lofas.py

from __future__ import division
import numpy as np
import matplotlib.pyplot as plt
 
def read_data():
  f=open("Iris.txt", 'r')
  lines=[line.strip() for line in f.readlines()]
  f.close()
   
  lines=[line.split(",") for line in lines if line]
  
  class1=np.array([line[:4] for line in lines if line[-1]=="Iris-setosa"], dtype=np.float)
   
  class2=np.array([line[:4] for line in lines if line[-1]!="Iris-setosa"], dtype=np.float)
   
  return class1, class2
   
   
def main():
 
  class1, class2=read_data()
   
  mean1=np.mean(class1, axis=0)
  mean2=np.mean(class2, axis=0)
   
  #calculate variance within class
  Sw=np.dot((class1-mean1).T, (class1-mean1))+np.dot((class2-mean2).T, (class2-mean2))
   
  #calculate weights which maximize linear separation
  w=np.dot(np.linalg.inv(Sw), (mean2-mean1))
   
  print "vector of max weights", w
  #projection of classes on 1D space
  plt.plot(np.dot(class1, w), [0]*class1.shape[0], "bo", label="Iris-setosa")
  plt.plot(np.dot(class2, w), [0]*class2.shape[0], "go", label="Iris-versicolor and Iris-virginica")
  plt.legend()
   
  plt.show()
   
  main()