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()
dSave = np.zeros(iterations // 100) # 保存数据
cities[['x', 'y']] = normalize(cities[['x', 'y']]) # 单位化
# 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(n)
print('Network of {} neurons created. Starting the iterations:'.format(n))
for i in range(iterations):
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])
# 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
if not i % 100: # 每隔1000次画出神经元图像, 求距离
plot_network(cities,
network,
name='out_files\process\city_network%d.png' % (i))
route = get_route(cities, network)
p = problem.reindex(route)
dSave[i // 100] = 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))
plot_network(cities, network)
route = get_route(cities, network)
plot_route(cities, route)
return route, dSave
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 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 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 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 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(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
