kNN_by_myself.py

'''
@author: wepon
@github: https://github.com/wepe
@blog:   http://blog.csdn.net/u012162613
'''
#!/usr/bin/python
#-*-coding:utf-8-*-
from numpy import *
import operator
import csv
def toInt(array):
    array=mat(array)
    m,n=shape(array)
    newArray=zeros((m,n))
    for i in xrange(m):
        for j in xrange(n):
                newArray[i,j]=int(array[i,j])
    return newArray
    
def nomalizing(array):
    m,n=shape(array)
    for i in xrange(m):
        for j in xrange(n):
            if array[i,j]!=0:
                array[i,j]=1
    return array
    
def loadTrainData():
    l=[]
    with open('train.csv') as file:
         lines=csv.reader(file)
         for line in lines:
             l.append(line) #42001*785
    l.remove(l[0])
    l=array(l)
    label=l[:,0]
    data=l[:,1:]
    return nomalizing(toInt(data)),toInt(label)  #label 1*42000  data 42000*784
    #return data,label
    
def loadTestData():
    l=[]
    with open('test.csv') as file:
         lines=csv.reader(file)
         for line in lines:
             l.append(line)
     #28001*784
    l.remove(l[0])
    data=array(l)
    return nomalizing(toInt(data))  #  data 28000*784

def loadTestResult():
    l=[]
    with open('knn_benchmark.csv') as file:
         lines=csv.reader(file)
         for line in lines:
             l.append(line)
     #28001*2
    l.remove(l[0])
    label=array(l)
    return toInt(label[:,1])  #  label 28000*1

#dataSet:m*n   labels:m*1  inX:1*n
def classify(inX, dataSet, labels, k):
    inX=mat(inX)
    dataSet=mat(dataSet)
    labels=mat(labels)
    dataSetSize = dataSet.shape[0]                  
    diffMat = tile(inX, (dataSetSize,1)) - dataSet   
    sqDiffMat = array(diffMat)**2
    sqDistances = sqDiffMat.sum(axis=1)                  
    distances = sqDistances**0.5
    sortedDistIndicies = distances.argsort()            
    classCount={}                                      
    for i in range(k):
        voteIlabel = labels[sortedDistIndicies[i],0]
        classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1
    sortedClassCount = sorted(classCount.iteritems(), key=operator.itemgetter(1), reverse=True)
    return sortedClassCount[0][0]

def saveResult(result):
    with open('result.csv','wb') as myFile:    
        myWriter=csv.writer(myFile)
        for i in result:
            tmp=[]
            tmp.append(i)
            myWriter.writerow(tmp)
        

def handwritingClassTest():
    trainData,trainLabel=loadTrainData()
    testData=loadTestData()
    testLabel=loadTestResult()
    m,n=shape(testData)
    errorCount=0
    resultList=[]
    for i in range(m):
         classifierResult = classify(testData[i], trainData, trainLabel.transpose(), 5)
         resultList.append(classifierResult)
         print "the classifier came back with: %d, the real answer is: %d" % (classifierResult, testLabel[0,i])
         if (classifierResult != testLabel[0,i]): errorCount += 1.0
    print "\nthe total number of errors is: %d" % errorCount
    print "\nthe total error rate is: %f" % (errorCount/float(m))
    saveResult(resultList)
'''    
trainData[0:20000], trainLabel.transpose()[0:20000]
get 20000 of the 42000 samples to train
'''