def create_six_peeks_problem(length=40, t_pct=0.2):
problem = mlrose_hiive.fitness.six_peaks.SixPeaks(t_pct)
fitness = mlrose_hiive.SixPeaks(t_pct)
six_peaks = mlrose_hiive.opt_probs.discrete_opt.DiscreteOpt(length=length,
fitness_fn=fitness,
maximize=True)
return six_peaks
def get_six_peaks(size):
six_peaks = mlrose.SixPeaks(t_pct=0.1)
state = np.array([0, 1, 0, 1, 1, 1, 1])
six_peaks.evaluate(state)
problem = mlrose.DiscreteOpt(
length=size,
fitness_fn=six_peaks,
maximize=True,
max_val=2 # makes it bit string
)
return problem
def get_optimal_values(name, n):
if name == "queens":
return np.arange(n).sum()
elif name == "four_peaks":
fitness = mlrose.FourPeaks()
state = np.zeros(n, dtype=int)
t = int(np.ceil(fitness.t_pct * n))
state[0:t + 1] = 1
return fitness.evaluate(state)
elif name == "flip_flop":
return n - 1
elif name == "six_peaks":
fitness = mlrose.SixPeaks()
state = np.zeros(n, dtype=int)
t = int(np.ceil(fitness.t_pct * n))
state[0:t + 1] = 1
return fitness.evaluate(state)
else:
raise Exception("Invalid Problem Name!")
def runPart1(savePath):
fitness = mlrose.FourPeaks(t_pct=0.15)
init_state = None
fourPeaksProblem = mlrose.DiscreteOpt(length=12,
fitness_fn=fitness, maximize=True, max_val=2)
part1_1 = Part1(name='Four Peaks', fitness=fitness,
problem=fourPeaksProblem, init_state=init_state)
part1_1.runAll(savePath)
fitness = mlrose.Queens()
init_state = None
eightQueensProblem = mlrose.DiscreteOpt(length=8,
fitness_fn=fitness, maximize=False, max_val=8)
part1_2 = Part1(name='Eight Queens', fitness=fitness,
problem=eightQueensProblem, init_state=init_state)
part1_2.runAll(savePath)
fitness = mlrose.SixPeaks(t_pct=0.15)
init_state = None
sixPeaksProblem = mlrose.DiscreteOpt(length=11,
fitness_fn=fitness, maximize=True, max_val=2)
part1_4 = Part1(name='Six Peaks', fitness=fitness,
problem=sixPeaksProblem, init_state=init_state)
part1_4.runAll(savePath)
fitness = mlrose.FlipFlop()
init_state = None
flipFlopProblem = mlrose.DiscreteOpt(length=7,
fitness_fn=fitness, maximize=True, max_val=2)
part1_5 = Part1(name='Flip Flop - 7', fitness=fitness,
problem=flipFlopProblem, init_state=init_state)
part1_5.runAll(savePath)
fitness = mlrose.FlipFlop()
init_state = None
flipFlopProblem = mlrose.DiscreteOpt(length=100,
fitness_fn=fitness, maximize=True, max_val=2)
part1_5 = Part1(name='Flip Flop - 100', fitness=fitness,
problem=flipFlopProblem, init_state=init_state)
part1_5.runAll(savePath)
fitness = mlrose.Queens()
init_state = None
eightQueensProblem = mlrose.DiscreteOpt(length=80,
fitness_fn=fitness, maximize=False, max_val=8)
part1_2 = Part1(name='Eighty Queens', fitness=fitness,
problem=eightQueensProblem, init_state=init_state)
part1_2.runAll(savePath)
fitness = mlrose.FlipFlop()
init_state = None
flipFlopProblem = mlrose.DiscreteOpt(length=15,
fitness_fn=fitness, maximize=True, max_val=2)
part1_5 = Part1(name='Flip Flop - 15', fitness=fitness,
problem=flipFlopProblem, init_state=init_state)
part1_5.runAll(savePath)
edges = [(0, 1), (0, 2), (0, 4), (1, 3), (2, 0), (2, 3), (3, 4)]
fitness = mlrose.MaxKColor(edges)
init_state = None
maxKColorsProblem = mlrose.DiscreteOpt(length=7,
fitness_fn=fitness, maximize=False, max_val=2)
part1_3 = Part1(name='Max-K Color', fitness=fitness,
problem=maxKColorsProblem, init_state=init_state)
part1_3.runAll(savePath)
# =============================================================
# Source - Tutorial from MLRose Docs
# https://mlrose.readthedocs.io/en/stable/source/tutorial2.html
#
# =============================================================
# Create list of city coordinates
coords_list = [(1, 1), (4, 2), (5, 2), (6, 4), (4, 4), (3, 6), (1, 5), (2, 3)]
# Initialize fitness function object using coords_list
fitness_coords = mlrose.TravellingSales(coords = coords_list)
# Create list of distances between pairs of cities
dist_list = [(0, 1, 3.1623), (0, 2, 4.1231), (0, 3, 5.8310), (0, 4, 4.2426), \
(0, 5, 5.3852), (0, 6, 4.0000), (0, 7, 2.2361), (1, 2, 1.0000), \
(1, 3, 2.8284), (1, 4, 2.0000), (1, 5, 4.1231), (1, 6, 4.2426), \
(1, 7, 2.2361), (2, 3, 2.2361), (2, 4, 2.2361), (2, 5, 4.4721), \
(2, 6, 5.0000), (2, 7, 3.1623), (3, 4, 2.0000), (3, 5, 3.6056), \
(3, 6, 5.0990), (3, 7, 4.1231), (4, 5, 2.2361), (4, 6, 3.1623), \
(4, 7, 2.2361), (5, 6, 2.2361), (5, 7, 3.1623), (6, 7, 2.2361)]
# Initialize fitness function object using dist_list
fitness_dists = mlrose.TravellingSales(distances = dist_list)
# Define optimization problem object
problem_fit = mlrose.TSPOpt(length = 8, fitness_fn = fitness_coords, maximize=False)
coords_list = [(1, 1), (4, 2), (5, 2), (6, 4), (4, 4), (3, 6), (1, 5), (2, 3)]
# Define optimization problem object
problem_no_fit = mlrose.TSPOpt(length = 8, coords = coords_list, maximize=False)
part1_6 = Part1(name='TSP', fitness=coords_list,
problem=problem_no_fit, init_state=None)
part1_6.runAll(savePath)
# Knapsack
weights = np.random.randint(2, high=20, size=50)
values = np.random.randint(2, high=100, size=50)
max_weight_pct = 0.8
fitness = mlrose.Knapsack(weights, values, max_weight_pct)
knapsackProblem = mlrose.DiscreteOpt(length=50,
fitness_fn=fitness, maximize=False, max_val=2)
part1_7 = Part1(name='Knapsack', fitness=fitness,
problem=knapsackProblem, init_state=None)
part1_7.runAll(savePath)
max_it = 200
attempts = max_it * 3/4
rhc_fitnesses = []
sa_fitnesses = []
ga_fitnesses = []
m_fitnesses = []
rhc_times = []
sa_times = []
ga_times = []
m_times = []
test_range = [4,6,8,10,15,20,25]
final_prob_len = 30
fitness_cust = mlr.SixPeaks(t_pct=0.1)
schedule = mlr.GeomDecay(decay=0.99)
print(f"Attempts: {attempts}")
print(f"Max Iterations: {max_it}")
print(f"Problem Sizes: {test_range}")
print(f"Last Problem Size: {final_prob_len}\n\n")
part2_time = 0.0
print(f"\n######### PART 2 #########\n")
for i in test_range:
start = time.time()
print(f"Running for subproblem size: {i}")
def run_SixPeaks(self, mode=None):
fitness_fn = mlrose.SixPeaks(t_pct=0.15)
self.run_complexity(fitness_fn, mode)
def six_peaks(length=50):
return mlrose.SixPeaks(), length * 2