Great. Thanks for your time Manoj
Cheers, Klo On Mon, Oct 3, 2016 at 8:20 PM, Manoj Kumar <manojkumarsivaraj...@gmail.com> wrote: > Let's say you would like to generate just the first feature of 1000 > samples with label 0. > > The distribution of the first feature conditioned on label 1 follows a > Bernoulli distribution (as suggested by the name) with parameter > "exp(feature_log_prob_[0, 0])". You could then generate the first feature > of these 1000 samples by just doing > > first_feature = bernoulli.rvs(exp(feature_log_prob_[0, 0]), size=1000) > > And follow the same approach for all the other features with the > corresponding parameters. (They are conditionally independent) > > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > >
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