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Hello, i noticed some issues with the current implementation of the double sigmoid function But maybe what you really want is not a sigmoid? Problems the double sigmoid only works as intended as long as the type of the array submitted is float
Int arrays will lead to a the functional form of a hard_sigmoid independent of the value for k. The sigmoid is a strictly growing function that has asymptotic behaviour for $t\rightarrow\pm\infty$, not at finite values $t=\pm2$ or any other finite choices Use the `get_sigmoid` and `get_double_sigmoid` functions to get sigmoid objects which can be called with a parameter value to get the score.
Sigmoid of the double sigmoid asserts itself and causes a turning on of the transition state
Finally, the long tail of the second sigmoid takes over for large times. Any **kwarg left over in the call to get_scoring_function will be checked against a list of (allowed) kwargs for the class and if a match is found the value of the item will be the new value for the class If num_processes == 0, the scoring function will be run in the main process. A double sigmoid transformation using a left and right transition to determine the inflection points and the rate of change
The left and right transitions are expected to have opposite signs for the rates. The simple way is to just set the oe_license environment variable to the path of the file containing the license If you just want to set the license in the reinvent_scoring conda environment, it is a bit more complicated, but you only have to do it once. I want to create a new scoring function with a new transform type to support some variation of gp
So the current transform types (double_sigmoid, etc) does not support what i want
Could you help me figure out how to do so? These resources demonstrate how to use the reinvent4 platform for various molecular design tasks, from basic reinforcement learning to more complex workflows involving transfer learning and integration with external tools.
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