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python回归输出函数 python 回归

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怎么看python中逻辑回归输出的解释以下为python代码，由于训练数据比较少，这边使用了批处理梯度下降法，没有使用增量梯度下降法 。
##author:lijiayan##data:2016/10/27
##name:logReg.pyfrom numpy import *import matplotlib.pyplot as pltdef loadData(filename):
data = https://www.04ip.com/post/loadtxt(filename)
m,n = data.shapeprint 'the number ofexamples:',mprint 'the number of features:',n-1x = data[:,0:n-1]
y = data[:,n-1:n]return x,y#the sigmoid functiondef sigmoid(z):return 1.0 / (1 + exp(-z))#the cost functiondef costfunction(y,h):
y = array(y)
h = array(h)
J = sum(y*log(h))+sum((1-y)*log(1-h))return J# the batch gradient descent algrithmdef gradescent(x,y):
m,n = shape(x)#m: number of training example; n: number of featuresx = c_[ones(m),x]#add x0x = mat(x)# to matrixy = mat(y)
a = 0.0000025# learning ratemaxcycle = 4000theta = zeros((n+1,1))#initial thetaJ = []for i in range(maxcycle):
h = sigmoid(x*theta)
theta = theta + a * (x.T)*(y-h)
cost = costfunction(y,h)
J.append(cost)
plt.plot(J)
plt.show()return theta,cost#the stochastic gradient descent (m should be large,if you want the result is good)def stocGraddescent(x,y):
m,n = shape(x)#m: number of training example; n: number of featuresx = c_[ones(m),x]#add x0x = mat(x)# to matrixy = mat(y)
a = 0.01# learning ratetheta = ones((n+1,1))#initial thetaJ = []for i in range(m):
h = sigmoid(x[i]*theta)
theta = theta + a * x[i].transpose()*(y[i]-h)
cost = costfunction(y,h)
J.append(cost)
plt.plot(J)
plt.show()return theta,cost#plot the decision boundarydef plotbestfit(x,y,theta):
plt.plot(x[:,0:1][where(y==1)],x[:,1:2][where(y==1)],'ro')
plt.plot(x[:,0:1][where(y!=1)],x[:,1:2][where(y!=1)],'bx')
x1= arange(-4,4,0.1)
x2 =(-float(theta[0])-float(theta[1])*x1) /float(theta[2])
plt.plot(x1,x2)
plt.xlabel('x1')
plt.ylabel(('x2'))
plt.show()def classifyVector(inX,theta):
prob = sigmoid((inX*theta).sum(1))return where(prob = 0.5, 1, 0)def accuracy(x, y, theta):
m = shape(y)[0]
x = c_[ones(m),x]
y_p = classifyVector(x,theta)
accuracy = sum(y_p==y)/float(m)return accuracy
调用上面代码：
from logReg import *
x,y = loadData("horseColicTraining.txt")
theta,cost = gradescent(x,y)print 'J:',cost
ac_train = accuracy(x, y, theta)print 'accuracy of the training examples:', ac_train
x_test,y_test = loadData('horseColicTest.txt')
ac_test = accuracy(x_test, y_test, theta)print 'accuracy of the test examples:', ac_test
学习速率=0.0000025，迭代次数=4000时的结果：
似然函数走势（J = sum(y*log(h))+sum((1-y)*log(1-h))） ， 似然函数是求最大值，一般是要稳定了才算最好 。
下图为计算结果，可以看到训练集的准确率为73%，测试集的准确率为78% 。
这个时候，我去看了一下数据集，发现没个特征的数量级不一致，于是我想到要进行归一化处理：
归一化处理句修改列loadData(filename)函数：
def loadData(filename):
data = https://www.04ip.com/post/loadtxt(filename)
m,n = data.shapeprint 'the number ofexamples:',mprint 'the number of features:',n-1x = data[:,0:n-1]
max = x.max(0)
min = x.min(0)
x = (x - min)/((max-min)*1.0)#scalingy = data[:,n-1:n]return x,y
在没有归一化的时候，我的学习速率取了0.0000025（加大就会震荡 ， 因为有些特征的值很大，学习速率取的稍大，波动就很大），由于学习速率?。?000次也没有完全稳定 。现在当把特征归一化后（所有特征的值都在0~1之间），这样学习速率可以加大 ， 迭代次数就可以大大减少 ， 以下是学习速率=0.005，迭代次数=500的结果：
此时的训练集的准确率为72%，测试集的准确率为73%

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