Hi, Any one call help in above case?
Regards, Sanant On Mon, May 30, 2016 at 4:48 PM, Startup Hire <[email protected]> wrote: > 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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