Thank you very much! I didn't know about jenkspy. Raphael
On 13 April 2018 at 02:19, Pedro Pazzini <pedropazz...@gmail.com> wrote: > Hi Raphael. > > An option to highlight a dense region in your vector is to use a density > estimator (http://scikit-learn.org/stable/modules/density.html). > > But I think that the python module jenkspy > (https://pypi.python.org/pypi/jenkspy and https://github.com/mthh/jenkspy) > can help you also. The method finds the natural breaks of data in 1d > (https://en.wikipedia.org/wiki/Jenks_natural_breaks_optimization). I think > that if you find a good value for the 'nb_class' parameter you can separate > the dense region of your data from the sparse one. > > K-means is a generalization of Jenks break optimization for multivariate > data, so, maybe, you could use the K-means module of scikit-learn for that > also. On this approach, personally, I think the jenskpy module more > straightforward. > > I hope it helps. > > Pedro Pazzini > > 2018-04-12 16:22 GMT-03:00 Raphael C <drr...@gmail.com>: >> >> I have a set of points in 1d represented by a list X of floating point >> numbers. The list has one dense section and the rest is sparse and I >> want to find the dense part. I can't release the actual data but here >> is a simulation: >> >> N = 100 >> >> start = 0 >> points = [] >> rate = 0.1 >> for i in range(N): >> points.append(start) >> start = start + random.expovariate(rate) >> rate = 10 >> for i in range(N*10): >> points.append(start) >> start = start + random.expovariate(rate) >> rate = 0.1 >> for i in range(N): >> points.append(start) >> start = start + random.expovariate(rate) >> plt.hist(points, bins = 100) >> plt.show() >> >> I would like to use scikit learn to find the dense region. This feels >> a little like outlier detection or the task of finding one cluster >> with noise. >> >> Is there a suitable method in scikit learn for this task? >> >> Raphael >> _______________________________________________ >> 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 > _______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
