儒道哲學的浪漫人生

　　該範例摘自<<機器學習-算法原理與編程實踐>>，主要是告訴我們對於資料集的分佈趨勢，我們可以用＂最小二乘法＂求得回歸線(資料分佈趨勢線)，然該法僅適用於線性回歸函數Y = aX + b . 但藉由這個實例亦可以看出我們可以用最小二乘法做為線性回歸資料集的預測算法，從中找出線性資料分佈的合理函數，進而預測資料分佈趨勢。

[資料集]　Input File = regdataset.txt

0.635975 4.093119

0.552438 3.804358

0.855922 4.456531

0.083386 3.187049

0.975802 4.506176

0.181269 3.171914

0.129156 3.053996

0.605648 3.974659

0.301625 3.542525

0.698805 4.234199

0.226419 3.405937

0.519290 3.932469

0.354424 3.514051

0.118380 3.105317

0.512811 3.843351

0.236795 3.576074

0.353509 3.544471

0.481447 3.934625

0.060509 3.228226

0.174090 3.300232

0.806818 4.331785

0.531462 3.908166

0.853167 4.386918

0.304804 3.617260

0.612021 4.082411

0.620880 3.949470

0.580245 3.984041

0.742443 4.251907

0.110770 3.115214

0.742687 4.234319

0.574390 3.947544

0.986378 4.532519

0.294867 3.510392

0.472125 3.927832

0.872321 4.631825

0.843537 4.482263

0.864577 4.487656

0.341874 3.486371

0.097980 3.137514

0.757874 4.212660

0.877656 4.506268

0.457993 3.800973

0.475341 3.975979

0.848391 4.494447

0.746059 4.244715

0.153462 3.019251

0.694256 4.277945

0.498712 3.812414

0.023580 3.116973

0.976826 4.617363

0.624004 4.005158

0.472220 3.874188

0.390551 3.630228

0.021349 3.145849

0.173488 3.192618

0.971028 4.540226

0.595302 3.835879

0.097638 3.141948

0.745972 4.323316

0.676390 4.204829

0.488949 3.946710

0.982873 4.666332

0.296060 3.482348

0.228008 3.451286

0.671059 4.186388

0.379419 3.595223

0.285170 3.534446

0.236314 3.420891

0.629803 4.115553

0.770272 4.257463

0.493052 3.934798

0.631592 4.154963

0.965676 4.587470

0.598675 3.944766

0.351997 3.480517

0.342001 3.481382

0.661424 4.253286

0.140912 3.131670

0.373574 3.527099

0.223166 3.378051

0.908785 4.578960

0.915102 4.551773

0.410940 3.634259

0.754921 4.167016

0.764453 4.217570

0.101534 3.237201

0.780368 4.353163

0.819868 4.342184

0.173990 3.236950

0.330472 3.509404

0.162656 3.242535

0.476283 3.907937

0.636391 4.108455

0.758737 4.181959

0.778372 4.251103

0.936287 4.538462

0.510904 3.848193

0.515737 3.974757

0.437823 3.708323

0.828607 4.385210

0.556100 3.927788

0.038209 3.187881

0.321993 3.444542

0.067288 3.199263

0.774989 4.285745

0.566077 3.878557

0.796314 4.155745

0.746600 4.197772

0.360778 3.524928

0.397321 3.525692

0.062142 3.211318

0.379250 3.570495

0.248238 3.462431

0.682561 4.206177

0.355393 3.562322

0.889051 4.595215

0.733806 4.182694

0.153949 3.320695

0.036104 3.122670

0.388577 3.541312

0.274481 3.502135

0.319401 3.537559

0.431653 3.712609

0.960398 4.504875

0.083660 3.262164

0.122098 3.105583

0.415299 3.742634

0.854192 4.566589

0.925574 4.630884

0.109306 3.190539

0.805161 4.289105

0.344474 3.406602

0.769116 4.251899

0.182003 3.183214

0.225972 3.342508

0.413088 3.747926

0.964444 4.499998

0.203334 3.350089

0.285574 3.539554

0.850209 4.443465

0.061561 3.290370

0.426935 3.733302

0.389376 3.614803

0.096918 3.175132

0.148938 3.164284

0.893738 4.619629

0.195527 3.426648

0.407248 3.670722

0.224357 3.412571

0.045963 3.110330

0.944647 4.647928

0.756552 4.164515

0.432098 3.730603

0.990511 4.609868

0.649699 4.094111

0.584879 3.907636

0.785934 4.240814

0.029945 3.106915

0.075747 3.201181

0.408408 3.872302

0.583851 3.860890

0.497759 3.884108

0.421301 3.696816

0.140320 3.114540

0.546465 3.791233

0.843181 4.443487

0.295390 3.535337

0.825059 4.417975

0.946343 4.742471

0.350404 3.470964

0.042787 3.113381

0.352487 3.594600

0.590736 3.914875

0.120748 3.108492

0.143140 3.152725

0.511926 3.994118

0.496358 3.933417

0.382802 3.510829

0.252464 3.498402

0.845894 4.460441

0.132023 3.245277

0.442301 3.771067

0.266889 3.434771

0.008575 2.999612

0.897632 4.454221

0.533171 3.985348

0.285243 3.557982

0.377258 3.625972

0.486995 3.922226

0.305993 3.547421

0.277528 3.580944

0.750899 4.268081

0.694756 4.278096

0.870158 4.517640

0.276457 3.555461

0.017761 3.055026

0.802046 4.354819

0.559275 3.894387

0.941305 4.597773

0.856877 4.523616

[python] minsqrt.py

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from numpy import *
import matplotlib.pyplot as plt

def loadDataSet(filename):
    X = []
    Y = []
    fr = open(filename)
    """
    File data sample:
0.635975    4.093119
0.552438    3.804358
0.855922    4.456531
0.083386    3.187049
0.975802    4.506176
    """
    for line in fr.readlines():
        curLine = line.strip().split('\t')   #0.635975  4.093119
        X.append(float(curLine[0])) #0.635975
        Y.append(float(curLine[1])) #4.093119
    return X,Y

def plotscatter(Xmat,Ymat,a,b,plt):
    fig = plt.figure()
    ax = fig.add_subplot(111)
    ax.scatter(Xmat,Ymat,c='blue',marker='o')
    Xmat.sort()
    Yhat = [a*float(xi)+b for xi in Xmat] #calculate predict value

    plt.plot(Xmat,Yhat,'r')
    plt.show()

    return yhat

if __name__ == "__main__":

    Xmat, Ymat = loadDataSet('regdataset.txt')
    meanX = mean(Xmat)
    meanY = mean(Ymat)
    dx = Xmat - meanX
    dy = Ymat - meanY

    sumXY = vdot(dx,dy)
    sqX = sum(power(dx,2))

    a = sumXY/sqX
    b = meanY - a*meanX

    print("%f,%f"%(a,b))

    plotscatter(Xmat,Ymat,a,b,plt)

[結果]

PS D:\Lab\ScriptLab\ML> python minsqrt.py

1.668743,              3.007722

2018年9月17日星期一