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 npimport matplotlib.pyplot as pltfile='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 thetadef 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 pX,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
-0.683565 -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