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).
  • https://stackoverflow.com/questions/3949226/calculating-pearson-correlation-and-significance-in-python
  • https://medium.com/analytics-vidhya/pearsons-chi-squared-test-from-scratch-with-python-ba9e14d336c
  • https://stackoverflow.com/questions/29866961/pearsons-chi-square-test-python
• 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#

• 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