from scipy.stats.kde import gaussian_kde from scipy.stats import norm from numpy import linspace,hstack from pylab import plot,show,hist # creating data with two peaks sampD1 = norm.rvs(loc=-1.0,scale=1,size=300) sampD2 = norm.rvs(loc=2.0,scale=0.5,size=300) samp = hstack([sampD1,sampD2]) # obtaining the pdf (my_pdf is a function!) my_pdf = gaussian_kde(samp) # plotting the result x = linspace(-5,5,100) plot(x,my_pdf(x),'r') # distribution function hist(samp,normed=1,alpha=.3) # histogram show()The result should be as follows:

## Thursday, August 16, 2012

### Kernel Density Estimation with scipy

This post continues the last one where we have seen how to how to fit two types of distribution functions (Normal and Rayleigh). This time we will see how to use Kernel Density Estimation (KDE) to estimate the probability density function. KDE is a non-parametric technique for density estimation in which a known density function (the kernel) is averaged across the observed data points to create a smooth approximation. Given the non-parametrica nature of KDE, the main estimator has not a fixed functional form but only it depends upon all the data points we used for the estimation. Let's see the snippet:

Subscribe to:
Post Comments (Atom)

This comment has been removed by a blog administrator.

ReplyDeleteSuper helpful - thank you for posting!

ReplyDeleteThis comment has been removed by the author.

ReplyDeleteHi Samantha, you could have as many bins as many samples.

DeleteHello I would like to plot a histogram with counts on the y axis and binned data on the x. However once normed=1 is set my numbers change from 0 - 50 to 0 = 1.8.

DeleteThis comment has been removed by the author.

ReplyDeleteThis comment has been removed by the author.

ReplyDeleteThank you for posting. I'm currently trying to do something similar however, I'm trying to keep the count per bin data as the y axis. Do you have any hints on how to show this as the scale on the y axis?

ReplyDeleteHow does one plot each individual kernel?

ReplyDelete