Thanks to all the replies. I was able to write the intial code
- Refer the charts below.. After the second red point, can I say that the values of "BLUE" curve will always be higher than "GREEN" curve? - The ultimate objective is to find out when the values of blue curve starts exceeding the values of green curve. Regards, Sanant[image: Inline image 1] On Fri, May 27, 2016 at 10:29 PM, Jacob Schreiber <[email protected]> wrote: > 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 >> >> > > _______________________________________________ > scikit-learn mailing list > [email protected] > https://mail.python.org/mailman/listinfo/scikit-learn > >
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