def print_minim_info(self, x0, found_minimum):
		print "x0: "
		print x0
		print "cost x0: "
		print self.reg.cost_fun(x0)
		print "found_minimum: "
		print found_minimum
		print "cost found_minimum: "
		print KernelCorrelation.cost_fun(self.reg, found_minimum)
	def test_minimum_better_than_random(self):
		"""Test if for a random transform, the cost that is found after a few iterations is lower than the initial cost"""
		numpy.random.seed(0)
		scalefactor = 2
		model = self.normed_rand(self.num_vectors, self.mdim*scalefactor)
		scene = self.normed_rand(self.num_vectors, self.mdim*scalefactor)
		self.reg = RigidKernelCorrelation(model, scene)
		x0=numpy.random.rand(self.reg.transform_dof)
		found_minimum = self.reg.find_minimum(x0=x0)
		self.print_minim_info(x0, found_minimum)
		self.assertTrue(KernelCorrelation.cost_fun(self.reg, found_minimum) < self.reg.cost_fun(x0))