Took the motorcycle for its weekly spin and rode past the BWI terminal. By far the most Zombie Apocalypse thing I’ve seen so far.

The repository contains an ongoing collection of tweets IDs associated with the novel coronavirus COVID-19 (SARS-CoV-2), which commenced on January 28, 2020.

D20

• Reworked regression code to only use the last 14 days of data. It seems to take the slowing rate change into account better
• That could be a nice interactive feature to add to the website. A js version of regression curve fitting is here.

ACSOS

• Got Antonio’s revisions back and enbiggened the two chats for better readability

GPT-2 Agents

• Going to try the GPT-2 Client and see how it works.
• Whoops, needs TF 2.1. Upgraded that and the drivers – done

GOES

• Step through the GAN code and look for ways of restricting the latent space to being near the simulation output
• Here’s the GAN trying to fit a bit of a sin wave from the beginning of the day
• And here’s the evolution of the GAN using hints and 5 latent dimensions from the end of the day:
• And here are the accuracy outputs:

  epoch = 399, real accuracy = 87.99999952316284%, fake accuracy = 37.99999952316284%
  epoch = 799, real accuracy = 43.99999976158142%, fake accuracy = 56.99999928474426%
  epoch = 1199, real accuracy = 81.00000023841858%, fake accuracy = 25.999999046325684%
  epoch = 1599, real accuracy = 81.00000023841858%, fake accuracy = 40.99999964237213%
  epoch = 1999, real accuracy = 87.99999952316284%, fake accuracy = 25.999999046325684%
  epoch = 2399, real accuracy = 89.99999761581421%, fake accuracy = 20.000000298023224%
  epoch = 2799, real accuracy = 87.00000047683716%, fake accuracy = 46.00000083446503%
  epoch = 3199, real accuracy = 80.0000011920929%, fake accuracy = 47.999998927116394%
  epoch = 3599, real accuracy = 76.99999809265137%, fake accuracy = 43.99999976158142%
  epoch = 3999, real accuracy = 68.99999976158142%, fake accuracy = 30.000001192092896%
  epoch = 4399, real accuracy = 75.0%, fake accuracy = 33.000001311302185%
  epoch = 4799, real accuracy = 63.999998569488525%, fake accuracy = 28.00000011920929%
  epoch = 5199, real accuracy = 50.0%, fake accuracy = 56.00000023841858%
  epoch = 5599, real accuracy = 36.000001430511475%, fake accuracy = 56.00000023841858%
  epoch = 5999, real accuracy = 49.000000953674316%, fake accuracy = 60.00000238418579%
  epoch = 6399, real accuracy = 34.99999940395355%, fake accuracy = 58.99999737739563%
  epoch = 6799, real accuracy = 70.99999785423279%, fake accuracy = 43.00000071525574%
  epoch = 7199, real accuracy = 70.99999785423279%, fake accuracy = 30.000001192092896%
  epoch = 7599, real accuracy = 47.999998927116394%, fake accuracy = 50.0%
  epoch = 7999, real accuracy = 40.99999964237213%, fake accuracy = 52.99999713897705%
  epoch = 8399, real accuracy = 23.000000417232513%, fake accuracy = 82.99999833106995%
  epoch = 8799, real accuracy = 23.000000417232513%, fake accuracy = 75.0%
  epoch = 9199, real accuracy = 31.00000023841858%, fake accuracy = 69.9999988079071%
  epoch = 9599, real accuracy = 37.99999952316284%, fake accuracy = 68.00000071525574%
  epoch = 9999, real accuracy = 23.000000417232513%, fake accuracy = 83.99999737739563%
  

• Found a bug in the short-regression code. Need to roll in the fix
• Here’s the working code:

  slope, intercept, r_value, p_value, std_err = stats.linregress(xsub, ysub)
  # slope, intercept = np.polyfit(x, y, 1)
  yn = np.polyval([slope, intercept], xsub)
  
  steps = 0
  if slope < 0:
      steps = abs(y[-1] / slope)
  
  reg_x = []
  reg_y = []
  start = len(yl) - max_samples
  yval = intercept + slope * start
  for i in range(start, len(yl)-offset):
      reg_x.append(i)
      reg_y.append(yval)
      yval += slope