复旦大学人工智能N-Queens答案

Introduction
In this project, you are going to construct a CSP for N-Queens problem. You will be given several python files.
Files to Edit and Submit: You will fill in portions of submission.py during the assignment. You should submit this file with your code and comments. Please do NOT change th

import copy
from csp import CSP


def create_n_queens_csp（n=8）:
    “““Create an N-Queen problem on the board of size n * n.

    You should call csp.add_variable（） and csp.add_binary_factor（）.

    Args:
        n: int number of queens or the size of one dimension of the board.

    Returns
        csp: A CSP problem with correctly configured factor tables
        such that it can be solved by a weighted CSP solver
    “““
    csp = CSP（）
    # TODO: Problem b
    # TODO: BEGIN_YOUR_CODE
    variables = []
    for i in range（n）:
        # name variables from y1 to yn
        var = ‘y{}‘.format（i+1） 
        # assign values to variables
        csp.add_variable（varrange（1n+1））
        variables.append（var）
    for i in range（len（variables））:
        for j in range（len（variables））:
            if variables[i] != variables[j]:
                distance = abs（i-j）
                # Generate binary constraints between queens
                csp.add_binary_factor（variables[i] variables[j] lambda y1 y2: y1 != y2 and distance != abs（y1-y2））
    #raise NotImplementedError
    # TODO: END_YOUR_CODE
    return csp


class BacktrackingSearch:
    “““A backtracking algorithm that solves CSP.

    Attributes:
        num_assignments: keep track of the number of assignments
            （identical when the CSP is unweighted）
        num_operations: keep track of number of times backtrack（） gets called
        first_assignment_num_operations: keep track of number of operations to
            get to the very first successful assignment （maybe not optimal）
        all_assignments: list of all solutions found

        csp: a weighted CSP to be solved
        mcv: bool if True use Most Constrained Variable heuristics
        ac3: bool if True AC-3 will be used after each variable is made
        domains: dictionary of domains of every variable in the CSP

    Usage:
        search = BacktrackingSearch（）
        search.solve（csp）
    “““

    def __init__（self）:
        self.num_assignments = 0
        self.num_operations = 0
        self.first_assignment_num_operations = 0
        self.all_assignments = []

        self.csp = None
        self.mcv = False
        self.ac3 = False
        self.domains = {}

    def reset_results（self）:
        “““Resets the statistics of the different aspects of the CSP solver.“““
        self.num_assignments = 0
        self.num_operations = 0
        self.first_assignment_num_operations = 0
        self.all_assignments = []

    def check_factors（self assignment var val）:
        “““Check consistency between current assignment and a new variable.

        Given a CSP a partial assignment and a proposed new value for a
        variable return the change of weights after assigning the variable
        with the proposed value.

        Args:
            assignment: A dictionary of current assignment.
                Unassigned variables do not have entries while an assigned
                variable has the assigned va

 属性            大小     日期    时间   名称
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     文件       10420  2019-10-24 22:57  submission.py