局部加权回归

代码如下：

# coding=utf-8 from numpy import *import numpy as npimport pylab as pl#处理数据def loadDataSet(fileName):    numFeat = len(open(fileName).readline().split('\t')) - 1     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#求testpoint这一坐标的局部加权后的预测数值def lwlr(testPoint,xArr,yArr,k=1.0):    xMat = mat(xArr)    yMat = mat(yArr).T    m = shape(xMat)[0]    #构造m大小的单位矩阵    weights = mat(eye((m)))    for j in range(m):        #print 'testPoint:',testPoint        #print 'xMat[j,:]:',xMat[j,:]             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 * wsdef lwlrTest(testArr,xArr,yArr,k):    m = shape(testArr)[0]    yHat = zeros(m)    for i in range(m):        yHat[i] = lwlr(testArr[i],xArr,yArr,k)    return yHatdef show(yHat1, yHat2, yHat3, xArr, yArr):    xArr = array(xArr)    yArr = array(yArr)    xMat = mat(xArr)    srtInd = xMat[:,1].argsort(0)    xSort = xMat[srtInd][:,0,:]    pl.figure(figsize = (8,10))    pl.subplot(311)    pl.plot(xArr[:, 1], yArr, '.', color = 'g')    pl.plot(xSort[:,1], yHat1[srtInd], 'r')    pl.title("k=1.0")        pl.subplot(312)    pl.plot(xArr[:, 1], yArr, '.', color = 'g')    pl.plot(xSort[:,1], yHat2[srtInd], 'r')    pl.title("k=0.01")        pl.subplot(313)    pl.plot(xArr[:, 1], yArr, '.', color = 'g')    pl.plot(xSort[:,1], yHat3[srtInd], 'r')    pl.title("k=0.003")    pl.show()def main():    xArr,yArr = loadDataSet(r"C:\Users\l\Desktop\ex0.txt")    yHat1 = lwlrTest(xArr,xArr, yArr, 1.0)    yHat2 = lwlrTest(xArr,xArr, yArr, 0.01)    yHat3 = lwlrTest(xArr,xArr, yArr, 0.003)    show(yHat1,yHat2, yHat3, xArr, yArr)    if __name__ == '__main__':    main();

数据：

1.000000 0.067732 3.176513 1.000000 0.427810 3.816464 1.000000 0.995731 4.550095 1.000000 0.738336 4.256571 1.000000 0.981083 4.560815 1.000000 0.526171 3.929515 1.000000 0.378887 3.526170 1.000000 0.033859 3.156393 1.000000 0.132791 3.110301 1.000000 0.138306 3.149813 1.000000 0.247809 3.476346 1.000000 0.648270 4.119688 1.000000 0.731209 4.282233 1.000000 0.236833 3.486582 1.000000 0.969788 4.655492 1.000000 0.607492 3.965162 1.000000 0.358622 3.514900 1.000000 0.147846 3.125947 1.000000 0.637820 4.094115 1.000000 0.230372 3.476039 1.000000 0.070237 3.210610 1.000000 0.067154 3.190612 1.000000 0.925577 4.631504 1.000000 0.717733 4.295890 1.000000 0.015371 3.085028 1.000000 0.335070 3.448080 1.000000 0.040486 3.167440 1.000000 0.212575 3.364266 1.000000 0.617218 3.993482 1.000000 0.541196 3.891471 1.000000 0.045353 3.143259 1.000000 0.126762 3.114204 1.000000 0.556486 3.851484 1.000000 0.901144 4.621899 1.000000 0.958476 4.580768 1.000000 0.274561 3.620992 1.000000 0.394396 3.580501 1.000000 0.872480 4.618706 1.000000 0.409932 3.676867 1.000000 0.908969 4.641845 1.000000 0.166819 3.175939 1.000000 0.665016 4.264980 1.000000 0.263727 3.558448 1.000000 0.231214 3.436632 1.000000 0.552928 3.831052 1.000000 0.047744 3.182853 1.000000 0.365746 3.498906 1.000000 0.495002 3.946833 1.000000 0.493466 3.900583 1.000000 0.792101 4.238522 1.000000 0.769660 4.233080 1.000000 0.251821 3.521557 1.000000 0.181951 3.203344 1.000000 0.808177 4.278105 1.000000 0.334116 3.555705 1.000000 0.338630 3.502661 1.000000 0.452584 3.859776 1.000000 0.694770 4.275956 1.000000 0.590902 3.916191 1.000000 0.307928 3.587961 1.000000 0.148364 3.183004 1.000000 0.702180 4.225236 1.000000 0.721544 4.231083 1.000000 0.666886 4.240544 1.000000 0.124931 3.222372 1.000000 0.618286 4.021445 1.000000 0.381086 3.567479 1.000000 0.385643 3.562580 1.000000 0.777175 4.262059 1.000000 0.116089 3.208813 1.000000 0.115487 3.169825 1.000000 0.663510 4.193949 1.000000 0.254884 3.491678 1.000000 0.993888 4.533306 1.000000 0.295434 3.550108 1.000000 0.952523 4.636427 1.000000 0.307047 3.557078 1.000000 0.277261 3.552874 1.000000 0.279101 3.494159 1.000000 0.175724 3.206828 1.000000 0.156383 3.195266 1.000000 0.733165 4.221292 1.000000 0.848142 4.413372 1.000000 0.771184 4.184347 1.000000 0.429492 3.742878 1.000000 0.162176 3.201878 1.000000 0.917064 4.648964 1.000000 0.315044 3.510117 1.000000 0.201473 3.274434 1.000000 0.297038 3.579622 1.000000 0.336647 3.489244 1.000000 0.666109 4.237386 1.000000 0.583888 3.913749 1.000000 0.085031 3.228990 1.000000 0.687006 4.286286 1.000000 0.949655 4.628614 1.000000 0.189912 3.239536 1.000000 0.844027 4.457997 1.000000 0.333288 3.513384 1.000000 0.427035 3.729674 1.000000 0.466369 3.834274 1.000000 0.550659 3.811155 1.000000 0.278213 3.598316 1.000000 0.918769 4.692514 1.000000 0.886555 4.604859 1.000000 0.569488 3.864912 1.000000 0.066379 3.184236 1.000000 0.335751 3.500796 1.000000 0.426863 3.743365 1.000000 0.395746 3.622905 1.000000 0.694221 4.310796 1.000000 0.272760 3.583357 1.000000 0.503495 3.901852 1.000000 0.067119 3.233521 1.000000 0.038326 3.105266 1.000000 0.599122 3.865544 1.000000 0.947054 4.628625 1.000000 0.671279 4.231213 1.000000 0.434811 3.791149 1.000000 0.509381 3.968271 1.000000 0.749442 4.253910 1.000000 0.058014 3.194710 1.000000 0.482978 3.996503 1.000000 0.466776 3.904358 1.000000 0.357767 3.503976 1.000000 0.949123 4.557545 1.000000 0.417320 3.699876 1.000000 0.920461 4.613614 1.000000 0.156433 3.140401 1.000000 0.656662 4.206717 1.000000 0.616418 3.969524 1.000000 0.853428 4.476096 1.000000 0.133295 3.136528 1.000000 0.693007 4.279071 1.000000 0.178449 3.200603 1.000000 0.199526 3.299012 1.000000 0.073224 3.209873 1.000000 0.286515 3.632942 1.000000 0.182026 3.248361 1.000000 0.621523 3.995783 1.000000 0.344584 3.563262 1.000000 0.398556 3.649712 1.000000 0.480369 3.951845 1.000000 0.153350 3.145031 1.000000 0.171846 3.181577 1.000000 0.867082 4.637087 1.000000 0.223855 3.404964 1.000000 0.528301 3.873188 1.000000 0.890192 4.633648 1.000000 0.106352 3.154768 1.000000 0.917886 4.623637 1.000000 0.014855 3.078132 1.000000 0.567682 3.913596 1.000000 0.068854 3.221817 1.000000 0.603535 3.938071 1.000000 0.532050 3.880822 1.000000 0.651362 4.176436 1.000000 0.901225 4.648161 1.000000 0.204337 3.332312 1.000000 0.696081 4.240614 1.000000 0.963924 4.532224 1.000000 0.981390 4.557105 1.000000 0.987911 4.610072 1.000000 0.990947 4.636569 1.000000 0.736021 4.229813 1.000000 0.253574 3.500860 1.000000 0.674722 4.245514 1.000000 0.939368 4.605182 1.000000 0.235419 3.454340 1.000000 0.110521 3.180775 1.000000 0.218023 3.380820 1.000000 0.869778 4.565020 1.000000 0.196830 3.279973 1.000000 0.958178 4.554241 1.000000 0.972673 4.633520 1.000000 0.745797 4.281037 1.000000 0.445674 3.844426 1.000000 0.470557 3.891601 1.000000 0.549236 3.849728 1.000000 0.335691 3.492215 1.000000 0.884739 4.592374 1.000000 0.918916 4.632025 1.000000 0.441815 3.756750 1.000000 0.116598 3.133555 1.000000 0.359274 3.567919 1.000000 0.814811 4.363382 1.000000 0.387125 3.560165 1.000000 0.982243 4.564305 1.000000 0.780880 4.215055 1.000000 0.652565 4.174999 1.000000 0.870030 4.586640 1.000000 0.604755 3.960008 1.000000 0.255212 3.529963 1.000000 0.730546 4.213412 1.000000 0.493829 3.908685 1.000000 0.257017 3.585821 1.000000 0.833735 4.374394 1.000000 0.070095 3.213817 1.000000 0.527070 3.952681 1.000000 0.116163 3.129283