img[:,:,1] = np.random.rand(*img.shape[:-1])
img[0,0] = 3
img[1,1] = 3
img[3:6,3:6] = 50
dot[4,4] = 1
dot[5,5] = 1
dot[0,0] = 2
dot[1,1] = 2
backup_image = np.copy(img)
Counter = SVR(pMult, lMult, DENSITYBOUND, kernel = "linear", optimization =
"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"
