ytest_BCDEFGHI = txmat('ytest_BCDEFGHI_pow.mat','ytest')
xtltest_BCDEFGHI = txmat('xtltest_BCDEFGHI_pow.mat','xtltest')
xtrain_A = txmat('xtrain_A_pow.mat','xtrain') # we're gonna use this for testing'
ytrain_A = txmat('ytrain_A_pow.mat','ytrain')
xtltrain_A = txmat('xtltrain_A_pow.mat','xtltrain')
=======
xtrain_pow = sio.loadmat('xtrain_all_pow.mat')
xtrain_pow = xtrain_pow['xtrain']
>>>>>>> bd36724a187f99ba8e1e28e7167a1c6f578d48e7
print 'NA classifier pow training BCDEFGHI and A'
nu = [0.05, 0.1,.2,.3,.4, 0.5, 0.8]
for param in nu:
ystring,ystring1 = NA_Classifier.myclassify_NA(2, xtrain_BCDEFGHI, xtest_BCDEFGHI, xtltest_BCDEFGHI, xtrain_A, xtltrain_A, nuparam=param)
print 'for nu =' + str(param)
print 'results on BCDEFGHI testing set'
print ystring
print 'results on grid A data set'
print ystring1
print '/n /n'
xtrain_ABCDEFGH = txmat('xtrain_ABCDEFGH_pow.mat','xtrain')
ytrain_ABCDEFGH = txmat('ytrain_ABCDEFGH_pow.mat','ytrain')
xtltrain_ABCDEFGH = txmat('xtltrain_ABCDEFGH_pow.mat','xtltrain')
xtest_ABCDEFGH = txmat('xtest_ABCDEFGH_pow.mat','xtest')
xtest_ABCDEFGH = txmat('xtest_ABCDEFGH_pow.mat','xtest')
ytest_ABCDEFGH= txmat('ytest_ABCDEFGH_pow.mat','ytest')
xtltest_ABCDEFGH = txmat('xtltest_ABCDEFGH_pow.mat','xtltest')
xtrain_I = txmat('xtrain_I_pow.mat','xtrain') # we're gonna use this for testing'
ytrain_I = txmat('ytrain_I_pow.mat','ytrain')
xtltrain_I = txmat('xtltrain_I_pow.mat','xtltrain')
print 'NA classifier pow training BCDEFGHI and A'
nu = [0.05, 0.1,.2,.3,.4, 0.5, 0.8]
for param in nu:
ystring,ystring1 = NA_Classifier.myclassify_NA(2, xtrain_BCDEFGHI, xtest_BCDEFGHI, xtltest_BCDEFGHI, xtrain_A, xtltrain_A, nuparam=param)
ystring2,ystring3 = NA_Classifier.myclassify_NA(2, xtrain_ABCDEFGH, xtest_ABCDEFGH, xtltest_ABCDEFGH, xtrain_I, xtltrain_I, nuparam=param)
print 'for nu =' + str(param)
print 'results on BCDEFGHI testing set and A data set'
print ystring
print ystring1
print '\n'
print 'results on ABCDEFGH testing set and I data set'
print ystring2
print ystring3
print '\n'
# ystring0 = NA_Classifier.myclassify_NA(1,xtrain_pow,xtesting)