Yury, Please see inline
> On Nov 7, 2017, at 12:11 PM, Yury Babak <y.ch...@gmail.com> wrote: > > Denis, > > Let me clarify. > > Firstly, here gene is a single model coefficient(neuron weight, etc), > chromosomes - whole model representation. > Sounds good. Actually a chromosome can be see as a model. > Secondly GA should be implementation of Trainer API for each ML algorithm > such as regression, clusterization, NNs, etc. > It’s optional, right? Initially there should be a way to run standard operations over chromosomes specific to GA only. Those operations/algorithms are crossover, fitness score, mutations. Do this 3 operations fit trainer API? We’re putting aside extended support of regression, clusterization, etc. > And last but not least genetic algorithm does not fits for to Model API, so > it shouldn't`t implement it. Generally genetic algorithm dont produce any > predictive models. > I’m a bit confused here. Before we say that a chromosome is a model in terms of ML and it’s all about providing concrete trainer implementations. — Denis > Regards, > Yury > > > > -- > Sent from: http://apache-ignite-developers.2346864.n4.nabble.com/
