Now we can ask evaluator to calculate the scores for a set of parameters. (The lower the score the better the model). Let's calculate the score of the default_params set we used before, we know that this parameter set generates 1 spike in the first trace, and 5 spikes in the second, so we know that this should generate a perfect score of 0 for step1. For step2 we are searching for a solution with 6 spikes, so the score of our default_params won't be perfect for that trace:
The following text should be checked in https://github.com/BlueBrain/MOOC-neurons-and-synapses-2017/blob/master/ConstrModels/TutBallStickOpt/ballandstickopt.ipynb E.g. this is wrong: 'so we know that this should generate a perfect score of 0 for step1'
Now we can ask evaluator to calculate the scores for a set of parameters. (The lower the score the better the model). Let's calculate the score of the default_params set we used before, we know that this parameter set generates 1 spike in the first trace, and 5 spikes in the second, so we know that this should generate a perfect score of 0 for step1. For step2 we are searching for a solution with 6 spikes, so the score of our default_params won't be perfect for that trace: