Jake, Thanks for the suggestion. When I do that, I get back (anonymous function). What I would like to get back is the actual model (with the new parameters) to be able to do things like this
hmmmodel.means_ hmmmodel.predict([[1 2 3]]) Thanks, Arshak On Thu, Sep 25, 2014 at 12:37 PM, Jake Bolewski <[email protected]> wrote: > Julia doesn't allow overloading field access for types, so you have to use > this workaround in pycall -> hmmmodel[:fit](df[:abc]) > > On Thursday, September 25, 2014 3:22:16 PM UTC-4, Arshak Navruzyan wrote: >> >> I am trying to use a sklearn model in Julia. The first part works ok and >> I get back the model object but when I try to fit the model, I get an error >> >> @pyimport sklearn.hmm as hmm >> >> hmmmodel = hmm.GaussianHMM(3, "full") >> >> PyObject GaussianHMM(algorithm='viterbi', covariance_type='full', >> covars_prior=0.01, >> covars_weight=1, >> init_params='abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ', >> means_prior=None, means_weight=0, n_components=3, n_iter=10, >> params='abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ', >> random_state=None, startprob=None, startprob_prior=None, thresh=0.01, >> transmat=None, transmat_prior=None) >> >> >> hmmmodel.fit(df[:abc]) >> >> >> type PyObject has no field fit >> while loading In[275], in expression starting on line 1 >> >>
