7:00 – 3:30 ASRC GOES

• Dissertation – Done with Human Study!
• Evolver
  • • Work on parameter passing and function storing
    • You can use the * operator before an iterable to expand it within the function call. For example:

      timeseries_list = [timeseries1 timeseries2 ...]
      r = scikits.timeseries.lib.reportlib.Report(*timeseries_list)

    • Here’s the running code with variable arguments

      def plus_func(v1:float, v2:float) -> float:
          return v1 + v2
      
      def minus_func(v1:float, v2:float) -> float:
          return v1 - v2
      
      def mult_func(v1:float, v2:float) -> float:
          return v1 * v2
      
      def div_func(v1:float, v2:float) -> float:
          return v1 / v2
      
      if __name__ == '__main__':
          func_array = [plus_func, minus_func, mult_func, div_func]
      
          vf = EvolveAxis("func", ValueAxisType.FUNCTION, range_array=func_array)
          v1 = EvolveAxis("X", ValueAxisType.FLOAT, parent=vf, min=-5, max=5, step=0.25)
          v2 = EvolveAxis("Y", ValueAxisType.FLOAT, parent=vf, min=-5, max=5, step=0.25)
      
          for f in func_array:
              result = vf.get_random_val()
              print("------------\nresult = {}\n{}".format(result, vf.to_string()))

    • And here’s the output

      ------------
      result = -1.0
      func: cur_value = div_func
      	X: cur_value = -1.75
      	Y: cur_value = 1.75
      ------------
      result = -2.75
      func: cur_value = plus_func
      	X: cur_value = -0.25
      	Y: cur_value = -2.5
      ------------
      result = 3.375
      func: cur_value = mult_func
      	X: cur_value = -0.75
      	Y: cur_value = -4.5
      ------------
      result = -5.0
      func: cur_value = div_func
      	X: cur_value = -3.75
      	Y: cur_value = 0.75

    • Now I need to get this to work with different functions with different arg lists. I think I can do this with an EvolveAxis containing a list of EvolveAxis with functions. Done, I think. Here’s what the calling code looks like:

      # create a set of functions that all take two arguments
      func_array = [plus_func, minus_func, mult_func, div_func]
      vf = EvolveAxis("func", ValueAxisType.FUNCTION, range_array=func_array)
      v1 = EvolveAxis("X", ValueAxisType.FLOAT, parent=vf, min=-5, max=5, step=0.25)
      v2 = EvolveAxis("Y", ValueAxisType.FLOAT, parent=vf, min=-5, max=5, step=0.25)
      
      # create a single function that takes no arguments
      vp = EvolveAxis("random", ValueAxisType.FUNCTION, range_array=[random.random])
      
      # create a set of Axis from the previous function evolve args
      axis_list = [vf, vp]
      vv = EvolveAxis("meta", ValueAxisType.VALUEAXIS, range_array=axis_list)
      
      # run four times
      for i in range(4):
          result = vv.get_random_val()
          print("------------\nresult = {}\n{}".format(result, vv.to_string()))

    • Here’s the output. The random function has all the decimal places:

      ------------
      result = 0.03223958125899473
      meta: cur_value = 0.8840652389671935
      ------------
      result = -0.75
      meta: cur_value = -0.75
      ------------
      result = -3.5
      meta: cur_value = -3.5
      ------------
      result = 0.7762888191296017
      meta: cur_value = 0.13200324934487906

    • Verified that everything still works with the EvolutionaryOptimizer. Now I need to make sure that the new mutations include these new dimensions

   

• I think I should also move TF2OptimizationTestBase to TimeSeriesML2?
• Starting Human Compatible

“Everything that we see is a shadow cast by that which we do not see.” – Dr. King

ASRC GOES 7:00 – 4:30

• Dissertation – more human study. Pretty smooth progress right now!
• Cleaning up the sim code for tomorrow – done. All the prediction and manipulation to change the position data for the RWs and the vehicle are done in the inference section, while the updates to the drawing nodes are separated.
• I think this is the code to generate GPT-2 Agents?: github.com/huggingface/transformers/blob/master/examples/run_generation.py

7:00 – 9:00 ASRC GOES

• Cool thing: Our World in Data
  • The goal of our work is to make the knowledge on the big problems accessible and understandable. As we say on our homepage, Our World in Data is about Research and data to make progress against the world’s largest problems.
• Dissertation – more human study
• This is super-cool: The Future News Pilot Fund: Call for ideas
  • Between February and June 2020 we will fund and support a community of changemakers to test their promising ideas, technologies and models for public interest news, so communities in England have access to reliable and accurate news about the issues that matter most to them.
• October status report
• Sim + ML next steps:
  • I can’t do ensemble realtime inference because I’d need a gpu for each model. This means that I need to get the best “best” model and use that
  • Run the evolver to see if something better can be found
  • Add “flywheel mass” and “vehicle mass” to dictionary and get rid of the 0.05 value – done
  • Set up a second model that uses the inferred efficiency to move in accordance with the actual commands. Have them sit on either side of the origin
    • Graphics are done
    • Need to make second control system and ‘sim’ that uses inferred efficiency. Didn’t have to do all that. What I’m really doing is calculating rw angles based on the voltage and inferred efficiency. I can take the commands from the control system for the ‘actual’ satellite.

• ML seminar
  • Showed the sim, which runs on the laptop. Then everyone’s status reports
• Meeting with Aaron
  • Really good discussion. I think I have a handle on the paper/chapter. Added it to the ethical considerations section