逻辑回归示例

简单逻辑回归示例（结合公式容易理解），希望对你有帮助！

import numpy as np
import matplotlib.pyplot as plt

file='testSet.txt'

X=[]
Y=[]

def readData(file):
with open(file) as read:
data=read.readlines()
for line in data:
con=line.strip().split()
X.append([1.0,float(con[0]),float(con[1])])
Y.append(float(con[2]))
return np.mat(X),np.mat(Y).transpose()

def model(x):
return (1.0)/(1+np.exp(-x))

def costfunction(x,y,opt):
m,xl=np.shape(x)
alpha=opt['alpha']
iter_num=opt['iter_num']
theta=np.ones((xl,1))
for i in range(iter_num):
cost=model(x*theta)
h=y-cost
theta=theta+alpha*x.T*h
return theta

def matlib(x,y,theta):
xh,xl=np.shape(x)  #行num 列num
theta=theta.getA() #经实验将矩阵转化数组才能操作
versicolor = np.where(y == 1);
virginica = np.where(y == 0);
plt.plot(x[versicolor, 1], x[versicolor, 2],'rx');
plt.plot(x[virginica, 1], x[virginica, 2],'ro');
min_x = min(x[:, 1])[0, 0]  # 只有用[0,0]才能得到一个数值
max_x = max(x[:, 1])[0, 0]
y_min_x = float(-theta[0] - theta[1] * min_x) / theta[2]  # 这是令sigmoid函数的输入为零，得到分界处的值
y_max_x = float(-theta[0] - theta[1] * max_x) / theta[2]
plt.plot([min_x, max_x], [y_min_x, y_max_x], '-g')
plt.xlabel = ('X1');
plt.ylabel = ('X2')
plt.show()

def predict(theta, X):
m, n = np.shape(X)
p = np.zeros(shape=(m, 1));
h = model(X.dot(theta));
p = 1*(h>=0.5);
return p

X,Y=readData(file)
opt={'alpha':0.01,'iter_num':2000}
theta=costfunction(X,Y,opt)
print("递减后theta=",theta)
p = predict(theta, X);
print ('The classify accuracy is: %.3f%%' % ((Y[np.where(p == Y)].size / Y.size) * 100.0));
matlib(X,Y,theta)

运行结果：





训练样本testSet.txt，放到项目根目录下，不然file要写全路径名‘\\分割’

-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.
8b58
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