PCA on random walk data

gistfile1.py

# generate 100-dimensional random walk data so that each data point in a sequence is similar to the last data point
import numpy as np

last=np.random.normal(0, .1, 100)
for i in range(1000):
	new=last+np.random.normal(0, .1, 100)
	last=new

	print ' '.join(str(x) for x in new)

gistfile2.txt

# run PCA and plot 2D in R

library(stats)
A=read.table("data.gen", sep=" ")
res=princomp(A)
reduce=predict(res,A)
plot(reduce[,1], reduce[,2])