mc-dmsched.py

#!/usr/bin/python

def eval_solution(self, soln):
	self.apply_solution(soln)
	confs = self.find_conflicts(next_state)
	score = sum([weight_of(conflict) for conflict in confs])
	self.revert_solution(soln)
	return score	

def find_solutions(self, conflict):
	return conflict['issue'].fixer(conflict)

def find_conflicts(self, section):
	return filter(lambda conf: conf, [test(section) for test in self.conf_tests])

def apply_solution(self, soln):
	pass

def revert_solution(self, soln):
	pass

def min_conflicts(self):
	for k in xrange(1000):
		issues = []
		all_secs = Section.objects.all()
		for section in all_secs:
			conflicts = self.find_conflicts(section)
			if len(conflicts):
				issues.extend(conflicts)
		if not len(issues):
			return True
		conflict = random.choice(issues)
		solutions = self.find_solutions(conflict)
		scores = [self.eval_solution(soln) for soln in solutions]
		soln = solutions[scores.index(min(scores))]
		self.apply_solution(soln)
	return False

def no_planning_fixer(self, conflict):
	# return a [solution]
	pass

class Issue:
	def __init__(self, weight, fixer):
		self.weight = weight
		self.fixer = fixer

class ISched:
	__init__
		self.NoPlanningBlock = Issue(1.00, self.no_planning_fixer)

def some_test(self, section):
	return {
		'issue': self.NoPlanningBlock,
		'section': section,
		# other data
	}