数据
32.502345269453031,31.70700584656992 53.426804033275019,68.77759598163891 61.530358025636438,62.562382297945803 47.475639634786098,71.546632233567777 59.813207869512318,87.230925133687393 55.142188413943821,78.211518270799232 52.211796692214001,79.64197304980874 39.299566694317065,59.171489321869508 48.10504169176825,75.331242297063056 52.550014442733818,71.300879886850353 45.419730144973755,55.165677145959123 54.351634881228918,82.478846757497919 44.164049496773352,62.008923245725825 58.16847071685779,75.392870425994957 56.727208057096611,81.43619215887864 48.955888566093719,60.723602440673965 44.687196231480904,82.892503731453715 60.297326851333466,97.379896862166078 45.618643772955828,48.847153317355072 38.816817537445637,56.877213186268506 66.189816606752601,83.878564664602763 65.41605174513407,118.59121730252249 47.48120860786787,57.251819462268969 41.57564261748702,51.391744079832307 51.84518690563943,75.380651665312357 59.370822011089523,74.765564032151374 57.31000343834809,95.455052922574737 63.615561251453308,95.229366017555307 46.737619407976972,79.052406169565586 50.556760148547767,83.432071421323712 52.223996085553047,63.358790317497878 35.567830047746632,41.412885303700563 42.436476944055642,76.617341280074044 58.16454011019286,96.769566426108199 57.504447615341789,74.084130116602523 45.440530725319981,66.588144414228594 61.89622268029126,77.768482417793024 33.093831736163963,50.719588912312084 36.436009511386871,62.124570818071781 37.675654860850742,60.810246649902211 44.555608383275356,52.682983366387781 43.318282631865721,58.569824717692867 50.073145632289034,82.905981485070512 43.870612645218372,61.424709804339123 62.997480747553091,115.24415280079529 32.669043763467187,45.570588823376085 40.166899008703702,54.084054796223612 53.575077531673656,87.994452758110413 33.864214971778239,52.725494375900425 64.707138666121296,93.576118692658241 38.119824026822805,80.166275447370964 44.502538064645101,65.101711570560326 40.599538384552318,65.562301260400375 41.720676356341293,65.280886920822823 51.088634678336796,73.434641546324301 55.078095904923202,71.13972785861894 41.377726534895203,79.102829683549857 62.494697427269791,86.520538440347153 49.203887540826003,84.742697807826218 41.102685187349664,59.358850248624933 41.182016105169822,61.684037524833627 50.186389494880601,69.847604158249183 52.378446219236217,86.098291205774103 50.135485486286122,59.108839267699643 33.644706006191782,69.89968164362763 39.557901222906828,44.862490711164398 56.130388816875467,85.498067778840223 57.362052133238237,95.536686846467219 60.269214393997906,70.251934419771587 35.678093889410732,52.721734964774988 31.588116998132829,50.392670135079896 53.66093226167304,63.642398775657753 46.682228649471917,72.247251068662365 43.107820219102464,57.812512976181402 70.34607561504933,104.25710158543822 44.492855880854073,86.642020318822006 57.50453330326841,91.486778000110135 36.930076609191808,55.231660886212836 55.805733357942742,79.550436678507609 38.954769073377065,44.847124242467601 56.901214702247074,80.207523139682763 56.868900661384046,83.14274979204346 34.33312470421609,55.723489260543914 59.04974121466681,77.634182511677864 57.788223993230673,99.051414841748269 54.282328705967409,79.120646274680027 51.088719898979143,69.588897851118475 50.282836348230731,69.510503311494389 44.211741752090113,73.687564318317285 38.005488008060688,61.366904537240131 32.940479942618296,67.170655768995118 53.691639571070056,85.668203145001542 68.76573426962166,114.85387123391394 46.230966498310252,90.123572069967423 68.319360818255362,97.919821035242848 50.030174340312143,81.536990783015028 49.239765342753763,72.111832469615663 50.039575939875988,85.232007342325673 48.149858891028863,66.224957888054632 25.128484647772304,53.454394214850524
import numpy as np import matplotlib.pyplot as plt
#导入数据 data = np.genfromtxt("data.csv",delimiter=',') x_data = data[:,0] y_data = data[:,1]
#散点图 plt.scatter(x_data,y_data) lr=0.000001 b=0 k=0 epochs=100
#评价函数 def compute_error(b,k): err=0 for i in range(len(x_data)): err+=(y_data[i]-(k*x_data[i]+b))**2 return err/float(len(x_data))/2
#梯度下降法，评价函数求偏倒数，求k,b使得评价函数最小
def gradient_descent_runner(x_data,y_data,b,k,lr,epochs): m=len(x_data) for j in range(epochs): tmp_b=0 tmp_k=0 for i in range(m): tmp_b+=k*x_data[i]+b-y_data[i] tmp_k+=(k*x_data[i]+b-y_data[i])*x_data[i] b-=lr*tmp_b k-=lr*tmp_k return b,k b,k=gradient_descent_runner(x_data,y_data,b,k,lr,epochs) plt.plot(x_data, y_data, 'b.') plt.plot(x_data, k * x_data + b, 'r') plt.show()