def experiment_rhc_1(self):
print('restart vary')
init_state = None
restart_lengths = np.arange(10, 800, 100)
result = np.zeros((len(self.rand_seeds), len(restart_lengths)))
#best_state = np.zeros((len(self.rand_seeds), len(restart_lengths)))
print(self.problem_type)
for i in range(len(self.rand_seeds)):
rand_state = self.rand_seeds[i]
prob_length = 20
for j in range(len(restart_lengths)):
restart_length = restart_lengths[j]
max_iter = np.inf
#max_attempts is varied by trial and error
max_attempts = 100
fl = CustomProblem(prob_length, self.problem_type)
problem = fl.create_problem()
alg = RHC(problem, init_state, rand_state, max_attempts,
max_iter, restart_length.item())
best_state, best_fitness = alg.optimize()
result[i][j] = best_fitness
print('best fitness')
print(str(result))
print('best state')
print(best_state)
avg_result = np.mean(result, axis=0)
print('avg result for varying input size' + str(avg_result))
title = self.problem_type + ' with RHC - # of Restarts Variation'
plot_curve(restart_lengths, avg_result, title, '# of Restarts',
'Best Score')
def experiment(self):
se = SixPeaks(length = 20)
problem = se.create_problem()
rhc_a = RHC(problem)
best_state ,best_fitness = rhc_a.optimize()
print(best_fitness)
print(best_state)
def experiment_rhc_4(self):
init_state = None
t_pcts = np.arange(0.1, 1, 0.1)
result = np.zeros((len(self.rand_seeds), len(t_pcts)))
best_score = None
max_iter = np.inf
for i in range(len(self.rand_seeds)):
restarts = 400
rand_state = self.rand_seeds[i]
for j in range(len(t_pcts)):
prob_length = 20
restarts = 400
max_attempt = 50
t_pct = t_pcts[j].item()
fl = SixPeaks(prob_length, t_pct)
problem = fl.create_problem()
alg = RHC(problem, init_state, rand_state, max_attempt,
max_iter, restarts)
best_score, best_fitness = alg.optimize()
result[i][j] = best_fitness
avg_result = np.mean(result, axis=0)
print('avg result ' + str(avg_result))
print('best score')
print(best_score)
title = self.problem_type + ' with RHC - Threshold Variation'
plot_curve(t_pcts, avg_result, title, 'Threshold', 'Best Score')
def experiment_rhc_3(self):
init_state = None
max_iters = np.arange(100, 5000, 100)
result = np.zeros((len(self.rand_seeds), len(max_iters)))
for i in range(len(self.rand_seeds)):
rand_state = self.rand_seeds[i]
for j in range(len(max_iters)):
prob_length = 20
fl = CustomProblem(prob_length, self.problem_type)
problem = fl.create_problem()
max_attempt = 200
restarts = 150
max_iter = max_iters[j].item()
alg = RHC(problem, init_state, rand_state, max_attempt,
max_iter, restarts)
best_score, best_fitness = alg.optimize()
result[i][j] = best_fitness
avg_result = np.mean(result, axis=0)
print('avg result ' + str(avg_result))
print('best score')
print(best_score)
title = self.problem_type + ' with RHC - Max Iterations Variation'
plot_curve(max_iters, avg_result, title, 'Max Iterations',
'Best Score')
def experiment_rhc_22(self):
init_state = None
max_attempts = np.array(
[5, 10, 15, 30, 40, 50, 60, 80, 100, 200, 300, 350])
result = np.zeros((len(self.rand_seeds), len(max_attempts)))
best_score = None
for i in range(len(self.rand_seeds)):
restarts = 0
rand_state = self.rand_seeds[i]
for j in range(len(max_attempts)):
prob_length = 20
fl = CustomProblem(prob_length, self.problem_type)
problem = fl.create_problem()
max_attempt = max_attempts[j].item()
max_iter = np.inf
alg = RHC(problem, init_state, rand_state, max_attempt,
max_iter, restarts)
best_score, best_fitness = alg.optimize()
result[i][j] = best_fitness
avg_result = np.mean(result, axis=0)
print('avg result ' + str(avg_result))
print('best score')
print(best_score)
title = self.problem_type + ' with RHC - Max Attempts Variation - 0 restart'
plot_curve(max_attempts, avg_result, title, 'Max Attempts',
'Best Score')
def experiment_optimal_rhc(self):
prob_length = 20
max_attempt = 100
restarts = 100
max_iter = 1000
rand_state = 42
init_state = None
fl = CustomProblem(prob_length, self.problem_type)
problem = fl.create_problem()
start = time.time()
alg = RHC(problem, init_state, rand_state, max_attempt, max_iter,
restarts)
best_score, best_fitness = alg.optimize()
end = time.time()
diff = abs(end - start)
print('time taken for RHC- Knapsack: ' + str(diff))
def experiment_rhc_11(self):
init_state = None
prob_lengths = np.arange(7, 30)
result = np.zeros((len(self.rand_seeds), len(prob_lengths)))
print(self.problem_type)
for i in range(len(self.rand_seeds)):
rand_state = self.rand_seeds[i]
for j in range(len(prob_lengths)):
prob_length = prob_lengths[j]
fl = CustomProblem(prob_length.item(), self.problem_type)
problem = fl.create_problem()
alg = RHC(problem, init_state, rand_state, 10, 1000)
best_fitness = alg.optimize()
result[i][j] = best_fitness
print(str(result))
avg_result = np.mean(result, axis=0)
print('avg result for varying input size' + str(avg_result))
title = self.problem_type + ' with RHC - Input Size Variation'
plot_curve(prob_lengths, avg_result, title, 'Input Size', 'Best Score')