I don't understand what you mean. Does each sample have a fixed number of features or not?
On Sat, Jan 21, 2017 at 9:35 AM, Carlton Banks <[email protected]> wrote: > Thanks for the response! > > If you see it in 1d then yes…. it has variable length. In 2d will the > number of columns always be constant both for the input and output. > > Den 21. jan. 2017 kl. 18.25 skrev Jacob Schreiber <[email protected] > >: > > If what you're saying is that you have a variable length input, then most > sklearn classifiers won't work on this data. They expect a fixed feature > set. Perhaps you could try extracting a set of informative features being > fed into the classifier? > > On Sat, Jan 21, 2017 at 3:18 AM, Carlton Banks <[email protected]> wrote: > >> Hi guys.. >> >> I am currently working on a ASR project in which the objective is to >> substitute part of the general ASR framework with some form of neural >> network, to see whether the tested part improves in any way. >> >> I started working with the feature extraction and tried, to make a neural >> network (NN) that could create MFCC features. I already know what the >> desired output is supposed to be, so the problem boils down to a simple >> input - output mapping. Problem here is the my NN doesn’t seem to >> perform that well.. and i seem to get pretty large error for some reason. >> >> I therefore wanted to give random forrest a try, and see whether it could >> provide me a better result. >> >> I am currently storing my input and output in numpy.ndarrays, in which >> the input and output columns is consistent throughout all the examples, but >> the number of rows changes >> depending on length of the audio file. >> >> Is it possible with the random forrest implementation in scikit-learn to >> train a random forrest to map an input an output, given they are stored >> numpy.ndarrays? >> Or do i have do it in a different way? and if so how? >> >> kind regards >> >> Carl truz >> _______________________________________________ >> scikit-learn mailing list >> [email protected] >> https://mail.python.org/mailman/listinfo/scikit-learn >> > > _______________________________________________ > scikit-learn mailing list > [email protected] > https://mail.python.org/mailman/listinfo/scikit-learn > > > > _______________________________________________ > scikit-learn mailing list > [email protected] > https://mail.python.org/mailman/listinfo/scikit-learn > >
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