Parameters.py

# Simulated Annealing for Clustering Problems
import math
import random
num_objects = 10 # numbers of objects to be clustered
num_cost_increases = 100
avg_cost_increase = 200
acc_ratio = 0.75 # acceptance ratio should e between 0 and 1
prob_e = 0.00000000001 # probability factor
beta = 0.125
max_iter =  4 * num_objects # maximum number of iterations
num_temp = 200
k = 6 # number of labels
# SA accepts cost increse beta * avg_cost_increase at the probability of prob_e

initial_temperature = avg_cost_increase / math.log( num_cost_increases / ((num_cost_increases * acc_ratio) - (1-acc_ratio) * (max_iter - num_cost_increases)))
final_temperature = -beta * avg_cost_increase / math.log(prob_e)
alpha = math.pow(final_temperature / initial_temperature , 1 / num_temp) # decay rate for temperature

initial_state = {1:'A', 2: 'B', 3:'A', 4:'B', 5:'C', 6:'B', 7:'A', 8:'None', 9:'None', 10:'None' }
unused_labels = ['D', 'E', 'F']