def hellinger_distance( outputs, targets, derivative=False ):
    root_difference = np.sqrt( outputs ) - np.sqrt( targets )
    
    if derivative:
        return root_difference/( np.sqrt(2) * np.sqrt( outputs ))
    else:
        return np.sum( np.power(root_difference, 2 ) ) / math.sqrt( 2 )
#end cost function
Should not have contest, candidates or results for a Baltimore City comptroller
dataset/ETHPy150Open jorgenkg/python-neural-network/src/cost_functions.py/hellinger_distance--crossover with--dataset/ETHPy150Open openelections/openelections-core/openelex/us/md/validate/__init__.py/validate_no_baltimore_city_comptroller