On 15/06/21 12:51 am, Elena wrote:
I see what you mean, so I try to explain it better: Y is a vector say [y1, y2, ... yn], with large (n>>10), where yi = f(Xi) with Xi = [x1i, x2i, ... x10i] 1<=i<=n. All yi and xji assume discrete values.I already have a dataset of X={Xi} and would like to find the rules f able to minimize a complicated-undifferenciable Real function g(f(X)). Hope this makes more sense.
Hmmm, so the problem breaks down into two parts: (1) find a vector Y that minimises g (2) find a set of rules that will allow you to predict each component of Y from its corresponding X values Is that right?
x1...x10 are 10 chemical components that can be absent (0), present (1), modified (2). yi represent a quality index of the mixtures and g is a global quality of the whole process.
I ztill don't really understand. What are you going to do with this function f once you have it? I would have thought the idea was that if someone gives you a new mixture X[n+1] you can use f to predict how well it will work. But that will just give you a y[n+1], and it's not clear what to do with that. Do you append it to Y and feed an n+1 component vector into g? I think I still need more information about the underlying problem before I can help you much. -- Greg -- https://mail.python.org/mailman/listinfo/python-list
