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Matrix of activation distances between identified equations (without labels, since there's too many equations and Matplotlib doesn't let me generate images with sides bigger than 2^16 pixels):
The identified equations, also without labels for the same reason:
These are clustered in the activation space to find models with a consistent (frequently appearing) structure.
If the equations are filtered for equations that have at least one element on the lower triangular part of the activation distance matrix = 0, we get rid of most of them. This is analogous to clustering and keeping clusters with at least two points.
Activation distance matrix filtered for equations that appear at least twice:
And the solution matrix:
After clustering and keeping only clusters with 5 or more points, then sorting them so they're together when visualized:
Implicit dynamics assuming all parameters are 1 ( to make equations shorter):
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