"svr")
sigma = [0]
testimg, testdot, testmapping, testtags = Counter.prepareData(img, dot,
sigma,
normalize =
False, smooth
= True
)
#print "blub", testimg.shape
#print testimg
#print testdot, np.sum(testdot)
boxConstraints = []
#boxConstraints = [(12, img[:,:,:])]
#boxConstraints = [(3, img[0:30,0:30,:])]
#boxConstraints.reshape((-1, boxConstraints.shape[-1]))
success = Counter.fitPrepared(testimg[testmapping,:], testdot[testmapping], testtags, epsilon = 0.000,
boxConstraints = boxConstraints)
#success = Counter.fitPrepared(testimg[indices,:], testdot[indices], testtags[:len(indices)], epsilon = 0.000)
#print Counter.w, Counter.
print "learning finished"
#conversion step
#Q = kernelize(B, method = "gaussian")
##Q = B * B.transpose()
#tags = np.zeros(numVariables,dtype=np.int8)
#tags[0:len(pindices)] = 1
#tags[len(pindices):] = -1
#c = dot[allIndices] * (-tags)+ epsilon
#upperBounds = [None, pMult, lMult]
#success,solution = optimize(tags,Q,c,upperBounds)
## Put model data into dense matrices
#print Counter.b, Counter.w
