Hi Jacob, I have just changed my code from BayesianGaussianMixture to GaussianMixture, and the results is the same. I attached here the picture of the first component when I runned the code with 5, 10, and 50 nfeatures and 2 components. In my short test function I expect to have point that they can be in one component as well as another has visible for small number of nfeatures, but 0 1 for nfeatures >50 does not sounds correct. Seems that is just related to the size of the model and in particular to the number of features. With the BayesianGaussianMixture I have seen that it is sligthly better to increase the degree of freedoms to 2*nfeatures instead of the default nfeatures. However, this does not change the result when the nfeatures are 50 or more.
Thank you in advance Tommaso 2016-11-25 21:32 GMT-05:00 Jacob Schreiber <jmschreibe...@gmail.com>: > Typically this means that the model is so confident in its predictions it > does not believe it possible for the sample to come from the other > component. Do you get the same results with a regular GaussianMixture? > > On Fri, Nov 25, 2016 at 11:34 AM, Tommaso Costanzo < > tommaso.costanz...@gmail.com> wrote: > >> Hi, >> >> I am facing some problem with the "BayesianGaussianMixture" function, but >> I do not know if it is because of my poor knowledge on this type of >> statistics or if it is something related to the algorithm. I have set of >> data of around 1000 to 4000 observation (every feature is a spectrum of >> around 200 point) so in the end I have n_samples = ~1000 and n_features = >> ~20. The good things is that I am getting the same results of KMeans >> however the "predict_proba" has value only of 0 or 1. >> >> I have wrote a small function to simulate my problem with random data >> that is reported below. The first 1/2 of the array has the point with a >> positive slope while the second 1/2 has a negative slope, so the cross in >> the middle. What I have seen is that for a small number of features I >> obtain good probability, but if the number of features increases (say 50) >> than the probability become only 0 or 1. >> Can someone help me in interpret this result? >> >> Here is the code I wrote with the generated random number, I'll generally >> run it with ncomponent=2 and nfeatures=5 or 10 or 50 or 100. I am not sure >> if it will work in every case is not very highly tested. I have also >> attached as a file! >> >> ############################################################ >> ############## >> import numpy as np >> >> from sklearn.mixture import GaussianMixture, >> BayesianGaussianMixture >> import matplotlib.pyplot as plt >> >> >> >> def test_bgm(ncomponent, nfeatures): >> >> temp = np.random.randn(500,nfeatures) >> >> temp = temp + np.arange(-1,1, 2.0/nfeatures) >> >> temp1 = np.random.randn(400,nfeatures) >> >> temp1 = temp1 + np.arange(1,-1, (-2.0/nfeatures)) >> >> X = np.vstack((temp, temp1)) >> >> >> >> bgm = >> BayesianGaussianMixture(ncomponent,degrees_of_freedom_prior=nfeatures*2).fit(X) >> >> bgm_proba = bgm.predict_proba(X) >> >> bgm_labels = bgm.predict(X) >> >> >> >> plt.figure(-1) >> >> plt.imshow(bgm_labels.reshape(30,-1), origin='lower', >> interpolatio='none') >> plt.colorbar() >> >> >> >> for i in np.arange(0,ncomponent): >> >> plt.figure(i) >> >> plt.imshow(bgm_proba[:,i].reshape(30,-1), origin='lower', >> interpolatio='none') >> plt.colorbar() >> >> >> >> plt.show() >> ############################################################ >> ################## >> >> Thank you in advance >> Tommaso >> >> >> -- >> Please do NOT send Microsoft Office Attachments: >> http://www.gnu.org/philosophy/no-word-attachments.html >> >> _______________________________________________ >> scikit-learn mailing list >> scikit-learn@python.org >> https://mail.python.org/mailman/listinfo/scikit-learn >> >> > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > > -- Please do NOT send Microsoft Office Attachments: http://www.gnu.org/philosophy/no-word-attachments.html
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