python對邏輯回歸進行顯著性_python實作邏輯回歸

代價函數，原理參考

代碼

import re

import matplotlib.pyplotasplt

import numpyasnp

import pandasaspdfromnumpy import exp

def data():#讀取txt文本内的資料并轉成需要的list

f=open('data.txt')

data=[]for each_line inf:

each_line=each_line.strip('\n')

res=re.split(" ",each_line)

res=list(filter(None,res))

res= list(map(float,res))

data.append(res)for i inrange(len(data)):#加一個偏執項

data[i].insert(0,1)

f.close()returndata

def sin_cos(data,weights):#畫圖

x0=[]

x1=[]

y0=[]

y1=[]for i indata:if(i[3]==0):

x0.append(i[1])

y0.append(i[2])else:

x1.append(i[1])

y1.append(i[2])

fig=plt.figure()

ax= fig.add_subplot(111)

ax.scatter(x0,y0,marker='+',label='1',s=40,c='r')

ax.scatter(x1,y1,marker='*',label='1',s=40,c='b')

x= np.arange(-3, 3, 0.1)

y= (-weights[0, 0] - weights[1, 0] * x) / weights[2, 0] #matix

ax.plot(x, y)

plt.xlabel('X1')

plt.ylabel('X2')

plt.savefig('test.png')

plt.show()

def sigmoid(inX):#邏輯函數return 1.0/(1+exp(-inX))

'''def to_csv(data):

name=['x1','x2','y']

test=pd.DataFrame(columns=name,data=data)

test.to_csv('data.csv',encoding='utf-8')'''

def tiduxiajiang_sigmoid(data):

x=[]

y=[]for i inrange(len(data)):

x.append(data[i][:-1])

y.append(data[i][-1:])

alpha=0.002maxcycle=100x=np.mat(x)#轉成矩陣

y=np.mat(y)

x2=x.T#矩陣轉逆

weights=np.mat([[1],[1],[1]])for i inrange(maxcycle):

h= sigmoid(x*weights)

error=y-h

weights=weights+alpha*x2*error

print(sum(error))#看是否收斂returnweights

data=data()

weights=tiduxiajiang_sigmoid(data)

sin_cos(data,weights)

print('\n')

print(weights)

#to_csv(data)

error輸出:

[[-36.41425331]]

[[-12.72376078]]

[[33.81527249]]

[[22.76406708]]

[[13.06316319]]

[[18.43125645]]

[[13.940586]]

[[16.64375254]]

[[14.0792666]]

[[15.48731269]]

[[13.90872727]]

[[14.62827295]]

[[13.59898126]]

[[13.93720427]]

[[13.23050463]]

[[13.35316688]]

[[12.84395627]]

[[12.84416803]]

[[12.46028295]]

[[12.3918111]]

[[12.09008779]]

[[11.98454135]]

[[11.73835456]]

[[11.61447392]]

[[11.4069633]]

[[11.27583756]]

[[11.09608423]]

[[10.96417886]]

[[10.80497149]]

[[10.67593363]]

[[10.53242193]]

[[10.40818021]]

[[10.27704098]]

[[10.15848557]]

[[10.03739503]]

[[9.92480135]]

[[9.81209603]]

[[9.70538874]]

[[9.59984542]]

[[9.49876158]]

[[9.39945381]]

[[9.30364189]]

[[9.20984624]]

[[9.11892452]]

[[9.03005934]]

[[8.94364882]]

[[8.85923404]]

[[8.77697561]]

[[8.6966063]]

[[8.61816858]]

[[8.54149713]]

[[8.46657885]]

[[8.39330291]]

[[8.32163239]]

[[8.25148629]]

[[8.18281944]]

[[8.11556799]]

[[8.04968572]]

[[7.98511945]]

[[7.92182501]]

[[7.85975645]]

[[7.79887283]]

[[7.73913354]]

[[7.68050112]]

[[7.62293922]]

[[7.5664137]]

[[7.51089176]]

[[7.45634232]]

[[7.40273562]]

[[7.35004333]]

[[7.29823835]]

[[7.2472948]]

[[7.19718792]]

[[7.14789401]]

[[7.09939038]]

[[7.0516553]]

[[7.0046679]]

[[6.9584082]]

[[6.91285699]]

[[6.86799582]]

[[6.82380698]]

[[6.78027341]]

[[6.73737871]]

[[6.6951071]]

[[6.65344337]]

[[6.61237286]]

[[6.57188145]]

[[6.53195552]]

[[6.4925819]]

[[6.45374791]]

[[6.41544128]]

[[6.37765016]]

[[6.34036308]]

[[6.30356898]]

[[6.26725712]]

[[6.23141712]]

[[6.19603895]]

[[6.16111286]]

[[6.12662941]]

[[6.09257947]]

達到收斂的目的

weights輸出

[[ 2.78742697]

[ 0.36340767]

[-0.45020801]]

結果圖;

data.txt樣式；

-0.017612 14.053064 0

-1.395634 4.662541 1

-0.752157 6.538620 0

-1.322371 7.152853 0

0.423363 11.054677 0

0.406704 7.067335 1

0.667394 12.741452 0

-2.460150 6.866805 1

0.569411 9.548755 0

-0.026632 10.427743 0

0.850433 6.920334 1

1.347183 13.175500 0

1.176813 3.167020 1

-1.781871 9.097953 0

-0.566606 5.749003 1

0.931635 1.589505 1

-0.024205 6.151823 1

-0.036453 2.690988 1

-0.196949 0.444165 1

1.014459 5.754399 1

1.985298 3.230619 1

-1.693453 -0.557540 1

-0.576525 11.778922 0

-0.346811 -1.678730 1

-2.124484 2.672471 1

1.217916 9.597015 0

-0.733928 9.098687 0

-3.642001 -1.618087 1

0.315985 3.523953 1

1.416614 9.619232 0

-0.386323 3.989286 1

0.556921 8.294984 1

1.224863 11.587360 0

-1.347803 -2.406051 1

1.196604 4.951851 1

0.275221 9.543647 0

0.470575 9.332488 0

-1.889567 9.542662 0

-1.527893 12.150579 0

-1.185247 11.309318 0

-0.445678 3.297303 1

1.042222 6.105155 1

-0.618787 10.320986 0

1.152083 0.548467 1

0.828534 2.676045 1

-1.237728 10.549033 0

-0.683565 -2.166125 1

0.229456 5.921938 1

-0.959885 11.555336 0

0.492911 10.993324 0

0.184992 8.721488 0

-0.355715 10.325976 0

-0.397822 8.058397 0

0.824839 13.730343 0

1.507278 5.027866 1

0.099671 6.835839 1

-0.344008 10.717485 0

1.785928 7.718645 1

-0.918801 11.560217 0

-0.364009 4.747300 1

-0.841722 4.119083 1

0.490426 1.960539 1

-0.007194 9.075792 0

0.356107 12.447863 0

0.342578 12.281162 0

-0.810823 -1.466018 1

2.530777 6.476801 1

1.296683 11.607559 0

0.475487 12.040035 0

-0.783277 11.009725 0

0.074798 11.023650 0

-1.337472 0.468339 1

-0.102781 13.763651 0

-0.147324 2.874846 1

0.518389 9.887035 0

1.015399 7.571882 0

-1.658086 -0.027255 1

1.319944 2.171228 1

2.056216 5.019981 1

-0.851633 4.375691 1

-1.510047 6.061992 0

-1.076637 -3.181888 1

1.821096 10.283990 0

3.010150 8.401766 1

-1.099458 1.688274 1

-0.834872 -1.733869 1

-0.846637 3.849075 1

1.400102 12.628781 0

1.752842 5.468166 1

0.078557 0.059736 1

0.089392 -0.715300 1

1.825662 12.693808 0

0.197445 9.744638 0

0.126117 0.922311 1

-0.679797 1.220530 1

0.677983 2.556666 1

0.761349 10.693862 0

-2.168791 0.143632 1

1.388610 9.341997 0

0.317029 14.739025 0