def maxKColor(edges, nodes, colors):
fitness = mlrose.MaxKColor(edges)
problem = mlrose.DiscreteOpt(length = nodes, fitness_fn = fitness, maximize = False, max_val = colors)
t0 = time()
best_state, best_fitness = mlrose.random_hill_climb(problem, max_attempts=100, max_iters=np.inf,
init_state=None)
finish = time() - t0
print(best_state)
print(best_fitness)
print(finish)
def get_prob(self, t_pct=None, p_length=None):
if self.prob_name == 'Four Peaks':
fitness = mlrose.FourPeaks(t_pct)
p_len = 100
self.schedule = mlrose.ExpDecay()
self.restarts = 0
self.mutation_prob = 0.1
self.keep_pct = 0.1
self.pop_size = 500
elif self.prob_name == "Continuous Peaks":
fitness = mlrose.ContinuousPeaks(t_pct)
p_len = 100
self.schedule = mlrose.GeomDecay()
self.restarts = 0
self.mutation_prob = 0.1
self.keep_pct = 0.2
self.pop_size = 200
elif self.prob_name == "Max K Color":
fitness = mlrose.MaxKColor(self.COLOREDGE)
p_len = 100
self.schedule = mlrose.ExpDecay()
self.restarts = 0
self.mutation_prob = 0.2
self.keep_pct = 0.2
self.pop_size = 200
elif self.prob_name == "Flip Flop":
fitness = mlrose.FlipFlop()
p_len = 100
self.schedule = mlrose.ArithDecay()
self.restarts = 0
self.mutation_prob = 0.2
self.keep_pct = 0.5
self.pop_size = 500
elif self.prob_name == "One Max":
fitness = mlrose.OneMax()
p_len = 100
self.schedule = mlrose.GeomDecay()
self.restarts = 0
self.mutation_prob = 0.2
self.keep_pct = 0.1
self.pop_size = 100
else:
fitness = None
p_len = 0
if p_length is None:
p_length = p_len
problem = mlrose.DiscreteOpt(length=p_length, fitness_fn=fitness)
init_state = np.random.randint(2, size=p_length)
return problem, init_state
def KColorMax(state, edges, colors):
print("In KColorMax Function")
fitness = mlrose.MaxKColor(edges)
problem = mlrose.DiscreteOpt(length=state.shape[0],
fitness_fn=fitness,
maximize=False,
max_val=colors)
rh_iterations = [10, 25, 50, 75, 100, 125, 200, 250, 300]
max_attempts = 10
rh_fitness = []
sm_fitness = []
ga_fitness = []
mi_fitness = []
time_iterations = []
#print("Random Hill Iterations")
#print(rh_iterations)
for itera in rh_iterations:
color_rh_st = timeit.timeit()
rh_best_fitness = randomHill(problem, state, max_attempts, itera)
color_rh_et = timeit.timeit()
time_iterations.append(color_rh_et - color_rh_st)
color_sm_st = timeit.timeit()
sm_best_fitness = simulatedAnnealing(problem, state, max_attempts,
itera)
color_sm_et = timeit.timeit()
time_iterations.append(color_sm_et - color_sm_st)
color_ga_st = timeit.timeit()
ga_best_fitness = genetic(problem, state, max_attempts, itera)
color_ga_et = timeit.timeit()
time_iterations.append(color_ga_et - color_ga_st)
color_mi_st = timeit.timeit()
mi_best_fitness = mimic(problem, state, max_attempts, itera)
color_mi_et = timeit.timeit()
time_iterations.append(color_mi_et - color_mi_st)
rh_fitness.append(rh_best_fitness)
sm_fitness.append(sm_best_fitness)
ga_fitness.append(ga_best_fitness)
mi_fitness.append(mi_best_fitness)
#print("rh_best_fitness inside while loop")
#print(rh_best_fitness)
return rh_iterations, rh_fitness, sm_fitness, ga_fitness, mi_fitness, time_iterations
import mlrose
import numpy as np
import time
# Initialize fitness function object using state
nodes = [i for i in range(100)]
it = iter(nodes)
edges = [(x, next(it)) for x in it]
fitness = mlrose.MaxKColor(edges=edges)
# Define optimization problem object
problem_fit = mlrose.DiscreteOpt(length=100,
fitness_fn=fitness,
maximize=True,
max_val=2)
# Set random seed
np.random.seed(2)
## Solve problem using the genetic algorithm
start_time = time.time()
best_state1, best_fitness1 = mlrose.random_hill_climb(problem_fit,
max_attempts=100,
max_iters=100)
start_time2 = time.time()
best_state2, best_fitness2 = mlrose.simulated_annealing(
problem_fit, schedule=mlrose.GeomDecay(), max_attempts=100, max_iters=100)
start_time3 = time.time()
multipeak_fn = mlrose.CustomFitness(multipeak_eq)
# Knapsack
knapsack_weights = [5, 10, 15, 20, 25, 30, 35, 40, 45]
knapsack_values = np.arange(1, len(knapsack_weights) + 1)
knapsack_max_weight = 0.7
knapsack_fn = mlrose.Knapsack(knapsack_weights, knapsack_values,
knapsack_max_weight)
# K-colors
kcolor_edges = [(0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (0, 6), (0, 7), (0, 8),
(0, 9), (1, 4), (1, 5), (1, 6), (1, 7), (2, 3), (2, 5), (2, 7),
(3, 5), (3, 6), (3, 7), (3, 9), (4, 5), (5, 6), (5, 7), (5, 8),
(6, 7), (7, 8), (8, 9)]
kcolor_edges = list({tuple(sorted(edge)) for edge in kcolor_edges})
kcolor_fn = mlrose.MaxKColor(kcolor_edges)
def multipeak_prob_wrapper():
return mlrose.DiscreteOpt(length=20, fitness_fn=multipeak_fn)
def knapsack_prob_wrapper():
return mlrose.DiscreteOpt(length=len(knapsack_weights),
fitness_fn=knapsack_fn)
def kcolor_prob_wrapper():
return mlrose.DiscreteOpt(length=10, fitness_fn=kcolor_fn, max_val=2)
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])
bestFitnessRHC, timeRHC = radomHillClimb(fitnessArray[x], x)
def __discrete_bit_size_problems(problem,
algorithm,
length,
max_iter,
max_attempt,
init_state,
edges=None,
coords=None):
if problem == 'fourpeaks':
__fit = mlrose.FourPeaks()
__problem = mlrose.DiscreteOpt(length=length,
fitness_fn=__fit,
maximize=True,
max_val=2)
elif problem == 'kcolor':
__fit = mlrose.MaxKColor(edges=edges)
__problem = mlrose.DiscreteOpt(length=length,
fitness_fn=__fit,
maximize=True)
elif problem == 'flipflop':
__fit = mlrose.OneMax()
__problem = mlrose.DiscreteOpt(length=length,
fitness_fn=__fit,
maximize=True,
max_val=2)
elif problem == 'continouspeaks':
__fit = mlrose.ContinuousPeaks()
__problem = mlrose.DiscreteOpt(length=length,
fitness_fn=__fit,
maximize=True,
max_val=2)
elif problem == 'travellingsales':
__fit = mlrose.TravellingSales(coords=coords)
__problem = mlrose.TSPOpt(length=length,
fitness_fn=__fit,
maximize=False)
if algorithm == 'random_hill_climb':
start_time = time.time()
best_state, best_fitness, best_curve = mlrose.random_hill_climb(
__problem,
max_iters=max_iter,
max_attempts=max_attempt,
init_state=init_state,
curve=True)
end_time = time.time() - start_time
elif algorithm == 'simulated_annealing':
start_time = time.time()
best_state, best_fitness, best_curve = mlrose.simulated_annealing(
__problem,
max_iters=max_iter,
max_attempts=max_attempt,
init_state=init_state,
curve=True)
end_time = time.time() - start_time
elif algorithm == 'genetic_alg':
start_time = time.time()
best_state, best_fitness, best_curve = mlrose.genetic_alg(
__problem,
max_iters=max_iter,
max_attempts=max_attempt,
curve=True)
end_time = time.time() - start_time
elif algorithm == 'mimic':
start_time = time.time()
best_state, best_fitness, best_curve = mlrose.mimic(
__problem,
max_iters=max_iter,
max_attempts=max_attempt,
curve=True)
end_time = time.time() - start_time
return best_fitness, end_time, best_curve
def k_COLOR():
problem = mlrose.DiscreteOpt(
length=20,
fitness_fn=mlrose.MaxKColor(
edges=[(0, 1), (0, 2), (0, 4), (1, 3), (2, 0), (2, 3), (3, 4)]),
maximize=True,
max_val=2)
init_state = np.array([0] * 20)
startTime = datetime.now()
best_state, best_fitness, fitness_curve_rhc = mlrose.random_hill_climb(
problem,
max_attempts=1000,
max_iters=2500,
restarts=0,
init_state=init_state,
curve=True,
random_state=1,
state_fitness_callback=None,
callback_user_info=None)
totalTime = datetime.now() - startTime
rhcTime = totalTime.total_seconds()
print("RHC")
print("Time: ", rhcTime)
print("best_fitness: ", best_fitness)
print("Iteration: %d " % len(fitness_curve_rhc))
###############################
startTime = datetime.now()
best_statesa, best_fitnesssa, fitness_curve_sa = mlrose.simulated_annealing(
problem,
max_attempts=1000,
max_iters=2500,
init_state=init_state,
curve=True,
random_state=1,
state_fitness_callback=None,
callback_user_info=None)
totalTime = datetime.now() - startTime
saTime = totalTime.total_seconds()
print("SA")
print("Time: ", saTime)
print("best_fitness: ", best_fitnesssa)
print("Iteration: %d " % len(fitness_curve_sa))
###############################
startTime = datetime.now()
best_statega, best_fitnessga, fitness_curve_ga = mlrose.genetic_alg(
problem,
max_attempts=1000,
max_iters=2500,
curve=True,
random_state=1,
state_fitness_callback=None,
callback_user_info=None)
totalTime = datetime.now() - startTime
gaTime = totalTime.total_seconds()
print("GA")
print("Time: ", gaTime)
print("best_fitness: ", best_fitnessga)
print("Iteration: %d " % len(fitness_curve_ga))
###############################
startTime = datetime.now()
best_statemm, best_fitnessmm, fitness_curve_mm = mlrose.mimic(
problem,
max_attempts=1000,
max_iters=2500,
curve=True,
random_state=1,
state_fitness_callback=None,
callback_user_info=None)
totalTime = datetime.now() - startTime
mmTime = totalTime.total_seconds()
print("MIMIC")
print("Time: ", mmTime)
print("best_fitness: ", best_fitnessmm)
print("Iteration: %d " % len(fitness_curve_mm))
def main():
name_of_exp = "K-Color"
edges = [(0, 1), (0, 2), (0, 4), (1, 3), (2, 0), (2, 3), (3, 4)]
fitness = mlrose.MaxKColor(edges)
init_state = np.zeros(5)
problem = mlrose.DiscreteOpt(length=5, fitness_fn=fitness, maximize=False, max_val=2)
# Define decay schedule
schedule = mlrose.ExpDecay()
x_s = []
y_s = []
z_s = ['RHC', 'SA', 'GA', 'MIMIC']
w_s = []
max_val = 3.0
found_flag = False
for restarts in np.arange(0, 5):
if found_flag:
break
for max_iter_atts in np.arange(10, 1000, 10):
if found_flag:
break
# Solve problem using simulated annealing
best_state, best_fitness, learning_curve, timing_curve = mlrose.random_hill_climb(problem, max_attempts=int(
max_iter_atts), max_iters=int(max_iter_atts),
restarts=int(restarts),
init_state=init_state,
curve=True,
random_state=1)
if best_fitness >= max_val:
x_s.append(np.arange(0, len(learning_curve)))
y_s.append(learning_curve)
w_s.append(timing_curve)
print(best_state)
print(best_fitness)
print(max_iter_atts)
print(restarts)
found_flag = True
found_flag = False
for sched in [mlrose.ExpDecay(), mlrose.GeomDecay(), mlrose.ArithDecay()]:
if found_flag:
break
for max_iter_atts in np.arange(10, 1000, 10):
if found_flag:
break
best_state, best_fitness, learning_curve, timing_curve = mlrose.simulated_annealing(problem,
max_attempts=int(
max_iter_atts),
max_iters=int(
max_iter_atts),
schedule=sched,
init_state=init_state,
curve=True,
random_state=1)
if best_fitness >= max_val:
x_s.append(np.arange(0, len(learning_curve)))
y_s.append(learning_curve)
w_s.append(timing_curve)
print(best_state)
print(best_fitness)
print(max_iter_atts)
print(sched)
found_flag = True
found_flag = False
for prob in np.arange(0.1, 1.1, 0.1):
if found_flag:
break
for pop_size in np.arange(100, 5000, 100):
if found_flag:
break
for max_iter_atts in np.arange(10, 1000, 10):
if found_flag:
break
best_state, best_fitness, learning_curve, timing_curve = mlrose.genetic_alg(problem,
pop_size=int(pop_size),
mutation_prob=prob,
max_attempts=int(
max_iter_atts),
max_iters=int(
max_iter_atts),
curve=True,
random_state=1)
if best_fitness >= max_val:
x_s.append(np.arange(0, len(learning_curve)))
y_s.append(learning_curve)
w_s.append(timing_curve)
print(best_state)
print(best_fitness)
print(max_iter_atts)
print(prob)
print(pop_size)
found_flag = True
found_flag = False
for prob in np.arange(0.1, 1.1, 0.1):
if found_flag:
break
for pop_size in np.arange(100, 5000, 100):
if found_flag:
break
for max_iter_atts in np.arange(10, 1000, 10):
if found_flag:
break
best_state, best_fitness, learning_curve, timing_curve = mlrose.mimic(problem, pop_size=int(pop_size),
keep_pct=prob,
max_attempts=int(max_iter_atts),
max_iters=int(max_iter_atts),
curve=True,
random_state=1,
fast_mimic=True)
if best_fitness >= max_val:
x_s.append(np.arange(0, len(learning_curve)))
y_s.append(learning_curve)
w_s.append(timing_curve)
print(best_state)
print(best_fitness)
print(max_iter_atts)
print(prob)
print(pop_size)
found_flag = True
for x, y, z in zip(x_s, y_s, z_s):
plt.plot(x, y, label=z)
plt.legend()
plt.title('Randomized Optimization Iterations vs Fitness Function Value for {}'.format(name_of_exp))
plt.xlabel('Function iteration count')
plt.ylabel('Fitness function value')
plt.show()
plt.clf()
for x, w, z in zip(x_s, w_s, z_s):
plt.plot(x, w, label=z)
plt.legend()
plt.title('Randomized Optimization Time vs Fitness Function Value for {}'.format(name_of_exp))
plt.xlabel('Function iteration count')
plt.ylabel('Time in Seconds')
plt.show()
def optimizationFunction(n,iMin, iMax, iStep, jMin, jMax, jStep):
np.random.seed(100)
optScenarios = ['Knap Sack', 'Four Peaks', 'K - Colors']
for x in range(3):
if x == 0:
weights = np.random.randint(1,10,size=n)
#values = np.random.randint(1,50,size=n)
values = [i for i in range(1,n+1)]
max_weight_pct = 0.5
fitnessFunction = mlrose.Knapsack(weights, values, max_weight_pct)
optModel = mlrose.DiscreteOpt(len(values), fitness_fn = fitnessFunction, maximize=True)
elif x == 1:
inp = [0] * int(n/2) + [1]*int(n - int(n/2))
np.random.shuffle(inp)
fitnessFunction = mlrose.FourPeaks(t_pct = 0.15)
optModel = mlrose.DiscreteOpt(len(inp), fitness_fn = fitnessFunction, maximize =True)
elif x == 2:
edges = [(np.random.randint(0,n), np.random.randint(0,n)) for ab in range(n)]
fitnessFunction = mlrose.MaxKColor(edges)
optModel = mlrose.DiscreteOpt(len(edges), fitness_fn = fitnessFunction, maximize =True)
decay = mlrose.ExpDecay()
optResults = {'iterations':[],'attempts':[],'fitness':[],'time':[], 'optimization':[]}
for i in range(iMin,iMax,iStep):
for j in range(jMin,jMax,jStep):
start_time = timer()
best_state, best_fitness = mlrose.random_hill_climb(optModel, max_attempts = j, max_iters = i, random_state=100)
opt_time = timer() - start_time
optResults['iterations'].append(i)
optResults['attempts'].append(j)
optResults['fitness'].append(best_fitness)
optResults['time'].append(opt_time)
optResults['optimization'].append('Random Hill')
start_time = timer()
best_state, best_fitness = mlrose.simulated_annealing(optModel, schedule=decay, max_attempts = j,max_iters = i,random_state=1000)
opt_time = timer() - start_time
optResults['iterations'].append(i)
optResults['attempts'].append(j)
optResults['fitness'].append(best_fitness)
optResults['time'].append(opt_time)
optResults['optimization'].append('Simulated Annealing')
start_time = timer()
best_state, best_fitness = mlrose.genetic_alg(optModel, pop_size=200, mutation_prob = 0.25, max_attempts = j, max_iters = i, random_state=5000)
opt_time = timer() - start_time
optResults['iterations'].append(i)
optResults['attempts'].append(j)
optResults['fitness'].append(best_fitness)
optResults['time'].append(opt_time)
optResults['optimization'].append('Genetic Algorithm')
start_time = timer()
best_state, best_fitness = mlrose.mimic(optModel, pop_size = 200, keep_pct = 0.3, max_attempts = j, max_iters = i, random_state=150)
opt_time = timer() - start_time
optResults['iterations'].append(i)
optResults['attempts'].append(j)
optResults['fitness'].append(best_fitness)
optResults['time'].append(opt_time)
optResults['optimization'].append('MIMIC')
optResults = pd.DataFrame(optResults)
plotGraphs(optResults,optScenarios[x])
import pandas as pd
import math
import time
import mlrose
import matplotlib.pyplot as plt
from matplotlib.legend_handler import HandlerLine2D
edges = [(0, 1), (0, 2), (0, 4), (1, 3), (2, 0), (2, 3), (3, 4), (5, 6),
(6, 8), (7, 6), (3, 6), (6, 8), (1, 9), (3, 9), (6, 9), (7, 9),
(1, 10), (6, 10), (3, 9), (11, 5), (6, 11), (1, 11), (5, 7), (3, 11),
(2, 10), (7, 11), (6, 12), (13, 8), (14, 5), (13, 12), (15, 4),
(12, 16), (17, 18), (18, 19), (19, 2), (20, 4), (6, 19), (11, 21),
(22, 15), (10, 23)]
schedule = mlrose.ExpDecay()
fitness1 = mlrose.MaxKColor(edges[0:5])
fitness2 = mlrose.MaxKColor(edges[0:7])
fitness3 = mlrose.MaxKColor(edges[0:9])
fitness4 = mlrose.MaxKColor(edges[0:11])
fitness5 = mlrose.MaxKColor(edges[0:13])
fitness6 = mlrose.MaxKColor(edges[0:15])
fitness7 = mlrose.MaxKColor(edges[0:19])
fitness8 = mlrose.MaxKColor(edges[0:21])
fitness9 = mlrose.MaxKColor(edges[0:24])
fitness10 = mlrose.MaxKColor(edges[0:30])
start = time.time()
problem_fit1 = mlrose.DiscreteOpt(length=5,
fitness_fn=fitness1,
maximize=True,
max_val=4)
#sprint(fitness_two.evaluate(state))
best_state_two, best_fitness_two = ml.genetic_alg(opt_two, random_state = 2)
#best_state_two, best_fitness_two = ml.random_hill_climb(opt_two, random_state = 2)
best_state_two, best_fitness_two = ml.simulated_annealing(opt_two, max_iters=1, random_state = 2)
#best_state_two, best_fitness_two = ml.mimic(opt_two, max_iters=1, random_state = 2)
print(best_state_two)
print(best_fitness_two)
#plt.scatter([0,1,0,1,2,3],[1,2,2,3,3,4])
#plt.savefig("test.png")
edges = [(0, 1), (0, 2), (0, 4), (1, 3), (2, 0), (2, 3), (3, 4)]
fitness_three = ml.MaxKColor(edges)
opt_three = ml.DiscreteOpt(length=5, fitness_fn=fitness_three, maximize=False)
#best_state_three, best_fitness_three = ml.genetic_alg(opt_three, random_state = 2)
#best_state_three, best_fitness_three = ml.random_hill_climb(opt_three, random_state = 2)
best_state_three, best_fitness_three = ml.simulated_annealing(opt_three, random_state = 2)
best_state_three, best_fitness_three = ml.mimic(opt_three, max_iters=1, random_state = 2)
print(fitness_three.evaluate([1, 0, 0, 1, 0]))
print(best_state_three)
print(best_fitness_three)
import random
import mlrose
import numpy as np
import matplotlib.pyplot as plt
from time import clock
#https://mlrose.readthedocs.io/en/stable/source/fitness.html
edges = [(0, 1), (0, 2), (0, 4), (1, 3), (2, 0), (2, 3), (3, 4), (5, 1)]
edges_long = [(1, 1), (4, 2), (5, 2), (6, 4), (4, 4), (3, 6), (1, 5), (2, 3),
(5, 7), (5, 9), (5, 10), (4, 3), (4, 8), (4, 10), (6, 8), (6, 9),
(9, 3), (9, 6), (9, 1), (9, 8)]
fitness = mlrose.MaxKColor(edges_long)
init_state = np.array([0, 1, 0, 1, 1])
k = 20
problem = mlrose.DiscreteOpt(length=k, fitness_fn=fitness, maximize=False)
# Define decay schedule
schedule = mlrose.ExpDecay()
RHC_iterations = []
SA_iterations = []
GA_iterations = []
MIMIC_iterations = []
iterations = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024]
RHC_timings = []
SA_timings = []
GA_timings = []
