def rebuild_cities(cities_nm, neuron_chains,num_depots):
'''
rebuild cities_nm
:param cities:
:param neuron_chains:
:return:
'''
cities_od = cities_nm.copy()
depots = cities_nm.head(num_depots)[['x','y']]
gpids = -np.ones([len(cities_nm),2])
gpids[num_depots:] = cities_od.iloc[num_depots:][['x', 'y']].apply(
lambda c: select_closest_gpid(neuron_chains, c),
axis=1, raw=True).to_numpy()
idx = 0
for chain,depot in zip(neuron_chains,depots.to_numpy()):
gpids[:num_depots,0][idx] = idx
gpids[:num_depots,1][idx] = select_closest(chain,depot)
idx+=1
cities_od['gid'] = gpids[:,0]
cities_od['pid'] = gpids[:,1]
cities_od = cities_od.sort_values(['gid', 'pid'],ascending=[True,True])
return cities_od
def som(problem, iterations, learning_rate=0.8):
"""Solve the TSP using a Self-Organizing Map."""
# Obtain the normalized set of cities (w/ coord in [0,1])
cities = problem.copy()
cities[['x', 'y']] = normalize(cities[['x', 'y']]) # 归一化
# The population size is 8 times the number of cities 神经元个数
n = cities.shape[0] * 3
#n = cities.shape[0] * 3 # 测试用，by EE526
# Generate an adequate network of neurons:
network = generate_network(cities, n, c=4)
print('Network of {} neurons created. Starting the iterations:'.format(n))
for i in range(iterations):
# Check for plotting interval
if i % 100 == 0: # 每隔100次画出神经元图像
plot_network(cities,
network,
name='out_files\process\city_network%d.png' %
(i // 100))
if not i % 100: # if i%100==0
print('\t> Iteration {}/{}'.format(i, iterations), end="\r")
# Choose a random city
city = cities.sample(1)[['x', 'y']].values # 随机从cities中选取一组数，1*2数组
winner_idx = select_closest(network, city)
# Generate a filter that applies changes to the winner's gaussian
gaussian = get_neighborhood(winner_idx, n // 10,
network.shape[0]) # 高斯核函数是算法的核心
# Update the network's weights (closer to the city)
network += gaussian[:, np.newaxis] * learning_rate * (
city - network) # np.newaxis在该位置增加一维，变成神经元数*1维
# 实际上就是为了让对应的移动乘以对应的坐标
# Decay the variables
learning_rate = learning_rate * 0.99997
n = n * 0.9994
# Check if any parameter has completely decayed.
if n < 1:
print('Radius has completely decayed, finishing execution',
'at {} iterations'.format(i))
break
if learning_rate < 0.001:
print('Learning rate has completely decayed, finishing execution',
'at {} iterations'.format(i))
break
else:
print('Completed {} iterations.'.format(iterations))
# plot 部分
plot_network(cities, network)
route = get_route(cities, network)
cities = problem.copy()
citiesReal = cities[['x', 'y']] # 取实际坐标
plot_route(citiesReal, route)
return route
def som(problem, iterations, learning_rate=0.8):
'''
Solve the TSP using a Self-Organizing Map.
:params problem(dataframe): 城市坐标
:params iterations(int): 最大迭代次数
:learning_rate(float): 学习率
:return route
'''
cities = problem.copy()
# Obtain the normalized set of cities (w/ coord in [0,1])
cities[['x', 'y']] = normalize(cities[['x', 'y']])
# The population size is 8 times the number of cities
# <Hyperparameter:times>
n = cities.shape[0] * 8
# Generate an adequate network of neurons:
# (n,2)
network = generate_network(n)
print('Network of {} neurons created. Starting the iterations:'.format(n))
for i in range(iterations):
if not i % 100:
print('\t> Iteration {}/{}'.format(i, iterations), end="\r")
# Choose a random city
city = cities.sample(1)[['x', 'y']].values
winner_idx = select_closest(network, city)
# Generate a filter that applies changes to the winner's gaussian
# <Hyperparameter:radius>
gaussian = get_neighborhood(winner_idx, n // 10, network.shape[0])
# Update the network's weights (closer to the city)
network += gaussian[:, np.newaxis] * learning_rate * (city - network)
# Decay the variables
# <Hyperparameter:decay rate>
learning_rate = learning_rate * 0.99997
n = n * 0.9997
# Check for plotting interval
if not i % 1000:
plot_network(cities, network, name='diagrams/{:05d}.png'.format(i))
# Check if any parameter has completely decayed.
if n < 1:
print('Radius has completely decayed, finishing execution',
'at {} iterations'.format(i))
break
if learning_rate < 0.001:
print('Learning rate has completely decayed, finishing execution',
'at {} iterations'.format(i))
break
else:
print('Completed {} iterations.'.format(iterations))
plot_network(cities, network, name='diagrams/final.png')
route = get_route(cities, network)
plot_route(cities, route, 'diagrams/route.png')
return route
def get_route(cities, network):
"""Return the route computed by a network."""
cities['winner'] = cities[['x', 'y'
]].apply(lambda c: select_closest(network, c),
axis=1,
raw=True)
return cities.sort_values('winner').index
def som(problem, iterations, learning_rate=0.8):
"""Solve the TSP using a Self-Organizing Map."""
# Obtain the normalized set of cities (w/ coord in [0,1])
dStep = 100 # 每隔100次保存一次数据
dSave = np.zeros(iterations // dStep) # 保存数据
cities = problem.copy()
cities[['x', 'y']] = normalize(cities[['x', 'y']]) # 归一化
# The population size is 8 times the number of cities 神经元个数
n = cities.shape[0] * 4
# Generate an adequate network of neurons:
network = generate_network(cities, n, c=2)
print('Network of {} neurons created. Starting the iterations:'.format(n))
for i in range(iterations):
# Check for plotting interval
# if i % 100 == 0: # 每隔100次画出神经元图像
# plot_network(cities, network, name='out_files\process\city_network%d.png'%(i//100))
if not i % 100: # if i%100==0
print('\t> Iteration {}/{}'.format(i, iterations), end="\r")
# Choose a random city
city = cities.sample(1)[['x', 'y']].values # 随机从cities中选取一组数，1*2数组
winner_idx = select_closest(network, city)
# print(winner_idx) # DEBUG
# 改进方案, 获胜神经元距离小于阈值，则直接获胜
if np.linalg.norm(city - network[winner_idx, :], axis=1) < 0.005: # 求距离
network[winner_idx, :] = city
# print(winner_idx)
else:
# Generate a filter that applies changes to the winner's gaussian
gaussian = get_neighborhood(winner_idx, n // 10, network.shape[0]) # 高斯核函数是算法的核心
# Update the network's weights (closer to the city)
network += gaussian[:, np.newaxis] * learning_rate * (city - network) # np.newaxis在该位置增加一维，变成神经元数*1维
# 实际上就是为了让对应的移动乘以对应的坐标
# Decay the variables
learning_rate = learning_rate * 0.99999
n = n * 0.9995 # 较好的参数是0.9991-0.9997
if not i % 100: # 每隔100次， 求距离
route = get_route(cities, network)
p = problem.reindex(route)
dSave[i // dStep] = route_distance(p)
else:
print('Completed {} iterations.'.format(iterations))
# plot 部分
plot_network(cities, network)
route = get_route(cities, network)
cities = problem.copy()
citiesReal = cities[['x', 'y']] # 取实际坐标
plot_route(citiesReal, route)
return route, dSave
def som(instancia,problem, iterations, learning_rate,delta_learning_rate, delta_n,sensi_radio,sensi_learning_rate,factor_neuronas,plotear):
"""Solve the TSP using a Self-Organizing Map."""
# Obtenemos primero las ciudades normalizadas (con coordenadas en [0,1])
cities = problem.copy()
cities[['x', 'y']] = normalize(cities[['x', 'y']])
#La población de neuronas se crea con factor_neuronas veces la cantidad de ciudades
n = cities.shape[0] * factor_neuronas
# Generamos una adecuada red de neuronas de la forma
network = generate_network(n)
if plotear:
print('Red de {} neuronas creadas. Comenzando las iteraciones:'.format(n))
for i in range(iterations):
if not i % 100:
print('\t> Iteración {}/{}'.format(i, iterations), end="\r")
# Se escoge una ciudad de forma aleatoria
city = cities.sample(1)[['x', 'y']].values
#Se busca la neurona más cercana a la ciudad, la winner neuron
winner_idx = select_closest(network, city)
#Genera un filtro que aplica los cambios al winner o BMU
gaussian = get_neighborhood(winner_idx, n//10, network.shape[0])
# Actualizar los pesos de la red según una distribución gaussiana
network += gaussian[:,np.newaxis] * learning_rate * (city - network)
# actualizar las parametros
learning_rate = learning_rate * delta_learning_rate
n = n * delta_n
# Chequear para plotear cada 1000 iteraciones
if plotear:
if not i % 1000:
plot_network(cities, network, name='imagenes/'+instancia+'/{:05d}.png'.format(i))
# Chequear si algún parametro a caído por debajo de la sensibilidad
if n < sensi_radio:
print('Radio por debajo de sensibilidad, Se ha terminado la ejecución',
'a {} las iteraciones'.format(i))
break
if learning_rate < sensi_learning_rate:
print('Learning rate por debajo de sensibilidad, Se ha terminado la ejecución',
'a las {} iteraciones'.format(i))
break
else:
print('Se han completado las {} iteraciones.'.format(iterations))
if plotear:
plot_network(cities, network, name='imagenes/'+instancia+'/final.png')
route = get_route(cities, network)
if plotear:
plot_route(cities, route, 'imagenes/'+instancia+'/route.png')
return route
def IDtspsom(problem,
iterations,
learning_rate=0.8,
dsigma=0.9995,
dalpha=0.99997):
"""Solve the TSP using a Self-Organizing Map.
密度+渗透，n*3
"""
# Obtain the normalized set of cities (w/ coord in [0,1])
cities = problem.copy()
dStep = 100 # 每隔100次保存一次数据
dSave = np.zeros(iterations // dStep) # 保存数据
cities[['x', 'y']] = normalize(cities[['x', 'y']]) # 归一化
# The population size is 8 times the number of cities 神经元个数
n = cities.shape[0] * 3
# Generate an adequate network of neurons:
network = generate_network(cities, n, c=3)
print('Network of {} neurons created. Starting the iterations:'.format(n))
for i in range(iterations):
city = cities.sample(1)[['x', 'y']].values # 随机从cities中选取一组数，1*2数组
winner_idx = select_closest(network, city)
# 改进方案, 获胜神经元距离小于阈值，则直接获胜
if np.linalg.norm(city - network[winner_idx, :],
axis=1) < 0.005: # 求距离
network[winner_idx, :] = city
# print(winner_idx)
else:
gaussian = get_neighborhood(winner_idx, n // 10,
network.shape[0]) # 高斯核函数是算法的核心
network += gaussian[:, np.newaxis] * learning_rate * (
city - network) # np.newaxis在该位置增加一维，变成神经元数*1维
# 实际上就是为了让对应的移动乘以对应的坐标
# Decay the variables
learning_rate = learning_rate * dalpha
n = n * dsigma # 较好的参数是0.9991-0.9997
if not i % 100: # 每隔100次， 求距离
route = get_route(cities, network)
p = problem.reindex(route)
dSave[i // dStep] = route_distance(p)
# # Check if any parameter has completely decayed.
# if n < 1:
# print('Radius has completely decayed, finishing execution',
# 'at {} iterations'.format(i))
# break
# if learning_rate < 0.001:
# print('Learning rate has completely decayed, finishing execution',
# 'at {} iterations'.format(i))
# break
else:
print('Completed {} iterations.'.format(iterations))
route = get_route(cities, network)
return route, dSave
def get_route(cities, network):
"""
返回tsp路线
"""
# 找出距离每个城市最近的神经元
cities['winner'] = cities[['x', 'y'
]].apply(lambda c: select_closest(network, c),
axis=1,
raw=True)
# 返回神经元按一定顺序排序的数列
return cities.sort_values('winner').index
def get_route(cities, network):
"""
cities: [DataFrame] the normalized set of cities ['city', 'y', 'x']\n
network: [numpy.ndarray] the trained network with 8*cities neuron\n
return: [Index] the index of cities\n
如果有多个城市被分配到了同一个神经元，那么按照下面的代码排序的时候，这几个会按照之前的顺序（所以这里不是最优）\n
Return the route computed by a network.\n
"""
cities['winner'] = cities[['x', 'y']].apply(
lambda c: select_closest(network, c), # 计算结果存在新 col winner 里
axis=1, # 按行 index 迭代
raw=True # the passed function will receive ndarray objects.
)
# 先按照'winner'排序（神经元顺序），然后返回index，即对应的城市顺序
return cities.sort_values('winner').index
def som(problem, iterations, learning_rate=0.8):
"""Solve the TSP using a Self-Organizing Map."""
# Obtain the normalized set of cities (w/ coord in [0,1])
cities = problem.copy()
cities[['x', 'y']] = normalize(cities[['x', 'y']])
# The population size is 8 times the number of cities 神经元个数
n = cities.shape[0] * 8
network = generate_network(n) # Generate an adequate network of neurons:
print('Network of {} neurons created. Starting the iterations:'.format(n))
for i in range(iterations):
if i == 0 or (not i % 1000):
plot_network(cities,
network,
name='out_files\process\city_network%d.png' %
(i // 1000 + 1))
city = cities.sample(1)[['x', 'y']].values # 随机从cities中选取一组数，1*2数组
winner_idx = select_closest(network, city)
# Generate a filter that applies changes to the winner's gaussian
gaussian = get_neighborhood(winner_idx, n // 10, network.shape[0])
# Update the network's weights (closer to the city)
network += gaussian[:, np.newaxis] * learning_rate * (
city - network) # np.newaxis在该位置增加一维，变成神经元数*1维
# 实际上就是为了让对应的移动乘以对应的坐标
# Decay the variables
learning_rate = learning_rate * 0.99997
n = n * 0.9997
# Check for plotting interval
# Check if any parameter has completely decayed.
if n < 1:
print('Radius has completely decayed, finishing execution',
'at {} iterations'.format(i))
break
if learning_rate < 0.001:
print('Learning rate has completely decayed, finishing execution',
'at {} iterations'.format(i))
break
# if 神经元覆盖了城市
# print('在第{}次迭代，收敛'.format(i))
else:
print('Completed {} iterations.'.format(iterations))
plot_network(cities, network)
route = get_route(cities, network)
plot_route(problem[['x', 'y']], route)
return route
def som(problem, iterations, learning_rate=0.8):
cities = problem.copy()
cities[['x', 'y']] = normalize(cities[['x', 'y']])
n = cities.shape[0] * 8
network = generate_network(n)
print('Network of {} neurons created. Starting the iterations:'.format(n))
for i in range(iterations):
if not i % 100:
print('\t> Iteration {}/{}'.format(i, iterations), end="\r")
city = cities.sample(1)[['x', 'y']].values
winner_idx = select_closest(network, city)
gaussian = get_neighborhood(winner_idx, n // 10, network.shape[0])
network += gaussian[:, np.newaxis] * learning_rate * (city - network)
learning_rate = learning_rate * 0.99997
n = n * 0.9997
if not i % 1000:
plot_network(cities, network, name='diagrams/{:05d}.png'.format(i))
if n < 1:
print('Radius has completely decayed, finishing execution',
'at {} iterations'.format(i))
break
if learning_rate < 0.001:
print('Learning rate has completely decayed, finishing execution',
'at {} iterations'.format(i))
break
else:
print('Completed {} iterations.'.format(iterations))
plot_network(cities, network, name='diagrams/final.png')
route = get_route(cities, network)
plot_route(cities, route, 'diagrams/route.png')
return route
def som(target,
iterations,
learning_rate=0.8,
obstacle=None,
fbzs=None,
data_path="assets/"):
"""
target: [DataFrame] ['city', 'y', 'x']
iterations: [int] the max iteration times
learning rate: [float] the original learning rate, will decay
obstacle: [DataFrame] ['obs' 'y' 'x']
data_path: [str] 迭代过程中以文件形式保存的数据的路径
return: [index] route
Solve the TSP using a Self-Organizing Map.
"""
# Obtain the normalized set of cities (w/ coord in [0,1])
# copy one so the later process won't influence the original data
cities = target.copy()[['x', 'y']]
obs = obstacle.copy()[['x', 'y']] if obstacle is not None else None
norm_ans = normalization(fbzs, cities, obs)
cities, obs, span, fbzs = norm_ans["result"][0], norm_ans["result"][
1], norm_ans["dif"], norm_ans["fbzs"]
obs = obs[['x', 'y']].to_numpy()
targets = cities[['x', 'y']].to_numpy()
# The population size is 8 times the number of cities
n = targets.shape[0] * 8 # 这里是神经元数目，别误解为人口(population)数目
n = n + obs.shape[0] * 2 if obstacle is not None else n
n = n + len(fbzs) * 2 if obstacle is not None else n
# parameters set to observe and evaluate 自己加的
axes = update_figure()
old_delta, old_network = [], 0 # 用来判断变化大小的收敛变量
gate = 1 / span # 收敛条件设定，精度的映射
obs_size = 4 * gate
# Generate an adequate network of neurons:
network = generate_network(n) # 2列矩阵
logging.info('Network of %s neurons created. Starting iterations:', n)
for i in range(iterations):
if not i % 100:
# "\r"回车，将光标移到本行开头，大概就是覆盖了吧
print('\t> Iteration {}/{}'.format(i, iterations), end="\r")
route_dir_vec = get_route_vector(network, d=0, t=1) # 当前路径下的顺时针,出发方向向量
# Choose a random city
# DataFrame.values --> numpy.ndarray
# city = cities.sample(1)[['x', 'y']].values
city = random.choice(targets) # 随机选取一个目标
winner_idx = select_closest(network, city)
gaussian = get_neighborhood(winner_idx, n // 10, network.shape[0])
city_delta = gaussian[:, np.newaxis] * (city - network)
network += learning_rate * city_delta
# choose a random obstacle
# if obs is None:
# obs_delta = 0
# else:
# # obs_influence = ver_vec(np.roll(route_dir_vec, 1, axis=0),
# # get_ob_influences(network, obs, obs_size))
# obs_delta = get_ob_influences(network, obs, obs_size)
# network += learning_rate * obs_delta
# adjust the forbidden area
# if fbzs is not None:
# fbzs_delta = np.zeros(network.shape)
# for fbz in fbzs:
# for index, node in enumerate(network):
# if is_point_in_polygon(node, fbz) == 1:
# ver_dist_v = ver_vec(get_route_vector(fbz), fbz - node)
# # 计算 node to 边界的距离并找到最小值的位置
# ver_dist = np.linalg.norm(ver_dist_v, axis=1)
# closest = ver_dist.argmin()
# # update delta
# fbzs_delta[index] += ver_dist_v[closest]
# # 这里可以添加安全距离 / ver_dist[closest] * 1
# a = np.linalg.norm(fbzs_delta, axis=1)
# fbzs_delta = ver_vec(np.roll(route_dir_vec, 1, axis=0),
# fbzs_delta) # 垂直方向影响
# b = np.linalg.norm(fbzs_delta, axis=1)
# fbzs_delta[b != 0] *= (a[b != 0] / b[b != 0])[:, np.newaxis]
# network += fbzs_delta
# Update the network's weights (closer to the city)
# delta = city_delta + obs_delta
# network += learning_rate * delta
# 修正结点分布,使之间隔更加均匀
# network = sepaprate_node(network)
winner_indices = np.apply_along_axis(
func1d=lambda t: select_closest(network, t),
axis=1,
arr=targets,
) # 胜者不改变
network = sep_and_close_nodes(
network,
decay=learning_rate,
targets=targets,
obstacle=obs, # 圆形障碍物
obs_size=obs_size, # 障碍物半径
fbzs=fbzs, # 不规则障碍物
gate=gate, # 最大更新步长
winner_indices=winner_indices,
)
# Decay the variables
# 学习率更新 对应了 e^{-t/t0} t0=33332.83
learning_rate = learning_rate * 0.99997
# 高斯函数邻域更新 对应了σ=σ0*e^{-t/t0}, σ0=n//10 t0=3332.83
n = n * 0.9997
# Check for plotting interval
if not i % 200:
plot_network(
targets,
network,
name=data_path + '{:05d}.png'.format(i),
axes=axes,
obstacle=obs,
obs_size=obs_size,
span=span,
fbzs=fbzs,
)
update_figure(axes, clean=True)
# Check if any parameter has completely decayed. 收敛判断
if n < 1:
finish_info = 'Radius has completely decayed.'
break
if learning_rate < 0.001:
finish_info = 'Learning rate has completely decayed.'
break
delta = network - old_network if old_network is not None else network
max_delta = np.linalg.norm(delta, axis=1).max() # 计算变化的模长 (n,1) array
old_delta.append(max_delta)
old_network = network.copy()
if len(old_delta) > network.shape[0]: # 存储神经元结点数目的delta,避免概率影响收敛
old_delta.pop(0)
if max(old_delta) < gate:
# 当迭代变化最大值还小于设定的精度时就停止
finish_info = "Average movement has reduced to {},".format(
np.mean(old_delta) * span)
finish_info += "max movement {},".format(np.max(old_delta) * span)
break
# 训练完成后进行的工作
finish_info += "finishing execution at {} iterations".format(i)
logging.info(finish_info)
# 保存路径图片
plot_network(
targets,
network,
name=data_path + 'final.png',
obstacle=obs,
obs_size=obs_size,
span=span,
fbzs=fbzs,
)
# 计算路径距离
distance = route_distance(network) * span # 恢复到原坐标系下的距离
logging.info('Route found of length %s', distance)
return distance
def multi_som(target,
iterations,
learning_rate=0.8,
obstacle=None,
fbzs=None,
data_path="assets/"):
# 读取数据并进行归一化处理
logging.info('multi_som loading data')
targets = target.copy()[['x', 'y']]
obs = obstacle.copy()[['x', 'y']] if obstacle is not None else None
norm_ans = normalization(fbzs, targets, obs)
targets, obs, span, fbzs = norm_ans["result"][0], norm_ans["result"][
1], norm_ans["dif"], norm_ans["fbzs"]
# 将数据统一转化为ndarray类型
obs = obs[['x', 'y']].to_numpy()
targets = targets[['x', 'y']].to_numpy()
# 一些便于程序运行的设定
axes = update_figure() # set up a figure
old_delta = []
gate = 1 / span # 收敛条件设定，精度的映射
obs_size = 4 * gate
net_size = 15
# 聚类划分环
k = 2
labels = cluster(targets, n=k, fbzs=fbzs)
Network_group = [] # 按照聚类结果创建的Network
for i in range(k):
sub_targets = targets[labels == i]
num = sub_targets.shape[0] * net_size
radius = num
sub_network = generate_network(num)
Network_group.append(Network(sub_network, num, sub_targets, radius))
logging.info('%s network created', len(Network_group))
logging.info('Starting iterations:')
for i in range(iterations):
if not i % 100:
print('\t> Iteration {}/{}'.format(i, iterations), end="\r")
for net in Network_group:
# 常规SOM
target = random.choice(net.targets)
winner_idx = select_closest(net.network, target)
gaussian = get_neighborhood(winner_idx, net.radius // 10, net.num)
target_delta = gaussian[:, np.newaxis] * (target - net.network)
net.network += learning_rate * target_delta
# 选取最接近目标点的获胜结点,其他结点往距离最小处移动
winner_indices = np.apply_along_axis(
func1d=lambda t: select_closest(net.network, t),
axis=1,
arr=net.targets,
) # 胜者不改变
net.network = sep_and_close_nodes(
net.network,
decay=learning_rate,
targets=net.targets,
obstacle=obs, # 圆形障碍物
obs_size=obs_size, # 障碍物半径
fbzs=fbzs, # 不规则障碍物
gate=gate, # 最大更新步长
winner_indices=winner_indices,
)
# Decay the variables
learning_rate = learning_rate * 0.99997
for net in Network_group:
net.radius *= 0.9997
# Check for plotting interval
if not i % 200:
plot_network(
targets,
neurons=None,
name=data_path + '{:05d}.png'.format(i),
axes=axes,
obstacle=obs,
obs_size=obs_size,
span=span,
fbzs=fbzs,
Networks=Network_group,
)
update_figure(axes, clean=True)
# Check if any parameter has completely decayed. 收敛判断
if max([net.radius for net in Network_group]) < 1:
finish_info = 'Radius has completely decayed.'
break
if learning_rate < 0.001:
finish_info = 'Learning rate has completely decayed.'
break
for net in Network_group:
old_delta.append(net.get_delta())
if len(old_delta) > net_size * targets.shape[0]: # 避免概率影响收敛
old_delta.pop(0)
if max(old_delta) < gate:
# 当迭代变化最大值还小于设定的精度时就停止
finish_info = "Max movement has reduced to {},".format(
max(old_delta) * span)
break
# 训练完成后进行的工作
finish_info += "finishing execution at {} iterations".format(i)
logging.info(finish_info)
# 保存路径图片
plot_network(
targets,
neurons=None,
name=data_path + 'final.png',
obstacle=obs,
obs_size=obs_size,
span=span,
fbzs=fbzs,
Networks=Network_group,
)
# 计算路径距离
distance = 0
for net in Network_group:
distance += route_distance(net.network) * span # 恢复到原坐标系下的距离
logging.info('Route found of length %s', distance)
return distance
def som(problem, iterations, learning_rate=0.8):
'''
SOM解决TSP问题
'''
# 将城市数据归一化处理
cities = problem.copy()
cities[['x', 'y']] = normalize(cities[['x', 'y']])
# 神经元数量设定为城市的8倍
n = cities.shape[0] * 8
# 建立神经网络
network = generate_network(n)
print('创建{} 个神经元. 开始进行迭代:'.format(n))
for i in range(iterations):
if not i % 100:
print('\t> 迭代过程 {}/{}'.format(i, iterations), end='\r')
# 随机选择一个城市
city = cities.sample(1)[['x', 'y']].values
#优胜神经元（距离该城市最近的神经元）
winner_idx = select_closest(network, city)
# 以该神经元为中心建立高斯分布
gaussian = get_neighborhood(winner_idx, n // 10, network.shape[0])
# 更新神经元的权值，使神经元向被选中城市移动
network += gaussian[:, np.newaxis] * learning_rate * (city - network)
# 学习率衰减，方差衰减
learning_rate = learning_rate * 0.99997
n = n * 0.9997
# 每迭代1000次时作图
if not i % 1000:
plot_network(cities,
network,
name='som_diagrams/{:05d}.png'.format(i))
pass
# 判断方差和学习率是否达到阈值
if n < 1:
print('方差已经达到阈值，完成执行次数{}'.format(i))
break
if learning_rate < 0.001:
print('学习率已经达到阈值，完成执行次数{}'.format(i))
break
else:
print('完成迭代：{}次'.format(iterations))
plot_network(cities, network, name='som_diagrams/final.png')
route = get_route(cities, network)
cities = cities.reindex(route)
plot_route(cities, route, 'som_diagrams/route.png')
# 将多个png文件合成gif文件
path = os.chdir('.\som_diagrams')
pic_list = os.listdir()
create_gif(pic_list, 'result.gif', 0.3)
return route
def SOM(args):
"""Solve the TSP using a Self-Organizing Map."""
# Obtain the normalized set of cities (w/ coord in [0,1])
iteration = args.iteration
learning_rate = args.learning_rate
decay = args.decay
out_dir = Path(args.out_dir)
out_dir.mkdir_p()
cities = pd.read_csv(Path(args.data_dir)/'data1.csv')
cities.to_csv(out_dir/'cities.csv')
cities_nm = cities.copy()
cities_nm[['x', 'y']] = normalize(cities_nm[['x', 'y']])
cities_nm.to_csv(out_dir/'cities_nm.csv')
# The population size is 8 times the number of cities
n = cities_nm.shape[0] * 8
# Generate an adequate network of neurons:
neuron_chain = init_neurons(n)
print('--> Network of {} neurons created. Starting the iterations:'.format(n))
best_route=np.array([0])
best_id=0
min_loss=0
losses={}
losses_decay = {}
for i in tqdm(range(iteration)):
# Choose a random city
city = cities_nm.sample(1)[['x', 'y']].values#随机抽样 random sampling
winner_idx = select_closest(neuron_chain, city)
# Generate a filter that applies changes to the winner's gaussian
gaussian = get_neighborhood(center=winner_idx, radix=n//10, domain=neuron_chain.shape[0])
# Update the network's weights (closer to the city)
neuron_chain += gaussian[:,np.newaxis] * learning_rate * (city - neuron_chain)
# Decay the variables
learning_rate = learning_rate * decay
n = n * decay
if i % args.evaluate_freq==0:
route = get_route(cities_nm, neuron_chain)
cities_od = cities.reindex(route)
loss = route_distance(cities_od)
losses[i] = loss
if min_loss==0 or min_loss > loss:
min_loss=loss
best_route = list(route.astype(np.float64))
best_id = i
losses_decay[i] = loss
cities_od.to_csv(out_dir / 'route_{:04d}.csv'.format(i))
save_neuron_chain(neuron_chain, out_dir / "neuron_chain_{:04d}.npy".format(i))
#end for
# Check if any parameter has completely decayed.
if n < 1:
print('Radius has completely decayed, finishing execution',
'at {} iterations'.format(i))
break
if learning_rate < 0.001:
print('Learning rate has completely decayed, finishing execution',
'at {} iterations'.format(i))
break
print('Completed {} iterations.'.format(iteration))
results={}
results['min_loss'] = min_loss
results['best_id'] = best_id
results['best_route'] = best_route
results['losses_decay'] = losses_decay
results['losses'] = losses
p = Path(out_dir / 'results.json')
with open(p, 'w') as fp:
json.dump(results, fp)
print('ok')
return results
