The above normal distribution is plotted by taking log of the values.. So, you mean to say I can take exp(values) and see whether the criteria is satisfied after the meeting point.
Regards, Sanant On Fri, Jun 3, 2016 at 3:08 PM, Michael Eickenberg < [email protected]> wrote: > probably, especially if they are normalised. > you have the formulas for those, right? then you can say it for sure. just > take the log on both sides. start by plotting the log of both of those > distributions and you willprobably see already > > > On Friday, June 3, 2016, Startup Hire <[email protected]> wrote: > >> 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 >>>> >>>> >>> >> > _______________________________________________ > scikit-learn mailing list > [email protected] > https://mail.python.org/mailman/listinfo/scikit-learn > >
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