[scikit-learn] Fitting Lognormal Distribution

[Startup Hire]

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 <jmschreiber91 at gmail.com>
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 <
> warren.weckesser at gmail.com> wrote:
>
>>
>>
>> On Fri, May 27, 2016 at 2:08 AM, Startup Hire <blrstartuphire at gmail.com>
>> 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 <sbenthall at gmail.com
>>> > 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" <warren.weckesser at gmail.com>
>>>> wrote:
>>>>
>>>>>
>>>>>
>>>>> On Thu, May 26, 2016 at 2:08 AM, Startup Hire <
>>>>> blrstartuphire at gmail.com> 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
>>>>>>
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>>>>>>
>>>>>>
>>>>>
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