python3 集体智慧编程第九章advancedclassify.py代码
对于python2版本的集体智慧编程第九章SVM进行了修改，该代码适用于python3版本。
以下是advancedclassify.py
import matplotlib.pyplot as plt
import numpy as np
class matchrow:
def __init__(self,row,allnum=False):
if allnum:
self.data=[float(row[i]) for i in range(len(row)-1)]
else:
self.data=row[0:len(row)-1]
self.match=int(row[len(row)-1])
def loadmatch(f,allnum=False):
rows=[]
for line in open(f):
rows.append(matchrow(line.split(','),allnum))
return rows
from pylab import *
def plotagematches(rows):
xdm,ydm=[r.data[0] for r in rows if r.match==1],\
[r.data[1] for r in rows if r.match==1]
xdn,ydn=[r.data[0] for r in rows if r.match==0],\
[r.data[1] for r in rows if r.match==0]
plt.plot(xdm,ydm,'bo')
plt.plot(xdn,ydn,'b+')
plt.show()
def lineartrain(rows):
averages={}
counts={}
for row in rows:
# Get the class of this point
cl=row.match
averages.setdefault(cl,[0.0]*(len(row.data)))
counts.setdefault(cl,0)
# Add this point to the averages
for i in range(len(row.data)):
averages[cl][i]+=float(row.data[i])
# Keep track of how many points in each class
counts[cl]+=1
# Divide sums by counts to get the averages
for cl,avg in averages.items():
for i in range(len(avg)):
avg[i]/=counts[cl]
return averages
def dotproduct(v1,v2):
return sum([v1[i]*v2[i] for i in range(len(v1))])
def veclength(v):
return sum([p**2 for p in v])
def dpclassify(point,avgs):
b=(dotproduct(avgs[1],avgs[1])-dotproduct(avgs[0],avgs[0]))/2
y=dotproduct(point,avgs[0])-dotproduct(point,avgs[1])+b
if y>0: return 0
else: return 1
def yesno(v):
if v=='yes': return 1
elif v=='no': return -1
else: return 0
def matchcount(interest1,interest2):
l1=interest1.split(':')
l2=interest2.split(':')
x=0
for v in l1:
if v in l2: x+=1
return x
yahookey="YOUR API KEY"
from xml.dom.minidom import parseString
from urllib.request import urlopen
from urllib.parse import quote_plus
loc_cache={}
def getlocation(address):
if address in loc_cache: return loc_cache[address]
data=urlopen('http://api.local.yahoo.com/MapsService/V1/'+\
'geocode?appid=%s&location=%s' %
(yahookey,quote_plus('1 alewife center'))).read()
doc=parseString(data)
lat=doc.getElementsByTagName('Latitude')[0].firstChild.nodeValue
long=doc.getElementsByTagName('Longitude')[0].firstChild.nodeValue
loc_cache[address]=(float(lat),float(long))
return loc_cache[address]
def milesdistance(a1,a2):
#lat1,long1=getlocation(a1)
#lat2,long2=getlocation(a2)
#latdif=69.1*(lat2-lat1)
#longdif=53.0*(long2-long1)
#return (latdif**2+longdif**2)**.5
return 0
def loadnumerical():
oldrows=loadmatch('matchmaker.csv')
newrows=[]
for row in oldrows:
d=row.data
data=[float(d[0]),yesno(d[1]),yesno(d[2]),
float(d[5]),yesno(d[6]),yesno(d[7]),
matchcount(d[3],d[8]),
milesdistance(d[4],d[9]),
row.match]
newrows.append(matchrow(data))
return newrows
def scaledata(rows):
low=[999999999.0]*len(rows[0].data)
high=[-999999999.0]*len(rows[0].data)
# 寻找最大值和最小值
for row in rows:
d=row.data
for i in range(len(d)):
if d[i]high[i]: high[i]=d[i]
# 对数据进行缩放处理函数
def scaleinput(d):
return [(d[i]-low[i])/(high[i]-low[i])
for i in range(len(low)-1)]
# 对所有数据进行缩放处理
newrows=[matchrow(scaleinput(row.data)+[row.match])
for row in rows]
# 返回新数据和缩放处理函数
return newrows,scaleinput
def rbf(v1,v2,gamma=10):
dv=[v1[i]-v2[i] for i in range(len(v1))]
l=veclength(dv)
return math.e**(-gamma*l)
def nlclassify(point,rows,offset,gamma=10):
sum0=0.0
sum1=0.0
count0=0
count1=0
for row in rows:
if row.match==0:
sum0+=rbf(point,row.data,gamma)
count0+=1
else:
sum1+=rbf(point,row.data,gamma)
count1+=1
y=(1.0/count0)*sum0-(1.0/count1)*sum1+offset
if y>0: return 0
else: return 1
def getoffset(rows,gamma=10):
l0=[]
l1=[]
for row in rows:
if row.match==0: l0.append(row.data)
else: l1.append(row.data)
sum0=sum(sum([rbf(v1,v2,gamma) for v1 in l0]) for v2 in l0)
sum1=sum(sum([rbf(v1,v2,gamma) for v1 in l1]) for v2 in l1)
return (1.0/(len(l1)**2))*sum1-(1.0/(len(l0)**2))*sum0
篇幅有限，具体练习见另一篇。