QRS#

2020-07-23

  • 11:00 Meeting with Erik and Vadim about schedules. Erik will send an update. The meeting went well. Vadim’s going to exercise the model through a set of GOTO ANGLE 90 / GOTO ANGLE 0 for each of the rwheels, and we’ll see how they map to the primary axis of the GOES

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