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] > <javascript:_e(%7B%7D,'cvml','[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] >> <javascript:_e(%7B%7D,'cvml','[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] >>> <javascript:_e(%7B%7D,'cvml','[email protected]');>> wrote: >>> >>>> >>>> >>>> On Fri, May 27, 2016 at 2:08 AM, Startup Hire <[email protected] >>>> <javascript:_e(%7B%7D,'cvml','[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] >>>>> <javascript:_e(%7B%7D,'cvml','[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] >>>>>> <javascript:_e(%7B%7D,'cvml','[email protected]');>> wrote: >>>>>> >>>>>>> >>>>>>> >>>>>>> On Thu, May 26, 2016 at 2:08 AM, Startup Hire < >>>>>>> [email protected] >>>>>>> <javascript:_e(%7B%7D,'cvml','[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] >>>>>>>> <javascript:_e(%7B%7D,'cvml','[email protected]');> >>>>>>>> https://mail.python.org/mailman/listinfo/scikit-learn >>>>>>>> >>>>>>>> >>>>>>> >>>>>>> _______________________________________________ >>>>>>> scikit-learn mailing list >>>>>>> [email protected] >>>>>>> <javascript:_e(%7B%7D,'cvml','[email protected]');> >>>>>>> https://mail.python.org/mailman/listinfo/scikit-learn >>>>>>> >>>>>>> >>>>>> _______________________________________________ >>>>>> scikit-learn mailing list >>>>>> [email protected] >>>>>> <javascript:_e(%7B%7D,'cvml','[email protected]');> >>>>>> https://mail.python.org/mailman/listinfo/scikit-learn >>>>>> >>>>>> >>>>> >>>>> _______________________________________________ >>>>> scikit-learn mailing list >>>>> [email protected] >>>>> <javascript:_e(%7B%7D,'cvml','[email protected]');> >>>>> https://mail.python.org/mailman/listinfo/scikit-learn >>>>> >>>>> >>>> >>>> _______________________________________________ >>>> scikit-learn mailing list >>>> [email protected] >>>> <javascript:_e(%7B%7D,'cvml','[email protected]');> >>>> https://mail.python.org/mailman/listinfo/scikit-learn >>>> >>>> >>> >>> _______________________________________________ >>> scikit-learn mailing list >>> [email protected] >>> <javascript:_e(%7B%7D,'cvml','[email protected]');> >>> https://mail.python.org/mailman/listinfo/scikit-learn >>> >>> >> >
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