Another option is to use pomegranate <https://github.com/jmschrei/pomegranate> which has probability distribution fitting with the same API as scikit-learn. You can see a tutorials here <https://github.com/jmschrei/pomegranate/blob/master/tutorials/Tutorial_1_Distributions.ipynb> and it includes LogNormalDistribution, in addition to a lot of others. All distributions also have plotting methods.
On Fri, May 27, 2016 at 6:53 AM, Warren Weckesser < [email protected]> wrote: > > > On Fri, May 27, 2016 at 2:08 AM, Startup Hire <[email protected]> > wrote: > >> Hi, >> >> @ Warren: I was thinking of using federico method as its quite simple. I >> know the mu and sigma of log(values) and I need to plot a normal >> distribution based on that. Anything inaccurate in doing that? >> >> > > Getting mu and sigma from log(values) is fine. That's one of the three > methods (the one labeled "Explicit formula") that I included in this > answer: > http://stackoverflow.com/questions/15630647/fitting-lognormal-distribution-using-scipy-vs-matlab/15632937#15632937 > > Warren > > > >> @ Sebastian: Thanks for your suggestion. I got to know more about >> powerlaw distributions. But, I dont think my values have a long tail. do >> you think it is still relevant? What are the potential applications of the >> same? >> >> Thanks & Regards, >> Sanant >> >> On Thu, May 26, 2016 at 7:50 PM, Sebastian Benthall <[email protected]> >> wrote: >> >>> You may also be interested in the 'powerlaw' Python package, which >>> detects the tail cutoff. >>> On May 26, 2016 5:46 AM, "Warren Weckesser" <[email protected]> >>> wrote: >>> >>>> >>>> >>>> On Thu, May 26, 2016 at 2:08 AM, Startup Hire <[email protected] >>>> > wrote: >>>> >>>>> Hi all, >>>>> >>>>> Hope you are doing good. >>>>> >>>>> I am working on a project where I need to do the following things: >>>>> >>>>> 1. I need to fit a lognormal distribution to a set of values [I know >>>>> its lognormal by a simple XY scatter plot in excel] >>>>> >>>>> >>>> >>>> The probability distributions in scipy have a fit() method, and >>>> scipy.stats.lognorm implements the log-normal distribution ( >>>> http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.lognorm.html) >>>> so you can use scipy.lognorm.fit(). See, for example, >>>> http://stackoverflow.com/questions/26406056/a-lognormal-distribution-in-python >>>> or http://stackoverflow.com/ >>>> /questions/15630647/fitting-lognormal-distribution-using-scipy-vs-matlab >>>> >>>> Warren >>>> >>>> >>>> >>>>> 2. I need to find the intersection of the lognormal distribution so >>>>> that I can decide cut-off values based on that. >>>>> >>>>> >>>>> Can you guide me on (1) and (2) can be achieved in python? >>>>> >>>>> Regards, >>>>> Sanant >>>>> >>>>> _______________________________________________ >>>>> 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 >>> >>> >> >> _______________________________________________ >> 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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