A solution to N-Queens using the Min-Conflicts local search algorithm

nqueens.py

#!/usr/bin/python
# A solution to N-Queens using the Min-Conflicts local search algorithm
# Vedant Kumar <vminch@gmail.com>

import random

def nqueens(nr):
	show(min_conflicts(list(range(nr)), nr), nr)

def show(soln, nr):
	for i in range(nr):
		row = ['~'] * nr
		for col in range(nr):
			if soln[col] == nr - 1 - i:
				row[col] = 'Q'
		print(''.join(row))

def min_conflicts(soln, nr, iters=1000):
	def random_pos(li, filt):
		return random.choice([i for i in range(nr) if filt(li[i])])

	for k in range(iters):
		confs = find_conflicts(soln, nr)
		if sum(confs) == 0:
			return soln
		col = random_pos(confs, lambda elt: elt > 0)
		vconfs = [hits(soln, nr, col, row) for row in range(nr)]
		soln[col] = random_pos(vconfs, lambda elt: elt == min(vconfs))
	raise Exception("Incomplete solution: try more iterations.")

def find_conflicts(soln, nr):
	return [hits(soln, nr, col, soln[col]) for col in range(nr)]

def hits(soln, nr, col, row):
	total = 0
	for i in range(nr):
		if i == col:
			continue
		if soln[i] == row or abs(i - col) == abs(soln[i] - row):
			total += 1
	return total

nqueens(64)

The code is neat, I like that. I think you'll be interested in this: https://thewalnut.io/visualizer/visualize/3595/267/
It is a visualization of the N-Queens, solved using a the same algorithm, implemented in Javascript.
As the tool also supports Python, I think it would be interesting to port this implementation to come up with another cool visualization.