no, I mean to say log(yaxis) On Fri, Jun 3, 2016 at 12:02 PM, Startup Hire <[email protected]> wrote:
> 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 >> >> > > _______________________________________________ > scikit-learn mailing list > [email protected] > https://mail.python.org/mailman/listinfo/scikit-learn > >
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