Calculate eecentricity using a principle component analysis (PCA)
Source code name: "stat_eecentricity.py"
Programming language: Python
Topic: Statistics/Advanced
DMelt Version 1. Last modified: 12/11/2015. License: Pro
https://datamelt.org/code/cache/stat_eecentricity_4323.py
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# Eccentricity is defined as 1- variance(minor)/variance(major)

from jhplot  import *
from java.util import Random
c1 = HPlot("Canvas",600,400)
c1.setNameX("Xaxis")
c1.setNameY("Yaxis")
c1.visible(1)
c1.setRange(-4,4,-4,4)

p1 = P1D("Data")
rand = Random();
for i in range(5000):
          p1.add(rand.nextGaussian(),0.5*rand.nextGaussian())
c1.draw(p1);


# principle component analysis
from jhpro.stat import EEcentricity 
pca=EEcentricity(p1)
print " Min variance=", pca.getVarianceMin(), "Max variance=", pca.getVarianceMax()
print "EECentricity=" ,pca.getEccentricity()
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