Fitting Lognormal Distribution
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] 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
Hi Sanant, On Thursday, May 26, 2016, Startup Hire <blrstartuphire@gmail.com> wrote:
Hi all,
Hope you are doing good.
I would like to think so, but you never know where ML will lead us ...
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]
if your distribution is lognormal, why don't you try fitting a gaussian to the log of the values? is this too unstable?
2. I need to find the intersection of the lognormal distribution so that I can decide cut-off values based on that.
what exactly do you mean by intersection?
Can you guide me on (1) and (2) can be achieved in python?
Regards, Sanant
Michael
Hi Michael, :) (1) - I think you are right, how do I fit a normal distribution to the log of values? (2) Intersection ---> Meeting point (s) . as in where the curves cross each other (it can be in multiple places too!) Regards, Sanant On Thu, May 26, 2016 at 11:52 AM, Michael Eickenberg < michael.eickenberg@gmail.com> wrote:
Hi Sanant,
On Thursday, May 26, 2016, Startup Hire <blrstartuphire@gmail.com> wrote:
Hi all,
Hope you are doing good.
I would like to think so, but you never know where ML will lead us ...
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]
if your distribution is lognormal, why don't you try fitting a gaussian to the log of the values? is this too unstable?
2. I need to find the intersection of the lognormal distribution so that I can decide cut-off values based on that.
what exactly do you mean by intersection?
Can you guide me on (1) and (2) can be achieved in python?
Regards, Sanant
Michael
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1) The normal distribution is parametrized by standard deviation and mean. Simply take the mean and standard deviation of the log of your values? 2) Which curves? You only mentioned a single log normal distribution. On Thu, 26 May 2016 at 08:42 Startup Hire <blrstartuphire@gmail.com> wrote:
Hi Michael,
:)
(1) - I think you are right, how do I fit a normal distribution to the log of values?
(2) Intersection ---> Meeting point (s) . as in where the curves cross each other (it can be in multiple places too!)
Regards, Sanant
On Thu, May 26, 2016 at 11:52 AM, Michael Eickenberg < michael.eickenberg@gmail.com> wrote:
Hi Sanant,
On Thursday, May 26, 2016, Startup Hire <blrstartuphire@gmail.com> wrote:
Hi all,
Hope you are doing good.
I would like to think so, but you never know where ML will lead us ...
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]
if your distribution is lognormal, why don't you try fitting a gaussian to the log of the values? is this too unstable?
2. I need to find the intersection of the lognormal distribution so that I can decide cut-off values based on that.
what exactly do you mean by intersection?
Can you guide me on (1) and (2) can be achieved in python?
Regards, Sanant
Michael
_______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
_______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
Hi, (1) - Thanks. will do that (2) - I am fitting the distribution for 2 different set of values.. I will find the distribution as mentioned by you in (1).. But, now having 2 curves, how do i find the meetings point(s) ? Regards, Sanant On Thu, May 26, 2016 at 12:16 PM, federico vaggi <vaggi.federico@gmail.com> wrote:
1) The normal distribution is parametrized by standard deviation and mean. Simply take the mean and standard deviation of the log of your values?
2) Which curves? You only mentioned a single log normal distribution.
On Thu, 26 May 2016 at 08:42 Startup Hire <blrstartuphire@gmail.com> wrote:
Hi Michael,
:)
(1) - I think you are right, how do I fit a normal distribution to the log of values?
(2) Intersection ---> Meeting point (s) . as in where the curves cross each other (it can be in multiple places too!)
Regards, Sanant
On Thu, May 26, 2016 at 11:52 AM, Michael Eickenberg < michael.eickenberg@gmail.com> wrote:
Hi Sanant,
On Thursday, May 26, 2016, Startup Hire <blrstartuphire@gmail.com> wrote:
Hi all,
Hope you are doing good.
I would like to think so, but you never know where ML will lead us ...
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]
if your distribution is lognormal, why don't you try fitting a gaussian to the log of the values? is this too unstable?
2. I need to find the intersection of the lognormal distribution so that I can decide cut-off values based on that.
what exactly do you mean by intersection?
Can you guide me on (1) and (2) can be achieved in python?
Regards, Sanant
Michael
_______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
_______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
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If you are talking about finding the values at which the probability density functions will have the same value, then you can just write the equations explicitly and solve in terms of theta1, sigma1 and theta2, sigma2? On Thu, 26 May 2016 at 09:23 Startup Hire <blrstartuphire@gmail.com> wrote:
Hi,
(1) - Thanks. will do that
(2) - I am fitting the distribution for 2 different set of values.. I will find the distribution as mentioned by you in (1).. But, now having 2 curves, how do i find the meetings point(s) ?
Regards, Sanant
On Thu, May 26, 2016 at 12:16 PM, federico vaggi <vaggi.federico@gmail.com
wrote:
1) The normal distribution is parametrized by standard deviation and mean. Simply take the mean and standard deviation of the log of your values?
2) Which curves? You only mentioned a single log normal distribution.
On Thu, 26 May 2016 at 08:42 Startup Hire <blrstartuphire@gmail.com> wrote:
Hi Michael,
:)
(1) - I think you are right, how do I fit a normal distribution to the log of values?
(2) Intersection ---> Meeting point (s) . as in where the curves cross each other (it can be in multiple places too!)
Regards, Sanant
On Thu, May 26, 2016 at 11:52 AM, Michael Eickenberg < michael.eickenberg@gmail.com> wrote:
Hi Sanant,
On Thursday, May 26, 2016, Startup Hire <blrstartuphire@gmail.com> wrote:
Hi all,
Hope you are doing good.
I would like to think so, but you never know where ML will lead us ...
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]
if your distribution is lognormal, why don't you try fitting a gaussian to the log of the values? is this too unstable?
2. I need to find the intersection of the lognormal distribution so that I can decide cut-off values based on that.
what exactly do you mean by intersection?
Can you guide me on (1) and (2) can be achieved in python?
Regards, Sanant
Michael
_______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
_______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
_______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
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Err, sorry - mu1, mu2, sigma1, sigma2, where mu1, sigma1 are the mean/standard deviation of the first distribution, and mu2, sigma2 are the mean and standard deviation of the second distribution. On Thu, 26 May 2016 at 09:26 federico vaggi <vaggi.federico@gmail.com> wrote:
If you are talking about finding the values at which the probability density functions will have the same value, then you can just write the equations explicitly and solve in terms of theta1, sigma1 and theta2, sigma2?
On Thu, 26 May 2016 at 09:23 Startup Hire <blrstartuphire@gmail.com> wrote:
Hi,
(1) - Thanks. will do that
(2) - I am fitting the distribution for 2 different set of values.. I will find the distribution as mentioned by you in (1).. But, now having 2 curves, how do i find the meetings point(s) ?
Regards, Sanant
On Thu, May 26, 2016 at 12:16 PM, federico vaggi < vaggi.federico@gmail.com> wrote:
1) The normal distribution is parametrized by standard deviation and mean. Simply take the mean and standard deviation of the log of your values?
2) Which curves? You only mentioned a single log normal distribution.
On Thu, 26 May 2016 at 08:42 Startup Hire <blrstartuphire@gmail.com> wrote:
Hi Michael,
:)
(1) - I think you are right, how do I fit a normal distribution to the log of values?
(2) Intersection ---> Meeting point (s) . as in where the curves cross each other (it can be in multiple places too!)
Regards, Sanant
On Thu, May 26, 2016 at 11:52 AM, Michael Eickenberg < michael.eickenberg@gmail.com> wrote:
Hi Sanant,
On Thursday, May 26, 2016, Startup Hire <blrstartuphire@gmail.com> wrote:
Hi all,
Hope you are doing good.
I would like to think so, but you never know where ML will lead us ...
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]
if your distribution is lognormal, why don't you try fitting a gaussian to the log of the values? is this too unstable?
2. I need to find the intersection of the lognormal distribution so that I can decide cut-off values based on that.
what exactly do you mean by intersection?
Can you guide me on (1) and (2) can be achieved in python?
Regards, Sanant
Michael
_______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
_______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
_______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
_______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
Thanks On Thu, May 26, 2016 at 12:57 PM, federico vaggi <vaggi.federico@gmail.com> wrote:
Err, sorry - mu1, mu2, sigma1, sigma2, where mu1, sigma1 are the mean/standard deviation of the first distribution, and mu2, sigma2 are the mean and standard deviation of the second distribution.
On Thu, 26 May 2016 at 09:26 federico vaggi <vaggi.federico@gmail.com> wrote:
If you are talking about finding the values at which the probability density functions will have the same value, then you can just write the equations explicitly and solve in terms of theta1, sigma1 and theta2, sigma2?
On Thu, 26 May 2016 at 09:23 Startup Hire <blrstartuphire@gmail.com> wrote:
Hi,
(1) - Thanks. will do that
(2) - I am fitting the distribution for 2 different set of values.. I will find the distribution as mentioned by you in (1).. But, now having 2 curves, how do i find the meetings point(s) ?
Regards, Sanant
On Thu, May 26, 2016 at 12:16 PM, federico vaggi < vaggi.federico@gmail.com> wrote:
1) The normal distribution is parametrized by standard deviation and mean. Simply take the mean and standard deviation of the log of your values?
2) Which curves? You only mentioned a single log normal distribution.
On Thu, 26 May 2016 at 08:42 Startup Hire <blrstartuphire@gmail.com> wrote:
Hi Michael,
:)
(1) - I think you are right, how do I fit a normal distribution to the log of values?
(2) Intersection ---> Meeting point (s) . as in where the curves cross each other (it can be in multiple places too!)
Regards, Sanant
On Thu, May 26, 2016 at 11:52 AM, Michael Eickenberg < michael.eickenberg@gmail.com> wrote:
Hi Sanant,
On Thursday, May 26, 2016, Startup Hire <blrstartuphire@gmail.com> wrote:
> Hi all, > > Hope you are doing good. >
I would like to think so, but you never know where ML will lead us ...
> > 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] >
if your distribution is lognormal, why don't you try fitting a gaussian to the log of the values? is this too unstable?
> > 2. I need to find the intersection of the lognormal distribution so > that I can decide cut-off values based on that. >
what exactly do you mean by intersection?
> > > Can you guide me on (1) and (2) can be achieved in python? > > Regards, > Sanant >
Michael
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On Thu, May 26, 2016 at 2:08 AM, Startup Hire <blrstartuphire@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-pyth... 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 scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
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@gmail.com> wrote:
On Thu, May 26, 2016 at 2:08 AM, Startup Hire <blrstartuphire@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-pyth... 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 scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
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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? @ 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@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@gmail.com> wrote:
On Thu, May 26, 2016 at 2:08 AM, Startup Hire <blrstartuphire@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-pyth... 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 scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
_______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
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On Fri, May 27, 2016 at 2:08 AM, Startup Hire <blrstartuphire@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-u... 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@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@gmail.com> wrote:
On Thu, May 26, 2016 at 2:08 AM, Startup Hire <blrstartuphire@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-pyth... 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 scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
_______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
_______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
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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_Dis...> 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@gmail.com> wrote:
On Fri, May 27, 2016 at 2:08 AM, Startup Hire <blrstartuphire@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-u...
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@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@gmail.com> wrote:
On Thu, May 26, 2016 at 2:08 AM, Startup Hire <blrstartuphire@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-pyth... 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 scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
_______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
_______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
_______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
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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@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_Dis...> 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@gmail.com> wrote:
On Fri, May 27, 2016 at 2:08 AM, Startup Hire <blrstartuphire@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-u...
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@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@gmail.com> wrote:
On Thu, May 26, 2016 at 2:08 AM, Startup Hire < blrstartuphire@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-pyth... 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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Hi, Any one call help in above case? Regards, Sanant On Mon, May 30, 2016 at 4:48 PM, Startup Hire <blrstartuphire@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@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_Dis...> 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@gmail.com> wrote:
On Fri, May 27, 2016 at 2:08 AM, Startup Hire <blrstartuphire@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-u...
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@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@gmail.com> wrote:
On Thu, May 26, 2016 at 2:08 AM, Startup Hire < blrstartuphire@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-pyth... 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 > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > >
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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@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@gmail.com <javascript:_e(%7B%7D,'cvml','blrstartuphire@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@gmail.com <javascript:_e(%7B%7D,'cvml','jmschreiber91@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_Dis...> 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@gmail.com <javascript:_e(%7B%7D,'cvml','warren.weckesser@gmail.com');>> wrote:
On Fri, May 27, 2016 at 2:08 AM, Startup Hire <blrstartuphire@gmail.com <javascript:_e(%7B%7D,'cvml','blrstartuphire@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-u...
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@gmail.com <javascript:_e(%7B%7D,'cvml','sbenthall@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@gmail.com <javascript:_e(%7B%7D,'cvml','warren.weckesser@gmail.com');>> wrote:
> > > On Thu, May 26, 2016 at 2:08 AM, Startup Hire < > blrstartuphire@gmail.com > <javascript:_e(%7B%7D,'cvml','blrstartuphire@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-pyth... > 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 >> scikit-learn@python.org >> <javascript:_e(%7B%7D,'cvml','scikit-learn@python.org');> >> https://mail.python.org/mailman/listinfo/scikit-learn >> >> > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > <javascript:_e(%7B%7D,'cvml','scikit-learn@python.org');> > https://mail.python.org/mailman/listinfo/scikit-learn > > _______________________________________________ scikit-learn mailing list scikit-learn@python.org <javascript:_e(%7B%7D,'cvml','scikit-learn@python.org');> https://mail.python.org/mailman/listinfo/scikit-learn
_______________________________________________ scikit-learn mailing list scikit-learn@python.org <javascript:_e(%7B%7D,'cvml','scikit-learn@python.org');> https://mail.python.org/mailman/listinfo/scikit-learn
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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@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@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@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@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_Dis...> 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@gmail.com> wrote:
On Fri, May 27, 2016 at 2:08 AM, Startup Hire < blrstartuphire@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-u...
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@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@gmail.com> wrote: > >> >> >> On Thu, May 26, 2016 at 2:08 AM, Startup Hire < >> blrstartuphire@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-pyth... >> 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 >>> scikit-learn@python.org >>> https://mail.python.org/mailman/listinfo/scikit-learn >>> >>> >> >> _______________________________________________ >> scikit-learn mailing list >> scikit-learn@python.org >> https://mail.python.org/mailman/listinfo/scikit-learn >> >> > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > >
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no, I mean to say log(yaxis) On Fri, Jun 3, 2016 at 12:02 PM, Startup Hire <blrstartuphire@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@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@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@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@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_Dis...> 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@gmail.com> wrote:
On Fri, May 27, 2016 at 2:08 AM, Startup Hire < blrstartuphire@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-u...
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@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@gmail.com> wrote: >> >>> >>> >>> On Thu, May 26, 2016 at 2:08 AM, Startup Hire < >>> blrstartuphire@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-pyth... >>> 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 >>>> scikit-learn@python.org >>>> https://mail.python.org/mailman/listinfo/scikit-learn >>>> >>>> >>> >>> _______________________________________________ >>> scikit-learn mailing list >>> scikit-learn@python.org >>> https://mail.python.org/mailman/listinfo/scikit-learn >>> >>> >> _______________________________________________ >> scikit-learn mailing list >> scikit-learn@python.org >> https://mail.python.org/mailman/listinfo/scikit-learn >> >> > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > >
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Hi everyone, I am running OneClassSVM method. It seems unlike the normal SVC, which has an option to return probability, this method does not have any option to retrieve probability values. I would like to draw some performance metric such as the ROC and Precision Recall about the performance of the classifier. Thanks, Mamun
Hi Mamun, You can draw ROC and PR curves using the OCSVM decision_function Nicolas 2016-06-03 11:54 GMT-04:00 Mamun Rashid <mamunbabu2001@gmail.com>:
Hi everyone, I am running OneClassSVM method. It seems unlike the normal SVC, which has an option to return probability, this method does not have any option to retrieve probability values. I would like to draw some performance metric such as the ROC and Precision Recall about the performance of the classifier.
Thanks, Mamun _______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
Hi Nicolas, Thanks for your reply. Apology for the naive question. I can see from the example that we can plot the decision boundary using the decision function. Not sure how can I extract the ROC and PRC metric from there. A small example would greatly help. Thanks, Mamun
On 3 Jun 2016, at 17:16, Nicolas Goix <goix.nicolas@gmail.com> wrote:
Hi Mamun, You can draw ROC and PR curves using the OCSVM decision_function Nicolas
2016-06-03 11:54 GMT-04:00 Mamun Rashid <mamunbabu2001@gmail.com <mailto:mamunbabu2001@gmail.com>>: Hi everyone, I am running OneClassSVM method. It seems unlike the normal SVC, which has an option to return probability, this method does not have any option to retrieve probability values. I would like to draw some performance metric such as the ROC and Precision Recall about the performance of the classifier.
Thanks, Mamun _______________________________________________ scikit-learn mailing list scikit-learn@python.org <mailto:scikit-learn@python.org> https://mail.python.org/mailman/listinfo/scikit-learn <https://mail.python.org/mailman/listinfo/scikit-learn>
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Hi Mamun, from sklearn.metrics import roc_curve, auc from sklearn.svm import OneClassSVM ocsvm = OneClassSVM().fit(X_train) scoring = - ocsvm.decision_function(X_test) # the lower, the more normal fpr, tpr, thresholds = roc_curve(y_test, scoring) AUC = auc(fpr, tpr) HTH Nicolas 2016-06-06 19:21 GMT-04:00 Mamun Rashid <mamunbabu2001@gmail.com>:
Hi Nicolas, Thanks for your reply. Apology for the naive question. I can see from the example that we can plot the decision boundary using the decision function. Not sure how can I extract the ROC and PRC metric from there. A small example would greatly help.
Thanks, Mamun
On 3 Jun 2016, at 17:16, Nicolas Goix <goix.nicolas@gmail.com> wrote:
Hi Mamun, You can draw ROC and PR curves using the OCSVM decision_function Nicolas
2016-06-03 11:54 GMT-04:00 Mamun Rashid <mamunbabu2001@gmail.com>:
Hi everyone, I am running OneClassSVM method. It seems unlike the normal SVC, which has an option to return probability, this method does not have any option to retrieve probability values. I would like to draw some performance metric such as the ROC and Precision Recall about the performance of the classifier.
Thanks, Mamun _______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
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participants (8)
-
federico vaggi -
Jacob Schreiber -
Mamun Rashid -
Michael Eickenberg -
Nicolas Goix -
Sebastian Benthall -
Startup Hire -
Warren Weckesser