Created
November 27, 2014 04:17
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def eval_constraints(sq, n): | |
fitness = 0 | |
totalRowDiff = 0 | |
totalColDiff = 0 | |
rightDiagonal = 0 | |
leftDiagonal = 0 | |
for row in range(0, len(sq)): | |
sum_row = 0 | |
sum_col = 0 | |
for col in range(0, len(sq)): | |
sum_col += sq[col][row] | |
sum_row += sq[row][col] | |
if row == col: | |
leftDiagonal += sq[row][col] | |
if row+col == len(sq)-1: | |
rightDiagonal += sq[row][col] | |
totalRowDiff += abs(sum_row-n) | |
totalColDiff += abs(sum_col-n) | |
return abs(rightDiagonal-n)+abs(leftDiagonal-n)+totalColDiff+totalRowDiff | |
def swap_values(sq, times): | |
temp = list(sq) | |
for i in range(0, times): | |
r1 = random.randint(0,len(sq)-1) | |
c1 = random.randint(0,len(sq)-1) | |
r2 = random.randint(0,len(sq)-1) | |
c2 = random.randint(0,len(sq)-1) | |
temp[r1][c1], temp[r2][c2] = temp[r2][c2],temp[r1][c1] | |
return temp | |
def initial_square(l): | |
return [[l[j] for j in range(i*int(np.sqrt(len(l))), (i+1)*int(np.sqrt(len(l))))] for i in range(0, int(np.sqrt(len(l))))] | |
def simulated_annealing(l, t): | |
iter = 0 | |
initial_temp = 1.0 | |
steps = 500 | |
cooling = 0.97 | |
constant = 0.01 | |
sq = initial_square(l) | |
n = sum(l)/int(np.sqrt(len(l))) | |
cost = eval_constraints(sq, n) | |
temp = initial_temp | |
valid = False | |
c_time = time.clock() | |
while not valid: | |
if cost is 0: | |
valid = True | |
break | |
temp *= cooling | |
ref_cost = cost | |
for i in range(0, steps): | |
temp_sq = swap_values(sq, 1) | |
print temp_sq | |
n_cost = eval_constraints(temp_sq, n) | |
if n_cost < cost or np.exp(-(n_cost-cost)/(temp*constant)) > np.random.ranf(): | |
cost = n_cost | |
sq = temp_sq | |
if cost is 0: | |
valid = True | |
break | |
if ref_cost != cost: | |
temp /= cooling | |
print 'Iteration: %d | Cost: %d' % (iter, cost) | |
iter += 1 | |
if time.clock() - c_time > t: | |
break | |
return [sq, valid] |
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