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