吴裕雄--天生自然python机器学习：Logistic回归
内容导读
互联网集市收集整理的这篇技术教程文章主要介绍了吴裕雄--天生自然python机器学习：Logistic回归，小编现在分享给大家，供广大互联网技能从业者学习和参考。文章包含15039字，纯文字阅读大概需要22分钟。
内容图文
假设现在有一些数据点，我们用
一条直线对这些点进行拟合（该线称为最佳拟合直线），这个拟合过程就称作回归。利用Logistic
回归进行分类的主要思想是：根据现有数据对分类边界线建立回归公式，以此进行分类。这里的
“ 回归” 一词源于最佳拟合，表示要找到最佳拟合参数集。
训练分类器时的做法就是寻找最佳拟合参数，使用的是最优化算法。
基于Logistic回归和Sigmoid函数的分类
import sys
from pylab import *
t = arange(-60.0, 60.3, 0.1)
s = 1/(1 + exp(-t))
ax = subplot(211)
ax.plot(t,s)
ax.axis([-5,5,0,1])
plt.xlabel('x')
plt.ylabel('Sigmoid(x)')
ax = subplot(212)
ax.plot(t,s)
ax.axis([-60,60,0,1])
plt.xlabel('x')
plt.ylabel('Sigmoid(x)')
show()
基于最优化方法的最佳回归系数确定
梯度上升法
梯度上升法基于的思想是：要找到某函数的
最大值，最好的方法是沿着该函数的梯度方向探寻。
import matplotlib
import numpy as np
import matplotlib.cm as cm
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
leafNode = dict(boxstyle="round4", fc="0.8")
arrow_args = dict(arrowstyle="<-")
matplotlib.rcParams['xtick.direction'] = 'out'
matplotlib.rcParams['ytick.direction'] = 'out'
delta = 0.025
x = np.arange(-2.0, 2.0, delta)
y = np.arange(-2.0, 2.0, delta)
X, Y = np.meshgrid(x, y)
Z1 = -((X-1)**2)
Z2 = -(Y**2)
#Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
#Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
# difference of Gaussians
Z = 1.0 * (Z2 + Z1)+5.0
# Create a simple contour plot with labels using default colors. The
# inline argument to clabel will control whether the labels are draw
# over the line segments of the contour, removing the lines beneath
# the label
plt.figure()
CS = plt.contour(X, Y, Z)
plt.annotate('', xy=(0.05, 0.05), xycoords='axes fraction',xytext=(0.2,0.2), textcoords='axes fraction',va="center", ha="center", bbox=leafNode, arrowprops=arrow_args )
plt.text(-1.9, -1.8, 'P0')
plt.annotate('', xy=(0.2,0.2), xycoords='axes fraction',xytext=(0.35,0.3), textcoords='axes fraction',va="center", ha="center", bbox=leafNode, arrowprops=arrow_args )
plt.text(-1.35, -1.23, 'P1')
plt.annotate('', xy=(0.35,0.3), xycoords='axes fraction',xytext=(0.45,0.35), textcoords='axes fraction',va="center", ha="center", bbox=leafNode, arrowprops=arrow_args )
plt.text(-0.7, -0.8, 'P2')
plt.text(-0.3, -0.6, 'P3')
plt.clabel(CS, inline=1, fontsize=10)
plt.title('Gradient Ascent')
plt.xlabel('x')
plt.ylabel('y')
plt.show()
可以看到，梯度算子总是指向函数值增长
最快的方向。这里所说的是移动方向，而未提到移动量的大小。该量值称为步长，记做a用向
量来表示的话，梯度算法的迭代公式如下：
训练算法：使用梯度上升找到最佳参数
from numpy import *
def loadDataSet():
dataMat = []; labelMat = []
fr = open('testSet.txt')
for line in fr.readlines():
lineArr = line.strip().split()
dataMat.append([1.0, float(lineArr[0]), float(lineArr[1])])
labelMat.append(int(lineArr[2]))
return dataMat,labelMat
def sigmoid(inX):
return 1.0/(1+exp(-inX))
def gradAscent(dataMatIn, classLabels):
dataMatrix = mat(dataMatIn) #convert to NumPy matrix
labelMat = mat(classLabels).transpose() #convert to NumPy matrix
m,n = shape(dataMatrix)
alpha = 0.001
maxCycles = 500
weights = ones((n,1))
for k in range(maxCycles): #heavy on matrix operations
h = sigmoid(dataMatrix*weights) #matrix mult
error = (labelMat - h) #vector subtraction
weights = weights + alpha * dataMatrix.transpose()* error #matrix mult
return weights
dataMat,labelMat = loadDataSet() weights = gradAscent(dataMat,labelMat) print(weights)
分析数据：画出决策边界
上面已经解出了一组回归系数,它确定了不同类别数据之间的分隔线。
import matplotlib
import matplotlib.pyplot as plt
from numpy import *
from matplotlib.patches import Rectangle
def loadDataSet():
dataMat = []
labelMat = []
fr = open('F:\\machinelearninginaction\\Ch05\\testSet.txt')
for line in fr.readlines():
lineArr = line.strip().split()
dataMat.append([1.0, float(lineArr[0]), float(lineArr[1])])
labelMat.append(int(lineArr[2]))
return dataMat,labelMat
def sigmoid(inX):
return 1.0/(1+exp(-inX))
def stocGradAscent0(dataMatrix, classLabels):
m,n = shape(dataMatrix)
alpha = 0.01
weights = ones(n) #initialize to all ones
for i in range(m):
h = sigmoid(sum(dataMatrix[i]*weights))
error = classLabels[i] - h
weights = weights + alpha * error * dataMatrix[i]
return weights
def gradAscent(dataMatIn, classLabels):
dataMatrix = mat(dataMatIn) #convert to NumPy matrix
labelMat = mat(classLabels).transpose() #convert to NumPy matrix
m,n = shape(dataMatrix)
alpha = 0.001
maxCycles = 500
weights = ones((n,1))
for k in range(maxCycles): #heavy on matrix operations
h = sigmoid(dataMatrix*weights) #matrix mult
error = (labelMat - h) #vector subtraction
weights = weights + alpha * dataMatrix.transpose()* error #matrix mult
return weights
dataMat,labelMat=loadDataSet()
dataArr = array(dataMat)
weights = gradAscent(dataArr,labelMat)
n = shape(dataArr)[0] #number of points to create
xcord1 = []
ycord1 = []
xcord2 = []
ycord2 = []
markers =[]
colors =[]
for i in range(n):
if int(labelMat[i])== 1:
xcord1.append(dataArr[i,1]); ycord1.append(dataArr[i,2])
else:
xcord2.append(dataArr[i,1]); ycord2.append(dataArr[i,2])
fig = plt.figure()
ax = fig.add_subplot(111)
#ax.scatter(xcord,ycord, c=colors, s=markers)
type1 = ax.scatter(xcord1, ycord1, s=30, c='red', marker='s')
type2 = ax.scatter(xcord2, ycord2, s=30, c='green')
x = arange(-3.0, 3.0, 0.1)
#weights = [-2.9, 0.72, 1.29]
#weights = [-5, 1.09, 1.42]
weights = [13.03822793, 1.32877317, -1.96702074]
weights = [4.12, 0.48, -0.6168]
y = (-weights[0]-weights[1]*x)/weights[2]
type3 = ax.plot(x, y)
#ax.legend([type1, type2, type3], ["Did Not Like", "Liked in Small Doses", "Liked in Large Doses"], loc=2)
#ax.axis([-5000,100000,-2,25])
plt.xlabel('X1')
plt.ylabel('X2')
plt.show()
训练算法：随机梯度上升
def stocGradAscent0(dataMatrix, classLabels):
m,n = shape(dataMatrix)
alpha = 0.01
weights = ones(n) #initialize to all ones
for i in range(m):
h = sigmoid(sum(dataMatrix[i]*weights))
error = classLabels[i] - h
weights = weights + alpha * error * dataMatrix[i]
return weights
import matplotlib
import matplotlib.pyplot as plt
from numpy import *
from matplotlib.patches import Rectangle
def loadDataSet():
dataMat = []
labelMat = []
fr = open('F:\\machinelearninginaction\\Ch05\\testSet.txt')
for line in fr.readlines():
lineArr = line.strip().split()
dataMat.append([1.0, float(lineArr[0]), float(lineArr[1])])
labelMat.append(int(lineArr[2]))
return dataMat,labelMat
def sigmoid(inX):
return 1.0/(1+exp(-inX))
def stocGradAscent0(dataMatrix, classLabels):
m,n = shape(dataMatrix)
alpha = 0.01
weights = ones(n) #initialize to all ones
for i in range(m):
h = sigmoid(sum(dataMatrix[i]*weights))
error = classLabels[i] - h
weights = weights + alpha * error * dataMatrix[i]
return weights
def gradAscent(dataMatIn, classLabels):
dataMatrix = mat(dataMatIn) #convert to NumPy matrix
labelMat = mat(classLabels).transpose() #convert to NumPy matrix
m,n = shape(dataMatrix)
alpha = 0.001
maxCycles = 500
weights = ones((n,1))
for k in range(maxCycles): #heavy on matrix operations
h = sigmoid(dataMatrix*weights) #matrix mult
error = (labelMat - h) #vector subtraction
weights = weights + alpha * dataMatrix.transpose()* error #matrix mult
return weights
dataMat,labelMat=loadDataSet()
dataArr = array(dataMat)
weights = stocGradAscent0(dataArr,labelMat)
n = shape(dataArr)[0] #number of points to create
xcord1 = []
ycord1 = []
xcord2 = []
ycord2 = []
markers =[]
colors =[]
for i in range(n):
if int(labelMat[i])== 1:
xcord1.append(dataArr[i,1]); ycord1.append(dataArr[i,2])
else:
xcord2.append(dataArr[i,1]); ycord2.append(dataArr[i,2])
fig = plt.figure()
ax = fig.add_subplot(111)
#ax.scatter(xcord,ycord, c=colors, s=markers)
type1 = ax.scatter(xcord1, ycord1, s=30, c='red', marker='s')
type2 = ax.scatter(xcord2, ycord2, s=30, c='green')
x = arange(-3.0, 3.0, 0.1)
#weights = [-2.9, 0.72, 1.29]
#weights = [-5, 1.09, 1.42]
weights = [13.03822793, 1.32877317, -1.96702074]
weights = [4.12, 0.48, -0.6168]
y = (-weights[0]-weights[1]*x)/weights[2]
type3 = ax.plot(x, y)
#ax.legend([type1, type2, type3], ["Did Not Like", "Liked in Small Doses", "Liked in Large Doses"], loc=2)
#ax.axis([-5000,100000,-2,25])
plt.xlabel('X1')
plt.ylabel('X2')
plt.show()
import matplotlib
import matplotlib.pyplot as plt
from numpy import *
from matplotlib.patches import Rectangle
def loadDataSet():
dataMat = []
labelMat = []
fr = open('F:\\machinelearninginaction\\Ch05\\testSet.txt')
for line in fr.readlines():
lineArr = line.strip().split()
dataMat.append([1.0, float(lineArr[0]), float(lineArr[1])])
labelMat.append(int(lineArr[2]))
return dataMat,labelMat
def sigmoid(inX):
return 1.0/(1+exp(-inX))
def stocGradAscent0(dataMatrix, classLabels):
m,n = shape(dataMatrix)
alpha = 0.5
weights = ones(n) #initialize to all ones
weightsHistory=zeros((500*m,n))
for j in range(500):
for i in range(m):
h = sigmoid(sum(dataMatrix[i]*weights))
error = classLabels[i] - h
weights = weights + alpha * error * dataMatrix[i]
weightsHistory[j*m + i,:] = weights
return weightsHistory
def stocGradAscent1(dataMatrix, classLabels):
m,n = shape(dataMatrix)
alpha = 0.4
weights = ones(n) #initialize to all ones
weightsHistory=zeros((40*m,n))
for j in range(40):
dataIndex = range(m)
for i in range(m):
alpha = 4/(1.0+j+i)+0.01
randIndex = int(random.uniform(0,len(dataIndex)))
h = sigmoid(sum(dataMatrix[randIndex]*weights))
error = classLabels[randIndex] - h
#print error
weights = weights + alpha * error * dataMatrix[randIndex]
weightsHistory[j*m + i,:] = weights
# del(dataIndex[randIndex])
print(weights)
return weightsHistory
dataMat,labelMat=loadDataSet()
dataArr = array(dataMat)
myHist = stocGradAscent1(dataArr,labelMat)
n = shape(dataArr)[0] #number of points to create
xcord1 = []
ycord1 = []
xcord2 = []
ycord2 = []
markers =[]
colors =[]
fig = plt.figure()
ax = fig.add_subplot(311)
type1 = ax.plot(myHist[:,0])
plt.ylabel('X0')
ax = fig.add_subplot(312)
type1 = ax.plot(myHist[:,1])
plt.ylabel('X1')
ax = fig.add_subplot(313)
type1 = ax.plot(myHist[:,2])
plt.xlabel('iteration')
plt.ylabel('X2')
plt.show()
值 得注意的是，在大的波动停止后，还有一些小的周期性波动。不难理解，产生这种现象的原因是
存在一些不能正确分类的样本点（数据集并非线性可分)，在每次迭代时会引发系数的剧烈改变。
我们期望算法能避免来回波动，从而收敛到某个值。另外，收敛速度也需要加快。
改进的随机梯度上升算法
def stocGradAscent1(dataMatrix, classLabels, numIter=150):
m,n = shape(dataMatrix)
weights = ones(n) #initialize to all ones
for j in range(numIter):
dataIndex = range(m)
for i in range(m):
alpha = 4/(1.0+j+i)+0.0001 #apha decreases with iteration, does not
randIndex = int(random.uniform(0,len(dataIndex)))#go to 0 because of the constant
h = sigmoid(sum(dataMatrix[randIndex]*weights))
error = classLabels[randIndex] - h
weights = weights + alpha * error * dataMatrix[randIndex]
del(dataIndex[randIndex])
return weights
import matplotlib
import matplotlib.pyplot as plt
from numpy import *
from matplotlib.patches import Rectangle
def loadDataSet():
dataMat = []
labelMat = []
fr = open('F:\\machinelearninginaction\\Ch05\\testSet.txt')
for line in fr.readlines():
lineArr = line.strip().split()
dataMat.append([1.0, float(lineArr[0]), float(lineArr[1])])
labelMat.append(int(lineArr[2]))
return dataMat,labelMat
def sigmoid(inX):
return 1.0/(1+exp(-inX))
def stocGradAscent0(dataMatrix, classLabels):
m,n = shape(dataMatrix)
alpha = 0.5
weights = ones(n) #initialize to all ones
weightsHistory=zeros((500*m,n))
for j in range(500):
for i in range(m):
h = sigmoid(sum(dataMatrix[i]*weights))
error = classLabels[i] - h
weights = weights + alpha * error * dataMatrix[i]
weightsHistory[j*m + i,:] = weights
return weightsHistory
def stocGradAscent1(dataMatrix, classLabels):
m,n = shape(dataMatrix)
alpha = 0.4
weights = ones(n) #initialize to all ones
weightsHistory=zeros((40*m,n))
for j in range(40):
dataIndex = range(m)
for i in range(m):
alpha = 4/(1.0+j+i)+0.01
randIndex = int(random.uniform(0,len(dataIndex)))
h = sigmoid(sum(dataMatrix[randIndex]*weights))
error = classLabels[randIndex] - h
#print error
weights = weights + alpha * error * dataMatrix[randIndex]
weightsHistory[j*m + i,:] = weights
# del(dataIndex[randIndex])
print(weights)
return weightsHistory
dataMat,labelMat=loadDataSet()
dataArr = array(dataMat)
myHist = stocGradAscent1(dataArr,labelMat)
n = shape(dataArr)[0] #number of points to create
xcord1 = []
ycord1 = []
xcord2 = []
ycord2 = []
markers =[]
colors =[]
fig = plt.figure()
ax = fig.add_subplot(311)
type1 = ax.plot(myHist[:,0])
plt.ylabel('X0')
ax = fig.add_subplot(312)
type1 = ax.plot(myHist[:,1])
plt.ylabel('X1')
ax = fig.add_subplot(313)
type1 = ax.plot(myHist[:,2])
plt.xlabel('iteration')
plt.ylabel('X2')
plt.show()
import matplotlib
import matplotlib.pyplot as plt
from numpy import *
from matplotlib.patches import Rectangle
def loadDataSet():
dataMat = []
labelMat = []
fr = open('F:\\machinelearninginaction\\Ch05\\testSet.txt')
for line in fr.readlines():
lineArr = line.strip().split()
dataMat.append([1.0, float(lineArr[0]), float(lineArr[1])])
labelMat.append(int(lineArr[2]))
return dataMat,labelMat
def sigmoid(inX):
return 1.0/(1+exp(-inX))
def stocGradAscent0(dataMatrix, classLabels):
m,n = shape(dataMatrix)
alpha = 0.01
weights = ones(n) #initialize to all ones
for i in range(m):
h = sigmoid(sum(dataMatrix[i]*weights))
error = classLabels[i] - h
weights = weights + alpha * error * dataMatrix[i]
return weights
def gradAscent(dataMatIn, classLabels):
dataMatrix = mat(dataMatIn) #convert to NumPy matrix
labelMat = mat(classLabels).transpose() #convert to NumPy matrix
m,n = shape(dataMatrix)
alpha = 0.001
maxCycles = 500
weights = ones((n,1))
for k in range(maxCycles): #heavy on matrix operations
h = sigmoid(dataMatrix*weights) #matrix mult
error = (labelMat - h) #vector subtraction
weights = weights + alpha * dataMatrix.transpose()* error #matrix mult
return weights
def stocGradAscent1(dataMatrix, classLabels, numIter=150):
m,n = shape(dataMatrix)
weights = ones(n) #initialize to all ones
for j in range(numIter):
dataIndex = range(m)
for i in range(m):
alpha = 4/(1.0+j+i)+0.0001 #apha decreases with iteration, does not
randIndex = int(random.uniform(0,len(dataIndex)))#go to 0 because of the constant
h = sigmoid(sum(dataMatrix[randIndex]*weights))
error = classLabels[randIndex] - h
weights = weights + alpha * error * dataMatrix[randIndex]
# del(dataIndex[randIndex])
return weights
dataMat,labelMat=loadDataSet()
dataArr = array(dataMat)
weights = stocGradAscent1(dataArr,labelMat)
n = shape(dataArr)[0] #number of points to create
xcord1 = []
ycord1 = []
xcord2 = []
ycord2 = []
markers =[]
colors =[]
for i in range(n):
if int(labelMat[i])== 1:
xcord1.append(dataArr[i,1]); ycord1.append(dataArr[i,2])
else:
xcord2.append(dataArr[i,1]); ycord2.append(dataArr[i,2])
fig = plt.figure()
ax = fig.add_subplot(111)
#ax.scatter(xcord,ycord, c=colors, s=markers)
type1 = ax.scatter(xcord1, ycord1, s=30, c='red', marker='s')
type2 = ax.scatter(xcord2, ycord2, s=30, c='green')
x = arange(-3.0, 3.0, 0.1)
#weights = [-2.9, 0.72, 1.29]
#weights = [-5, 1.09, 1.42]
weights = [13.03822793, 1.32877317, -1.96702074]
weights = [4.12, 0.48, -0.6168]
y = (-weights[0]-weights[1]*x)/weights[2]
type3 = ax.plot(x, y)
#ax.legend([type1, type2, type3], ["Did Not Like", "Liked in Small Doses", "Liked in Large Doses"], loc=2)
#ax.axis([-5000,100000,-2,25])
plt.xlabel('X1')
plt.ylabel('X2')
plt.show()
内容总结
以上是互联网集市为您收集整理的吴裕雄--天生自然python机器学习：Logistic回归全部内容，希望文章能够帮你解决吴裕雄--天生自然python机器学习：Logistic回归所遇到的程序开发问题。 如果觉得互联网集市技术教程内容还不错，欢迎将互联网集市网站推荐给程序员好友。
内容备注
版权声明：本文内容由互联网用户自发贡献，该文观点与技术仅代表作者本人。本站仅提供信息存储空间服务，不拥有所有权，不承担相关法律责任。如发现本站有涉嫌侵权/违法违规的内容， 请发送邮件至 gblab@vip.qq.com 举报，一经查实，本站将立刻删除。
内容手机端
扫描二维码推送至手机访问。
来源：【匿名】