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'''
@author: Garvin
'''
from numpy import *
import matplotlib.pyplot as plt

def loadDataSet(fileName, delim='\t'):
  fr = open(fileName)
  stringArr = [line.strip().split(delim) for line in fr.readlines()]
  datArr = [map(float,line) for line in stringArr]
  return mat(datArr)

def pca(dataMat, topNfeat=9999999):
  meanVals = mean(dataMat, axis=0)
  meanRemoved = dataMat - meanVals #remove mean
  covMat = cov(meanRemoved, rowvar=0)
  eigVals,eigVects = linalg.eig(mat(covMat))
  eigValInd = argsort(eigVals)          #sort, sort goes smallest to largest
  eigValInd = eigValInd[:-(topNfeat+1):-1]  #cut off unwanted dimensions
  redEigVects = eigVects[:,eigValInd]      #reorganize eig vects largest to smallest
  lowDDataMat = meanRemoved * redEigVects#transform data into new dimensions
  reconMat = (lowDDataMat * redEigVects.T) + meanVals
  return lowDDataMat, reconMat

def plotBestFit(dataSet1,dataSet2):   
  dataArr1 = array(dataSet1)
  dataArr2 = array(dataSet2)
  n = shape(dataArr1)[0] 
  n1=shape(dataArr2)[0]
  xcord1 = []; ycord1 = []
  xcord2 = []; ycord2 = []
  xcord3=[];ycord3=[]
  j=0
  for i in range(n):

      xcord1.append(dataArr1[i,0]); ycord1.append(dataArr1[i,1])
      xcord2.append(dataArr2[i,0]); ycord2.append(dataArr2[i,1])                 fig = plt.figure()
  ax = fig.add_subplot(111)
  ax.scatter(xcord1, ycord1, s=30, c='red', marker='s')
  ax.scatter(xcord2, ycord2, s=30, c='green')

  plt.xlabel('X1'); plt.ylabel('X2');
  plt.show()    




if __name__=='__main__':
   mata=loadDataSet('/Users/hakuri/Desktop/testSet.txt')  
   a,b= pca(mata, 2)