DANKE TRUMP

def mrv(csp):
    counter = 0
    refvariable = csp.variables[0], 0
    for var in csp.variables:
        if var.get_value() is None:
            for peer in var.peers:
                if peer.get_value() is not None:
                    counter += 1
                    print peer
            variable = var, counter
            counter = 0
            if variable[1] > refvariable[1]:
                refvariable = variable

    print refvariable
    return refvariable

def backtracking(csp, ac_3=False):
    """
    Implement the basic backtracking algorithm to solve a CSP.

    Optional: if ac_3 == True use the AC-3 algorithm for constraint
              propagation. Important: if ac_3 == false don't use
              AC-3!
    :param csp: A csp.ConstrainedSatisfactionProblem object
                representing the CSP to solve
    :return: A csp.ConstrainedSatisfactionProblem, where all Variables
             are set and csp.complete() returns True. (I.e. the solved
             CSP)
    """
    variable = csp.variables[0]
    if csp.complete():
        return csp
    for var in csp.variables:
        if var.get_value() is None:
            variable = var
    for value in variable.domain:
        variable.set_value(value)
        if csp.consistent():
            result = backtracking(csp)
            if csp.consistent():
                return result
            variable.set_value(None)


def minimum_remaining_values(csp, ac_3=False):
    """
    Implement the basic backtracking algorithm to solve a CSP with
    minimum remaining values heuristic and no tie-breaker. Thus the
    first of all best solution is taken.

    Optional: if ac_3 == True use the AC-3 algorithm for constraint
              propagation. Important: if ac_3 == false don't use
              AC-3!
    :param csp: A csp.ConstrainedSatisfactionProblem object
                representing the CSP to solve
    :return: A tuple of 1) a csp.ConstrainedSatisfactionProblem, where
             all Variables are set and csp.complete() returns True. (I.e.
             the solved CSP) and 2) a list of all variables in the order
             they have been assigned.
    """
    return min_rem_rec(csp, [])


def min_rem_rec(csp, varlist):
    if csp.complete():
        return csp, varlist
    variable,_ = mrv(csp)
    varlist.append(variable)
    print varlist
    for value in variable.domain:
        variable.set_value(value)
        if csp.consistent():
            result, varlist = min_rem_rec(csp, varlist)
            if csp.consistent():
                return result, varlist
            variable.set_value(None)