fastKDE calculates a kernel density estimate of arbitrarily dimensioned data; it does so rapidly and robustly using recently developed KDE techniques. It does so with statistical skill that is as good as state-of-the-science 'R' KDE packages, and it does so 10,000 times faster for bivariate data (even better improvements for higher dimensionality).
Please cite the following papers when using this method:
O’Brien, T. A., Kashinath, K., Cavanaugh, N. R., Collins, W. D. & O’Brien, J. P. A fast and objective multidimensional kernel density estimation method: fastKDE. Comput. Stat. Data Anal. 101, 148–160 (2016).
O’Brien, T. A., Collins, W. D., Rauscher, S. A. & Ringler, T. D. Reducing the computational cost of the ECF using a nuFFT: A fast and objective probability density estimation method. Comput. Stat. Data Anal. 79, 222–234 (2014).
For a standard PDF
#!python import numpy as np from fastkde import fastKDE import pylab as PP #Generate two random variables dataset (representing 100000 pairs of datapoints) N = 2e5 var1 = 50*np.random.normal(size=N) + 0.1 var2 = 0.01*np.random.normal(size=N) - 300 #Do the self-consistent density estimate myPDF,axes = fastKDE.pdf(var1,var2) #Extract the axes from the axis list v1,v2 = axes #Plot contours of the PDF should be a set of concentric ellipsoids centered on #(0.1, -300) Comparitively, the y axis range should be tiny and the x axis range #should be large PP.contour(v1,v2,myPDF) PP.show()
For a conditional PDF
The following code generates samples from a non-trivial joint distribution
from fastkde import fastKDE import pylab as PP import numpy as np #*************************** # Generate random samples #*************************** # Stochastically sample from the function underlyingFunction() (a sigmoid): # sample the absicissa values from a gamma distribution # relate the ordinate values to the sample absicissa values and add # noise from a normal distribution #Set the number of samples numSamples = int(1e6) #Define a sigmoid function def underlyingFunction(x,x0=305,y0=200,yrange=4): return (yrange/2)*np.tanh(x-x0) + y0 xp1,xp2,xmid = 5,2,305 #Set gamma distribution parameters yp1,yp2 = 0,12 #Set normal distribution parameters (mean and std) #Generate random samples of X from the gamma distribution x = -(np.random.gamma(xp1,xp2,int(numSamples))-xp1*xp2) + xmid #Generate random samples of y from x and add normally distributed noise y = underlyingFunction(x) + np.random.normal(loc=yp1,scale=yp2,size=numSamples)
Now that we have the x,y samples, the following code calcuates the conditional
#*************************** # Calculate the conditional #*************************** pOfYGivenX,axes = fastKDE.conditional(y,x)
The following plot shows the results:
#*************************** # Plot the conditional #*************************** fig,axs = PP.subplots(1,2,figsize=(10,5)) #Plot a scatter plot of the incoming data axs.plot(x,y,'k.',alpha=0.1) axs.set_title('Original (x,y) data') #Set axis labels for i in (0,1): axs[i].set_xlabel('x') axs[i].set_ylabel('y') #Draw a contour plot of the conditional axs.contourf(axes,axes,pOfYGivenX,64) #Overplot the original underlying relationship axs.plot(axes,underlyingFunction(axes),linewidth=3,linestyle='--',alpha=0.5) axs.set_title('P(y|x)') #Set axis limits to be the same xlim = [np.amin(axes),np.amax(axes)] ylim = [np.amin(axes),np.amax(axes)] axs.set_xlim(xlim) axs.set_ylim(ylim) axs.set_xlim(xlim) axs.set_ylim(ylim) fig.tight_layout() PP.savefig('conditional_demo.png') PP.show()
How do I get set up?
A standard python build: python setup.py install
pip install fastkde
Download the source
Please contact Travis A. O'Brien TAOBrien@lbl.gov to obtain the latest version of the source.
This code requires the following software:
- Python >= 2.7.3
- Numpy >= 1.7
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