'''
Created on Jan 8, 2011

@author: Peter
'''
from numpy import *

def loadDataSet(fileName):      #general function to parse tab -delimited floats
    numFeat = len(open(fileName).readline().split('\t')) - 1 #get number of fields 
    dataMat = []; labelMat = []
    fr = open(fileName)
    for line in fr.readlines():
        lineArr =[]
        curLine = line.strip().split('\t')
        for i in range(numFeat):
            lineArr.append(float(curLine[i]))
        dataMat.append(lineArr)
        labelMat.append(float(curLine[-1]))
    return dataMat,labelMat

def standRegres(xArr,yArr):
    xMat = mat(xArr); yMat = mat(yArr).T
    xTx = xMat.T*xMat
    if linalg.det(xTx) == 0.0:
        print "This matrix is singular, cannot do inverse"
        return
    ws = xTx.I * (xMat.T*yMat)
    return ws

def lwlr(testPoint,xArr,yArr,k=1.0):
    xMat = mat(xArr); yMat = mat(yArr).T
    m = shape(xMat)[0]
    weights = mat(eye((m)))
    for j in range(m):                      #next 2 lines create weights matrix
        diffMat = testPoint - xMat[j,:]     #
        weights[j,j] = exp(diffMat*diffMat.T/(-2.0*k**2))
    xTx = xMat.T * (weights * xMat)
    if linalg.det(xTx) == 0.0:
        print "This matrix is singular, cannot do inverse"
        return
    ws = xTx.I * (xMat.T * (weights * yMat))
    return testPoint * ws

def lwlrTest(testArr,xArr,yArr,k=1.0):  #loops over all the data points and applies lwlr to each one
    m = shape(testArr)[0]
    yHat = zeros(m)
    for i in range(m):
        yHat[i] = lwlr(testArr[i],xArr,yArr,k)
    return yHat

def lwlrTestPlot(xArr,yArr,k=1.0):  #same thing as lwlrTest except it sorts X first
    yHat = zeros(shape(yArr))       #easier for plotting
    xCopy = mat(xArr)
    xCopy.sort(0)
    for i in range(shape(xArr)[0]):
        yHat[i] = lwlr(xCopy[i],xArr,yArr,k)
    return yHat,xCopy

def rssError(yArr,yHatArr): #yArr and yHatArr both need to be arrays
    return ((yArr-yHatArr)**2).sum()

def ridgeRegres(xMat,yMat,lam=0.2):
    xTx = xMat.T*xMat
    denom = xTx + eye(shape(xMat)[1])*lam
    if linalg.det(denom) == 0.0:
        print "This matrix is singular, cannot do inverse"
        return
    ws = denom.I * (xMat.T*yMat)
    return ws
    
def ridgeTest(xArr,yArr):
    xMat = mat(xArr); yMat=mat(yArr).T
    yMean = mean(yMat,0)
    yMat = yMat - yMean     #to eliminate X0 take mean off of Y
    #regularize X's
    xMeans = mean(xMat,0)   #calc mean then subtract it off
    xVar = var(xMat,0)      #calc variance of Xi then divide by it
    xMat = (xMat - xMeans)/xVar
    numTestPts = 30
    wMat = zeros((numTestPts,shape(xMat)[1]))
    for i in range(numTestPts):
        ws = ridgeRegres(xMat,yMat,exp(i-10))
        wMat[i,:]=ws.T
    return wMat

def regularize(xMat):#regularize by columns
    inMat = xMat.copy()
    inMeans = mean(inMat,0)   #calc mean then subtract it off
    inVar = var(inMat,0)      #calc variance of Xi then divide by it
    inMat = (inMat - inMeans)/inVar
    return inMat

def stageWise(xArr,yArr,eps=0.01,numIt=100):
    xMat = mat(xArr); yMat=mat(yArr).T
    yMean = mean(yMat,0)
    yMat = yMat - yMean     #can also regularize ys but will get smaller coef
    xMat = regularize(xMat)
    m,n=shape(xMat)
    returnMat = zeros((numIt,n)) #testing code remove
    ws = zeros((n,1)); wsTest = ws.copy(); wsMax = ws.copy()
    for i in range(numIt):#could change this to while loop
        #print ws.T
        lowestError = inf; 
        for j in range(n):
            for sign in [-1,1]:
                wsTest = ws.copy()
                wsTest[j] += eps*sign
                yTest = xMat*wsTest
                rssE = rssError(yMat.A,yTest.A)
                if rssE < lowestError:
                    lowestError = rssE
                    wsMax = wsTest
        ws = wsMax.copy()
        returnMat[i,:]=ws.T
    return returnMat

def scrapePage(inFile,outFile,yr,numPce,origPrc):
    from BeautifulSoup import BeautifulSoup
    fr = open(inFile); fw=open(outFile,'a') #a is append mode writing
    soup = BeautifulSoup(fr.read())
    i=1
    currentRow = soup.findAll('table', r="%d" % i)
    while(len(currentRow)!=0):
        currentRow = soup.findAll('table', r="%d" % i)
        title = currentRow[0].findAll('a')[1].text
        lwrTitle = title.lower()
        if (lwrTitle.find('new') > -1) or (lwrTitle.find('nisb') > -1):
            newFlag = 1.0
        else:
            newFlag = 0.0
        soldUnicde = currentRow[0].findAll('td')[3].findAll('span')
        if len(soldUnicde)==0:
            print "item #%d did not sell" % i
        else:
            soldPrice = currentRow[0].findAll('td')[4]
            priceStr = soldPrice.text
            priceStr = priceStr.replace('$','') #strips out $
            priceStr = priceStr.replace(',','') #strips out ,
            if len(soldPrice)>1:
                priceStr = priceStr.replace('Free shipping', '') #strips out Free Shipping
            print "%s\t%d\t%s" % (priceStr,newFlag,title)
            fw.write("%d\t%d\t%d\t%f\t%s\n" % (yr,numPce,newFlag,origPrc,priceStr))
        i += 1
        currentRow = soup.findAll('table', r="%d" % i)
    fw.close()
    
def setDataCollect():
    scrapePage('setHtml/lego8288.html','out.txt', 2006, 800, 49.99)
    scrapePage('setHtml/lego10030.html','out.txt', 2002, 3096, 269.99)
    scrapePage('setHtml/lego10179.html','out.txt', 2007, 5195, 499.99)
    scrapePage('setHtml/lego10181.html','out.txt', 2007, 3428, 199.99)
    scrapePage('setHtml/lego10189.html','out.txt', 2008, 5922, 299.99)
    scrapePage('setHtml/lego10196.html','out.txt', 2009, 3263, 249.99)
    
def crossValidation(xArr,yArr,numVal=10):
    m = len(yArr)                           
    indexList = range(m)
    errorMat = zeros((numVal,30))#create error mat 30columns numVal rows
    for i in range(numVal):
        trainX=[]; trainY=[]
        testX = []; testY = []
        random.shuffle(indexList)
        for j in range(m):#create training set based on first 90% of values in indexList
            if j < m*0.9: 
                trainX.append(xArr[indexList[j]])
                trainY.append(yArr[indexList[j]])
            else:
                testX.append(xArr[indexList[j]])
                testY.append(yArr[indexList[j]])
        wMat = ridgeTest(trainX,trainY)    #get 30 weight vectors from ridge
        for k in range(30):#loop over all of the ridge estimates
            matTestX = mat(testX); matTrainX=mat(trainX)
            meanTrain = mean(matTrainX,0)
            varTrain = var(matTrainX,0)
            matTestX = (matTestX-meanTrain)/varTrain #regularize test with training params
            yEst = matTestX * mat(wMat[k,:]).T + mean(trainY)#test ridge results and store
            errorMat[i,k]=rssError(yEst.T.A,array(testY))
            #print errorMat[i,k]
    meanErrors = mean(errorMat,0)#calc avg performance of the different ridge weight vectors
    minMean = float(min(meanErrors))
    bestWeights = wMat[nonzero(meanErrors==minMean)]
    #can unregularize to get model
    #when we regularized we wrote Xreg = (x-meanX)/var(x)
    #we can now write in terms of x not Xreg:  x*w/var(x) - meanX/var(x) +meanY
    xMat = mat(xArr); yMat=mat(yArr).T
    meanX = mean(xMat,0); varX = var(xMat,0)
    unReg = bestWeights/varX
    print "the best model from Ridge Regression is:\n",unReg
    print "with constant term: ",-1*sum(multiply(meanX,unReg)) + mean(yMat)
