[scikit-learn] Fitting Lognormal Distribution

Fri Jun 3 08:06:56 EDT 2016

no, I mean to say log(yaxis)

On Fri, Jun 3, 2016 at 12:02 PM, Startup Hire <blrstartuphire at gmail.com>
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 <
> michael.eickenberg at gmail.com> 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 <blrstartuphire at gmail.com> wrote:
>>
>>> Hi,
>>>
>>> Any one call help in above case?
>>>
>>> Regards,
>>> Sanant
>>>
>>> On Mon, May 30, 2016 at 4:48 PM, Startup Hire <blrstartuphire at gmail.com>
>>> 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 <
>>>> 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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