Phil 11.14.19

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

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