#lib="linearblock"
#pc=0.008 #single resolution
pc = cfg.svmc #high res
#util.trainSvmRaw(ftrpos,ftrneg,svmname,dir="",pc=pc,lib=lib)
#util.trainSvmRaw(ftrneg,ftrpos,svmname,dir="",pc=pc,lib=lib)
#w,r=util.loadSvm(svmname,dir="",lib=lib)
#w,r=util.trainSvmRawPeg(ftrpos,ftrneg,testname+".rpt.txt",dir="",pc=pc)
import pegasos
if w == None:
w = numpy.zeros(cumsize[-1])
w, r, prloss = pegasos.trainComp(
trpos,
trneg,
testname + "loss.rpt.txt",
trposcl,
trnegcl,
oldw=w,
dir="",
pc=pc,
k=10,
numthr=numcore)
#pylab.figure(300)
#pylab.clf()
#pylab.plot(w)
pylab.figure(500)
pylab.clf()
oldprloss = numpy.concatenate((oldprloss, prloss), 0)
pylab.plot(oldprloss)
pylab.semilogy()
pylab.legend(["loss+", "loss-", "reg", "obj", "hard+", "hard-"],
loc='upper left')
print "SVM learning"
svmname="%s.svm"%cfg.testname
#lib="libsvm"
lib="linear"
#pc=0.008 #single resolution
pc=cfg.svmc #high res
#util.trainSvmRaw(trpos,trneg,svmname,dir="",pc=pc,lib=lib)
#w,r=util.loadSvm(svmname,dir="",lib=lib)
import time
tt1=time.time()
#w,r=pegasos.train(trpos,trneg,svmname,dir="",pc=pc)
if w==None:
w=numpy.zeros(len(trpos[0]),numpy.float32)
r=0
w,r,prloss=pegasos.trainComp(trpos,trneg,svmname,oldw=numpy.concatenate((w,[-r/100])),pc=pc,k=10,numthr=numcore)
#w,r,prloss=pegasos.trainComp(trpos,trneg,svmname,dir="",pc=pc)
r=-w[-1]*100;w=w[:-1]
#print "Results"
#raw_input()
#w,r=pegasos.trainkeep(trpos,trneg,svmname,dir="",pc=pc)
pylab.figure(500)
pylab.clf()
oldprloss=numpy.concatenate((oldprloss,prloss),0)
pylab.plot(oldprloss)
pylab.semilogy()
pylab.legend(["loss+","loss-","reg","obj","hard+","hard-"],loc='upper left')
pylab.savefig("%s_loss%d.pdf"%(cfg.testname,it))
pylab.draw()
pylab.show()
lib = "linear"
#lib="linearblock"
#pc=0.008 #single resolution
pc = cfg.svmc #high res
#util.trainSvmRaw(ftrpos,ftrneg,svmname,dir="",pc=pc,lib=lib)
#util.trainSvmRaw(ftrneg,ftrpos,svmname,dir="",pc=pc,lib=lib)
#w,r=util.loadSvm(svmname,dir="",lib=lib)
#w,r=util.trainSvmRawPeg(ftrpos,ftrneg,testname+".rpt.txt",dir="",pc=pc)
import pegasos
if w == None:
w = numpy.zeros(cumsize[-1])
w, r, prloss = pegasos.trainComp(trpos,
trneg,
testname + "loss.rpt.txt",
trposcl,
trnegcl,
oldw=w,
dir="",
pc=pc,
k=10,
numthr=numcore)
#pylab.figure(300)
#pylab.clf()
#pylab.plot(w)
pylab.figure(500)
pylab.clf()
oldprloss = numpy.concatenate((oldprloss, prloss), 0)
pylab.plot(oldprloss)
pylab.semilogy()
pylab.legend(["loss+", "loss-", "reg", "obj", "hard+", "hard-"],
loc='upper left')
pylab.savefig("%s_loss%d.pdf" % (cfg.testname, it))
#lib="libsvm"
lib = "linear"
#pc=0.008 #single resolution
pc = cfg.svmc #high res
#util.trainSvmRaw(trpos,trneg,svmname,dir="",pc=pc,lib=lib)
#w,r=util.loadSvm(svmname,dir="",lib=lib)
import time
tt1 = time.time()
#w,r=pegasos.train(trpos,trneg,svmname,dir="",pc=pc)
if w == None:
w = numpy.zeros(len(trpos[0]), numpy.float32)
r = 0
w, r, prloss = pegasos.trainComp(trpos,
trneg,
svmname,
oldw=numpy.concatenate(
(w, [-r / 100])),
pc=pc,
k=10,
numthr=numcore)
#w,r,prloss=pegasos.trainComp(trpos,trneg,svmname,dir="",pc=pc)
r = -w[-1] * 100
w = w[:-1]
#print "Results"
#raw_input()
#w,r=pegasos.trainkeep(trpos,trneg,svmname,dir="",pc=pc)
pylab.figure(500)
pylab.clf()
oldprloss = numpy.concatenate((oldprloss, prloss), 0)
pylab.plot(oldprloss)
pylab.semilogy()
pylab.legend(["loss+", "loss-", "reg", "obj", "hard+", "hard-"],
