def __init__(self, coords, temp, alpha, stopping_temp, stopping_iter):
''' animate the solution over time
Parameters
----------
coords: array_like
list of coordinates
temp: float
initial temperature
alpha: float
rate at which temp decreases
stopping_temp: float
temerature at which annealing process terminates
stopping_iter: int
interation at which annealing process terminates
'''
self.coords = coords
self.sample_size = len(coords)
self.temp = temp
self.alpha = alpha
self.stopping_temp = stopping_temp
self.stopping_iter = stopping_iter
self.iteration = 1
self.dist_matrix = tsp_utils.vectorToDistMatrix(coords)
self.curr_solution = tsp_utils.nearestNeighbourSolution(
self.dist_matrix)
self.best_solution = self.curr_solution
self.solution_history = [self.curr_solution]
self.curr_weight = self.weight(self.curr_solution)
self.initial_weight = self.curr_weight
self.min_weight = self.curr_weight
self.weight_list = [self.curr_weight]
self.acceptance_probability_list = []
self.temperature_list = [self.temp]
self.random_solution = tsp_utils.randomSolution(self.dist_matrix)
self.random_weight = self.weight(self.random_solution)
print
print "Random: ", self.random_weight
print
print "Greedy: ", self.curr_weight
print
def __init__(self,
adjacency_matrix,
temp=-1,
alpha=-1,
stopping_temp=-1,
stopping_iter=-1):
''' animate the solution over time
Parameters
----------
coords: array_like
list of coordinates
temp: float
initial temperature
alpha: float
rate at which temp decreases
stopping_temp: float
temerature at which annealing process terminates
stopping_iter: int
interation at which annealing process terminates
'''
self.adjacency_matrix = adjacency_matrix
self.sample_size = len(adjacency_matrix)
self.temp = math.sqrt(self.sample_size) if temp == -1 else temp
self.alpha = 0.995 if alpha == -1 else alpha
self.stopping_temp = 1e-8 if stopping_temp == -1 else stopping_temp
self.stopping_iter = 200000 if stopping_iter == -1 else stopping_iter
self.iteration = 1
self.dist_matrix = adjacency_matrix
self.curr_solution = tsp_utils.nearestNeighbourSolution(
self.dist_matrix)
self.best_solution = self.curr_solution
self.solution_history = [self.curr_solution]
self.curr_weight = self.weight(self.curr_solution)
self.initial_weight = self.curr_weight
self.min_weight = self.curr_weight
self.weight_list = [self.curr_weight]
print('Intial weight: ', self.curr_weight)