Open redcican opened 2 years ago
Hi,
Could you specify how you defined 'feature_mean'?
Thank you
The constraint is:
optimizer.parametrization.register_cheap_constraint(lambda X: np.sum(X[:10])*chemi_list[0] + np.sum(X[10:])*chemi_list[1] - chemi_to_achieve)`
which means the first 10 elements of array times the first array of
chemi_list
plus last 10 elements of array times the second array ofchemi_list0
minus chemi_to_achieve should equal to 0.
There is a discrepancy between the documentation on the website and in the code. On the website >= 0 is a passing value, but in the code this is reversed and any value > 0 is a fail.
Please check if your code works after taking this into account.
I was wondering how to perform an equality constrain by using nevergrad:
the objective is defined as:
where
reg
is a linear regression function,price_list
is array of shape (20,) I want to minimize this objective function.let's say I have a
array
of shape (20,) to optimize, it has lower bound of 0, and upper bound of 100.The constraint is:
which means the first 10 elements of array times the first array of
chemi_list
plus last 10 elements of array times the second array ofchemi_list0
minus chemi_to_achieve should equal to 0.the all constants arrays look like below:
But I kept getting error
TypeError: can only concatenate tuple (not "dict") to tuple
can anyone tell me how to solve this?