if iter < 2000 path.pos(1).x = x_G; path.pos(1).y = y_G; path.pos(2).x = T.v(end).x; path.pos(2).y = T.v(end).y; pathIndex = T.v(end).indPrev; % 终点加入路径 j=0; while 1 path.pos(j+3).x = T.v(pathIndex).x; path.pos(j+3).y = T.v(pathIndex).y; pathIndex = T.v(pathIndex).indPrev; if pathIndex == 1 break end j=j+1; end % 沿终点回溯到起点 path.pos(end+1).x = x_I; path.pos(end).y = y_I; % 起点加入路径 for j = 2:length(path.pos) plot([path.pos(j).x; path.pos(j-1).x;], [path.pos(j).y; path.pos(j-1).y], 'g', 'Linewidth', 4); end else disp('Error, no path found!'); end

这段代码是用于在RRT路径规划中绘制路径的。如果迭代次数iter小于2000，则路径被找到，可以将其绘制出来。path.pos是一个结构体数组，存储了路径上的所有点的x和y坐标。在这段代码中，起点和终点分别被添加到路径的第一个和最后一个元素。然后，通过while循环，从终点开始向回遍历树结构，将路径上所有的点添加到path.pos中。最后，使用for循环将路径上相邻的点连接起来，并用绿色线条绘制出来。如果迭代次数iter超过2000，则认为路径未找到，输出错误信息。

相关问题

import matplotlib.pyplot as plt import numpy as np import pandas as pd import seaborn as sns import copy import math import random import time from multiprocessing import Pool as ThreadPool path1='att48.tsp' path2='eil76.tsp' path3='pcb442.tsp' path4='rd100.tsp' path5='tsp225.tsp' def readcity(path): df = pd.read_csv("C:\\文件\\现代优化算法\\TSP训练数据集\\"+path, sep=" ", skiprows=6, header=None) return df df = readcity(path4) city = np.array(range(1,len(df[0][0:len(df)-1])+1)) city_x = np.array(df[1][0:len(df)-1]) city_y = np.array(df[2][0:len(df)-1]) city_pos = np.stack((city_x, city_y), axis=1) def distance(city1, city2): return math.sqrt((city1[0]-city2[0])**2 + (city1[1]-city2[1])**2) def path_length(path): length = 0 for i in range(len(path)-1): length += distance(city_pos[path[i]-1], city_pos[path[i+1]-1]) length += distance(city_pos[path[-1]-1], city_pos[path[0]-1]) return length def initial_solution(): unvisited_cities = list(range(1, len(city)+1)) current_city = random.choice(unvisited_cities) solution = [current_city] unvisited_cities.remove(current_city) while unvisited_cities: next_city = min(unvisited_cities, key=lambda city: distance(city_pos[current_city-1], city_pos[city-1])) unvisited_cities.remove(next_city) solution.append(next_city) current_city = next_city return solution def two_opt_swap(path, i, k): new_path = path[:i] + path[i:k + 1][::-1] + path[k + 1:] return new_path 请以上述代码为开头，输出一段以模拟退火算法解决tsp问题的代码，输入为.tsp文件，要求实现用2-opt法构造邻域、在内循环中用Metropolis准则接受解、用最近邻居构造启发式贪心算法构造初始解、输出初始解和解值、最优解和解值、迭代次数和迭代过程的功能

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import copy
import math
import random
import time
from multiprocessing import Pool as ThreadPool

path = 'rd100.tsp'

def readcity(path):
df = pd.read_csv("C:\\文件\\现代优化算法\\TSP训练数据集\\"+path, sep=" ", skiprows=6, header=None)
return df

df = readcity(path)

city = np.array(range(1,len(df[0][0:len(df)-1])+1))
city_x = np.array(df[1][0:len(df)-1])
city_y = np.array(df[2][0:len(df)-1])
city_pos = np.stack((city_x, city_y), axis=1)

def distance(city1, city2):
return math.sqrt((city1[0]-city2[0])**2 + (city1[1]-city2[1])**2)

def path_length(path):
length = 0
for i in range(len(path)-1):
length += distance(city_pos[path[i]-1], city_pos[path[i+1]-1])
length += distance(city_pos[path[-1]-1], city_pos[path[0]-1])
return length

def initial_solution():
unvisited_cities = list(range(1, len(city)+1))
current_city = random.choice(unvisited_cities)
solution = [current_city]
unvisited_cities.remove(current_city)
while unvisited_cities:
next_city = min(unvisited_cities, key=lambda city: distance(city_pos[current_city-1], city_pos[city-1]))
unvisited_cities.remove(next_city)
solution.append(next_city)
current_city = next_city
return solution

def two_opt_swap(path, i, k):
new_path = path[:i] + path[i:k + 1][::-1] + path[k + 1:]
return new_path

def simulated_annealing(path):
df = readcity(path)

city = np.array(range(1,len(df[0][0:len(df)-1])+1))
city_x = np.array(df[1][0:len(df)-1])
city_y = np.array(df[2][0:len(df)-1])
city_pos = np.stack((city_x, city_y), axis=1)

T = 1000 # 初始温度
Tmin = 1 # 终止温度
alpha = 0.95 # 降温速率
path = initial_solution() # 初始化路径
best_path = copy.deepcopy(path) # 最优路径
best_length = path_length(best_path) # 最优路径长度
length_list = [best_length] # 记录每个温度下的路径长度
iter_num = 0 # 迭代次数

while T > Tmin:
i = random.randint(0, len(city)-1)
k = random.randint(0, len(city)-1)
new_path = two_opt_swap(path, i, k) # 产生新路径
new_length = path_length(new_path) # 计算新路径长度
delta = new_length - best_length # 计算路径长度差

if delta < 0:
path = copy.deepcopy(new_path)
best_path = copy.deepcopy(new_path)
best_length = new_length
else:
p = math.exp(-delta/T) # 计算概率
if random.random() < p: # 根据概率接受新解
path = copy.deepcopy(new_path)

length_list.append(best_length)
T *= alpha # 降温

iter_num += 1

print("初始解路径：", initial_solution())
print("初始解路径长度：", path_length(initial_solution()))
print("最优解路径：", best_path)
print("最优解路径长度：", best_length)
print("迭代次数：", iter_num)
plt.plot(range(len(length_list)), length_list)
plt.title("Simulated Annealing")
plt.xlabel("Iteration")
plt.ylabel("Length")
plt.show()

simulated_annealing(path)

用Python代码实现基于RNA序列数据集实验，数据集在model文件夹中： ALKBH5_Baltz2012.train.negatives.fa 是训练集负样本； ALKBH5_Baltz2012.train.positives.fa 是训练集正样本； ALKBH5_Baltz2012.val.negatives.fa 是验证集负样本； ALKBH5_Baltz2012.val.positives.fa 是验证集正样本。 用Pytorch框架搭建卷积神经网络，输出最终分类正确率。

好的，这是一个比较复杂的问题，我会尽力为您解答。首先，您需要安装Pytorch框架以及相关依赖项。接下来，您可以按照以下步骤进行操作：

1.导入必要的库和模块

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
from sklearn.metrics import accuracy_score

2.定义数据读取器和预处理模块

class RNADataLoader:
    def __init__(self, file_path):
        self.file_path = file_path

    def __iter__(self):
        with open(self.file_path, 'r') as f:
            lines = f.readlines()
            for i in range(0, len(lines), 2):
                yield lines[i + 1].strip()

class RNAPreprocessor:
    def __init__(self, l=101):
        self.l = l
        self.dict = {'A': 0, 'C': 1, 'G': 2, 'T': 3}

    def __call__(self, x):
        x = x.upper()
        x = x.replace('U', 'T')
        x = x.replace('N', 'A')
        x = x.replace('R', 'A')
        x = x.replace('Y', 'T')
        x = x.replace('S', 'C')
        x = x.replace('W', 'A')
        x = x.replace('K', 'G')
        x = x.replace('M', 'A')
        x = x.replace('B', 'C')
        x = x.replace('D', 'A')
        x = x.replace('H', 'A')
        x = x.replace('V', 'A')
        x = [self.dict[c] for c in x]
        if len(x) < self.l:
            x = x + [0] * (self.l - len(x))
        else:
            x = x[:self.l]
        return np.array(x)

3.定义模型

class ConvNet(nn.Module):
    def __init__(self):
        super(ConvNet, self).__init__()
        self.conv1 = nn.Conv1d(4, 16, kernel_size=5, padding=2)
        self.conv2 = nn.Conv1d(16, 32, kernel_size=5, padding=2)
        self.conv3 = nn.Conv1d(32, 64, kernel_size=5, padding=2)
        self.pool = nn.MaxPool1d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(64 * 13, 128)
        self.fc2 = nn.Linear(128, 1)

    def forward(self, x):
        x = x.transpose(1, 2)
        x = self.pool(torch.relu(self.conv1(x)))
        x = self.pool(torch.relu(self.conv2(x)))
        x = self.pool(torch.relu(self.conv3(x)))
        x = x.view(-1, 64 * 13)
        x = torch.relu(self.fc1(x))
        x = torch.sigmoid(self.fc2(x))
        return x

4.定义训练和验证函数

def train(model, train_loader, val_loader, criterion, optimizer, device, epochs):
    for epoch in range(epochs):
        model.train()
        train_loss = 0.0
        train_acc = 0.0
        for x, y in train_loader:
            x, y = x.to(device), y.to(device)
            optimizer.zero_grad()
            outputs = model(x.float())
            loss = criterion(outputs, y.float())
            loss.backward()
            optimizer.step()
            train_loss += loss.item()
            train_acc += accuracy_score(y.cpu().numpy(), (outputs.cpu().numpy() > 0.5).astype(int))
        train_loss /= len(train_loader)
        train_acc /= len(train_loader)

        model.eval()
        val_loss = 0.0
        val_acc = 0.0
        for x, y in val_loader:
            x, y = x.to(device), y.to(device)
            outputs = model(x.float())
            loss = criterion(outputs, y.float())
            val_loss += loss.item()
            val_acc += accuracy_score(y.cpu().numpy(), (outputs.cpu().numpy() > 0.5).astype(int))
        val_loss /= len(val_loader)
        val_acc /= len(val_loader)

        print('Epoch [{}/{}], Train Loss: {:.4f}, Train Acc: {:.4f}, Val Loss: {:.4f}, Val Acc: {:.4f}'
              .format(epoch + 1, epochs, train_loss, train_acc, val_loss, val_acc))

5.训练模型并输出最终分类正确率

train_neg = RNADataLoader('model/ALKBH5_Baltz2012.train.negatives.fa')
train_pos = RNADataLoader('model/ALKBH5_Baltz2012.train.positives.fa')
val_neg = RNADataLoader('model/ALKBH5_Baltz2012.val.negatives.fa')
val_pos = RNADataLoader('model/ALKBH5_Baltz2012.val.positives.fa')
preprocessor = RNAPreprocessor()
train_data = [(preprocessor(x), 0) for x in train_neg] + [(preprocessor(x), 1) for x in train_pos]
val_data = [(preprocessor(x), 0) for x in val_neg] + [(preprocessor(x), 1) for x in val_pos]
train_loader = torch.utils.data.DataLoader(train_data, batch_size=128, shuffle=True)
val_loader = torch.utils.data.DataLoader(val_data, batch_size=128, shuffle=False)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = ConvNet().to(device)
criterion = nn.BCELoss()
optimizer = optim.Adam(model.parameters())
train(model, train_loader, val_loader, criterion, optimizer, device, epochs=10)
model.eval()
test_neg = RNADataLoader('model/ALKBH5_Baltz2012.val.negatives.fa')
test_pos = RNADataLoader('model/ALKBH5_Baltz2012.val.positives.fa')
test_data = [(preprocessor(x), 0) for x in test_neg] + [(preprocessor(x), 1) for x in test_pos]
test_loader = torch.utils.data.DataLoader(test_data, batch_size=128, shuffle=False)
test_acc = 0.0
for x, y in test_loader:
    x, y = x.to(device), y.to(device)
    outputs = model(x.float())
    test_acc += accuracy_score(y.cpu().numpy(), (outputs.cpu().numpy() > 0.5).astype(int))
test_acc /= len(test_loader)
print('Test Accuracy: {:.4f}'.format(test_acc))

以上代码中，我们使用了卷积神经网络(ConvNet)模型，并使用BCELoss作为损失函数，使用Adam作为优化器。在训练过程中，我们使用了10个epoch，训练结束后，我们使用验证集计算了模型的分类正确率。最后，我们使用测试集计算了模型的最终分类正确率。