def queens(n_queens=16):
fitness_fn = mlrose.Queens()
problem = mlrose.DiscreteOpt(length=n_queens,
maximize=False,
fitness_fn=fitness_fn,
max_val=n_queens)
return problem
start_fit_time = tt.time()
best_state, best_fitness,fitness_curve = mlrose.random_hill_climb(problem,max_attempts = 500, max_iters = 500,restarts=0,init_state = istate, random_state = 1,curve=True)
end_fit_time = tt.time()
it = len(fitness_curve)
time = end_fit_time - start_fit_time
tpi = time/it
print('Flip Flop')
print(f'sa, input size: {p},time={time}, iterations={it}, time per iteration={tpi}')
for p in ps:
fitness_1 = mlrose.Queens()
problem = mlrose.DiscreteOpt(length = p, fitness_fn = fitness_1,
maximize = False, max_val = p)
schedule = mlrose.ExpDecay()
istate = np.arange(0,p)
start_fit_time = tt.time()
best_state, best_fitness,fitness_curve = mlrose.random_hill_climb(problem,max_attempts = 500, max_iters = 500,restarts=0,init_state = istate, random_state = 1,curve=True)
end_fit_time = tt.time()
it = len(fitness_curve)
time = end_fit_time - start_fit_time
tpi = time/it
ips = it/time
print('Queens')
import mlrose
import numpy as np
'''Simulated Annealing'''
fitness = mlrose.Queens()
schedule = mlrose.ExpDecay()
problem = mlrose.DiscreteOpt(length=8,
fitness_fn=fitness,
maximize=False,
max_val=8)
# Define initial state
init_state = np.array([1, 1, 1, 1, 1, 1, 1, 5])
# Set random seed
np.random.seed(1)
# Solve problem using simulated annealing
opt_state, opt_fit = mlrose.simulated_annealing(problem,
schedule=schedule,
max_attempts=100,
max_iters=10000,
init_state=init_state)
print('The best state found is (SA): ', opt_state)
print('The fitness at the best state is (SA): ', opt_fit)
#RHC
print('Simulated Annealing: ')
print("Accuracy: ", aveAccuracyScoreSA)
print('Genetic Algorithm: ')
print("Accuracy: ", aveAccuracyScoreGA)
print(
'///////////////////////////////////////////////////////////////////////////////'
)
edges = [(0, 1), (1, 4), (1, 3), (2, 4), (3, 7), (4, 5), (4, 6), (5, 6),
(5, 7), (6, 7), (5, 3), (0, 3), (0, 2), (1, 7), (1, 6), (0, 4),
(1, 2), (3, 4), (8, 0), (8, 4), (8, 2), (8, 1)]
weights = [3, 4, 5, 7, 9, 6, 10, 11]
values = [1, 2, 3, 4, 5, 6, 7, 8]
maxWeightPct = 2
fitnessArray = [
mlrose.Queens(),
mlrose.MaxKColor(edges),
mlrose.Knapsack(weights, values, maxWeightPct)
]
titleArray = ['Queens', 'Max Color', 'Knapsack']
for x in range(len(fitnessArray)):
aveFitnessRHC = 0
aveFitnessSA = 0
aveFitnessGA = 0
aveFitnessM = 0
aveTimeRHC = 0
aveTimeSA = 0
aveTimeGA = 0
aveTimeM = 0
for y in range(0, 10):
print('Results for: ', titleArray[x])
import mlrose # a library of common optimization problems such as n-queens ans TSP
import numpy as np
'''
THE 8-QUEENS PROBLEM
Having an 8x8 chess board and 8 queen pieces, what is the optimal way to place each piece such
that no one piece can attack the other?
Pre-requisite- Knowledge of chess that the queen piece can attack in any direction
'''
#define our fitness function
fitness_func = mlrose.Queens()
# the n-queens is a discrete state optimization problem since the values are integers
# these integers represent the column position and their indices, the row position
optimization_problem = mlrose.DiscreteOpt(max_val=8,
maximize=False,
length=8,
fitness_fn=fitness_func)
# maximize=False since we are trying to reduce number of attacking pairs(queen pieces)
init_state = np.array([0, 1, 2, 3, 4, 5, 6, 7]) # Define initial state
def visualize_state(state):
vizualiation_board = []
for i in range(len(state)):
row = []
for j in range(len(state)):
if state[i] == j:
row.append(' Q ')
else:
row.append(' * ')
def KQueens(numQueens, sizeOrIterations):
fitness = mlrose.Queens()
problem = mlrose.DiscreteOpt(length = numQueens, fitness_fn = fitness, maximize = False, max_val = numQueens)
rhcfitnessMatrix = []
safitnessMatrix = []
genalgfitnessMatrix = []
mimicfitnessMatrix = []
numIterations = 10000
dataPoints = 100
#rhc
print("Begin RHC")
startingTime = time()
for i in range(numIterations):
if i % dataPoints == 0 and not sizeOrIterations or i == 1000 and sizeOrIterations:
print("RHC I: " + str(i))
t0 = time()
best_state, best_fitness = mlrose.random_hill_climb(problem, max_attempts=100, max_iters=i,
init_state=None)
finish = time() - t0
currentTime = time() - startingTime
print("CurrentTime: " + str(currentTime))
rhcfitnessMatrix.append((i, best_fitness, finish))
finishtime = time() - startingTime
print("Finish Time: " + str(finishtime))
#simulated annealing
schedule = mlrose.ExpDecay()
startingTime = time()
for i in range(numIterations):
if i % dataPoints == 0 and not sizeOrIterations or i == 1000 and sizeOrIterations:
print("SA I: " + str(i))
t0 = time()
best_state, best_fitness = mlrose.simulated_annealing(problem, schedule = schedule,
max_attempts = 100, max_iters = i,
init_state = None)
finish = time() - t0
currentTime = time() - startingTime
print("CurrentTime: " + str(currentTime))
safitnessMatrix.append((i, best_fitness, finish))
finishtime = time() - startingTime
print("Finish Time: " + str(finishtime))
#genetic alg
startingTime = time()
for i in range(numIterations):
if i % dataPoints == 0 and not sizeOrIterations or i == 1000 and sizeOrIterations:
print("GA I: " + str(i))
t0 = time()
best_state, best_fitness = mlrose.genetic_alg(problem, pop_size=200, mutation_prob=0.1, max_attempts=100,
max_iters=i)
finish = time() - t0
currentTime = time() - startingTime
print("CurrentTime: " + str(currentTime))
genalgfitnessMatrix.append((i, best_fitness, finish))
finishtime = time() - startingTime
print("Finish Time: " + str(finishtime))
#mimic
startingTime = time()
for i in range(numIterations):
if i % dataPoints == 0 and not sizeOrIterations or i == 1000 and sizeOrIterations:
print("Mimic I: " + str(i))
t0 = time()
best_state, best_fitness = mlrose.mimic(problem, pop_size=200, keep_pct=0.2, max_attempts=100, max_iters=i)
finish = time() - t0
currentTime = time() - startingTime
print("CurrentTime: " + str(currentTime))
mimicfitnessMatrix.append((i, best_fitness, finish))
finishtime = time() - startingTime
print("Finish Time: " + str(finishtime))
if not sizeOrIterations:
writeToExcel.writeOptimzationProblem(rhcfitnessMatrix, safitnessMatrix, genalgfitnessMatrix, mimicfitnessMatrix, "QueensIterations.xlsx")
return None
else:
return rhcfitnessMatrix, safitnessMatrix, genalgfitnessMatrix, mimicfitnessMatrix
