num_svms = 6
width = 0.5
svmList = [None] * num_svms
trainfeatList = [None] * num_svms
traindatList = [None] * num_svms
trainlabList = [None] * num_svms
trainlabsList = [None] * num_svms
kernelList = [None] * num_svms
for i in range(num_svms):
pos = util.get_realdata(True)
neg = util.get_realdata(False)
traindatList[i] = concatenate((pos, neg), axis=1)
trainfeatList[i] = util.get_realfeatures(pos, neg)
trainlabsList[i] = util.get_labels(True)
trainlabList[i] = util.get_labels()
kernelList[i] = GaussianKernel(trainfeatList[i], trainfeatList[i], width)
svmList[i] = LibSVM(10, kernelList[i], trainlabList[i])
for i in range(num_svms):
print "Training svm nr. %d" % (i)
currentSVM = svmList[i]
currentSVM.train()
print currentSVM.get_num_support_vectors()
print "Done."
x, y, z = util.compute_output_plot_isolines(currentSVM, kernelList[i],
trainfeatList[i])
subplot(num_svms / 2, 2, i + 1)
pcolor(x, y, z)
from pylab import plot,grid,title,subplot,xlabel,ylabel,text,subplots_adjust,fill_between,mean,connect,show
from shogun.Kernel import GaussianKernel
from shogun.Classifier import LibSVM, LDA
from shogun.Evaluation import PRCEvaluation
import util
util.set_title('PRC example')
util.DISTANCE=0.5
subplots_adjust(hspace=0.3)
pos=util.get_realdata(True)
neg=util.get_realdata(False)
features=util.get_realfeatures(pos, neg)
labels=util.get_labels()
# classifiers
gk=GaussianKernel(features, features, 1.0)
svm = LibSVM(1000.0, gk, labels)
svm.train()
lda=LDA(1,features,labels)
lda.train()
## plot points
subplot(211)
plot(pos[0,:], pos[1,:], "r.")
plot(neg[0,:], neg[1,:], "b.")
grid(True)
title('Data',size=10)
# plot PRC for SVM
subplot(223)
num_svms=6 width=0.5 svmList = [None]*num_svms trainfeatList = [None]*num_svms traindatList = [None]*num_svms trainlabList = [None]*num_svms trainlabsList = [None]*num_svms kernelList = [None]*num_svms for i in range(num_svms): pos=util.get_realdata(True) neg=util.get_realdata(False) traindatList[i] = concatenate((pos, neg), axis=1) trainfeatList[i] = util.get_realfeatures(pos, neg) trainlabsList[i] = util.get_labels(True) trainlabList[i] = util.get_labels() kernelList[i] = GaussianKernel(trainfeatList[i], trainfeatList[i], width) svmList[i] = LibSVM(10, kernelList[i], trainlabList[i]) for i in range(num_svms): print "Training svm nr. %d" % (i) currentSVM = svmList[i] currentSVM.train() print currentSVM.get_num_support_vectors() print "Done." x, y, z=util.compute_output_plot_isolines( currentSVM, kernelList[i], trainfeatList[i]) subplot(num_svms/2, 2, i+1) pcolor(x, y, z, shading='interp')
