1053. Path of Equal Weight (30)

Given a non-empty tree with root R, and with weight Wi assigned to each tree node Ti.  The weight of a path from R to L is defined to be the sum of the weights of all the nodes along the path from R to any leaf node L.

Now given any weighted tree, you are supposed to find all the paths with their weights equal to a given number.  For example, let's consider the tree showed in Figure 1: for each node, the upper number is the node ID which is a two-digit number, and the lower number is the weight of that node.  Suppose that the given number is 24, then there exists 4 different paths which have the same given weight: {10 5 2 7}, {10 4 10}, {10 3 3 6 2} and {10 3 3 6 2}, which correspond to the red edges in Figure 1.


Figure 1

Input Specification:

Each input file contains one test case.  Each case starts with a line containing 0 < N <= 100, the number of nodes in a tree, M (< N), the number of non-leaf nodes, and 0 < S < 230, the given weight number. The next line contains N positive numbers where Wi (<1000) corresponds to the tree node Ti.  Then M lines follow, each in the format:

ID K ID[1] ID[2] ... ID[K]

where ID is a two-digit number representing a given non-leaf node, K is the number of its children, followed by a sequence of two-digit ID's of its children. For the sake of simplicity, let us fix the root ID to be 00.

Output Specification:

For each test case, print all the paths with weight S in non-increasing order.  Each path occupies a line with printed weights from the root to the leaf in order.  All the numbers must be separated by a space with no extra space at the end of the line.

Note: sequence {A1, A2, ..., An} is said to be greater than sequence {B1, B2, ..., Bm} if there exists 1 <= k < min{n, m} such that Ai = Bi for i=1, ... k, and Ak+1 > Bk+1.

Sample Input:
20 9 24
10 2 4 3 5 10 2 18 9 7 2 2 1 3 12 1 8 6 2 2
00 4 01 02 03 04
02 1 05
04 2 06 07
03 3 11 12 13
06 1 09
07 2 08 10
16 1 15
13 3 14 16 17
17 2 18 19
Sample Output:
10 5 2 7
10 4 10
10 3 3 6 2
10 3 3 6 2

 思路:很具代表性的一道图的遍历的题目,用DFS和邻接链表的数据结构,基本没有什么坑点,但是题目要读仔细,特别是加粗的那几个字

AC参考代码:

#include <iostream>
#include <vector>
#include <fstream>
#include <algorithm>
using namespace std;

int N,M,S;
vector<int> node;
vector<vector<int> > edge(101);//edge中储存的是索引 索引再根据node找到对应的值
vector<vector<int> >result;//把符合条件的路径节点作为vector数组储存起来
vector<int> isVisited(101);//标记节点是否被遍历
vector<int> currentNode;//储存当前路径上的节点
int sum = 0;//路径权重和

int compare(vector<int> a,vector<int> b){//按照字典逆序排序
    int minN = a.size()<b.size()?a.size():b.size();
    for(int i=0;i<minN;i++){
        if(a[i]!=b[i]){
            return a[i]>b[i];
        }
    }
    return 0;
}
void DFS(int i){    //遍历并寻找满足条件的路径并记录节点
    isVisited[i] = 1;
    currentNode.push_back(node[i]);
    sum += node[i];
    if(edge[i].size()==0){//到达叶子节点
        if(sum==S){ //这条路径符合要求
            result.push_back(currentNode);
        }
    }else{
        for(int j=0;j<edge[i].size();j++){
            DFS(edge[i][j]);
        }
    }
    sum -= node[i];     //遍历完叶子节点后返回需要修改当前路径节点和节点和
    currentNode.pop_back();
}
int main()
{
    //ifstream cin("1.txt");
    cin>>N>>M>>S;
    node.resize(N);
    for(int i=0;i<N;i++){
        cin>>node[i];
    }
    for(int i=0;i<M;i++){
        int index,arrSize;
        cin>>index>>arrSize;
        for(int j=0;j<arrSize;j++){
            int temp;
            cin>>temp;
            edge[index].push_back(temp);
        }
    }
    DFS(0);
    sort(result.begin(),result.end(),compare);//按字典逆序排序
    for(int i=0;i<result.size();i++){
        for(int j=0;j<result[i].size();j++){
            j<result[i].size()-1?cout<<result[i][j]<<" ":cout<<result[i][j]<<endl;
        }
    }
    return 0;
}


 

  

  
         使用高级编辑
这是对单个文件进行预测“import os import json import torch from PIL import Image from torchvision import transforms import matplotlib.pyplot as plt from model import convnext_tiny as create_model def main(): device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") print(f"using {device} device.") num_classes = 5 img_size = 224 data_transform = transforms.Compose( [transforms.Resize(int(img_size * 1.14)), transforms.CenterCrop(img_size), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]) # load image img_path = "../tulip.jpg" assert os.path.exists(img_path), "file: '{}' dose not exist.".format(img_path) img = Image.open(img_path) plt.imshow(img) # [N, C, H, W] img = data_transform(img) # expand batch dimension img = torch.unsqueeze(img, dim=0) # read class_indict json_path = './class_indices.json' assert os.path.exists(json_path), "file: '{}' dose not exist.".format(json_path) with open(json_path, "r") as f: class_indict = json.load(f) # create model model = create_model(num_classes=num_classes).to(device) # load model weights model_weight_path = "./weights/best_model.pth" model.load_state_dict(torch.load(model_weight_path, map_location=device)) model.eval() with torch.no_grad(): # predict class output = torch.squeeze(model(img.to(device))).cpu() predict = torch.softmax(output, dim=0) predict_cla = torch.argmax(predict).numpy() print_res = "class: {} prob: {:.3}".format(class_indict[str(predict_cla)], predict[predict_cla].numpy()) plt.title(print_res) for i in range(len(predict)): print("class: {:10} prob: {:.3}".format(class_indict[str(i)], predict[i].numpy())) plt.show() if name == 'main': main()”,改为对指定文件夹下的左右文件进行预测,并绘制混淆矩阵,
06-11
好的,为了实现您的需求,您需要进行以下几个步骤: 1. 加载模型和类别标签: 您需要在代码中加载您的模型和类别标签。您可以使用之前的代码中的方式来加载它们,只需要做一些小的改动即可。例如,您需要将模型和类别标签的路径作为参数传递给您的函数。 2. 加载测试集: 您需要加载您的测试集。您可以使用 `torchvision.datasets.ImageFolder` 来加载测试集。这个函数会将每个文件夹中的所有图像文件都加载到一个 tensor 中,并自动为每个文件夹分配一个标签。 3. 进行预测: 您需要对测试集中的每个图像进行预测,并将预测结果与真实标签进行比较。您可以使用之前的代码中的方式来预测每个图像,只需要做一些小的改动即可。例如,您需要将预测结果保存到一个列表中,并将真实标签保存到另一个列表中。 4. 绘制混淆矩阵: 最后,您需要使用预测结果和真实标签来绘制混淆矩阵。您可以使用 `sklearn.metrics.confusion_matrix` 来计算混淆矩阵,并使用 `matplotlib` 来绘制它。 下面是修改后的代码示例: ``` import os import json import torch from PIL import Image from torchvision import transforms import matplotlib.pyplot as plt from sklearn.metrics import confusion_matrix import numpy as np from model import convnext_tiny as create_model def predict_folder(model_path, json_path, folder_path): device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") print(f"using {device} device.") num_classes = 5 img_size = 224 data_transform = transforms.Compose([ transforms.Resize(int(img_size * 1.14)), transforms.CenterCrop(img_size), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]) # load class_indict json with open(json_path, "r") as f: class_indict = json.load(f) # create model model = create_model(num_classes=num_classes).to(device) # load model weights model.load_state_dict(torch.load(model_path, map_location=device)) model.eval() y_true = [] y_pred = [] for root, dirs, files in os.walk(folder_path): for file in files: if file.endswith(".jpg") or file.endswith(".jpeg"): img_path = os.path.join(root, file) assert os.path.exists(img_path), "file: '{}' dose not exist.".format(img_path) img = Image.open(img_path) # [N, C, H, W] img = data_transform(img) # expand batch dimension img = torch.unsqueeze(img, dim=0) # predict class with torch.no_grad(): output = torch.squeeze(model(img.to(device))).cpu() predict = torch.softmax(output, dim=0) predict_cla = torch.argmax(predict).numpy() y_true.append(class_indict[os.path.basename(root)]) y_pred.append(predict_cla) # plot confusion matrix cm = confusion_matrix(y_true, y_pred) fig, ax = plt.subplots(figsize=(5, 5)) ax.imshow(cm, cmap=plt.cm.Blues, aspect='equal') ax.set_xlabel('Predicted label') ax.set_ylabel('True label') ax.set_xticks(np.arange(len(class_indict))) ax.set_yticks(np.arange(len(class_indict))) ax.set_xticklabels(class_indict.values(), rotation=90) ax.set_yticklabels(class_indict.values()) ax.tick_params(axis=u'both', which=u'both',length=0) for i in range(len(class_indict)): for j in range(len(class_indict)): text = ax.text(j, i, cm[i, j], ha="center", va="center", color="white" if cm[i, j] > cm.max() / 2. else "black") fig.tight_layout() plt.show() if __name__ == '__main__': # set the paths for the model, class_indict json, and test data folder model_path = './weights/best_model.pth' json_path = './class_indices.json' folder_path = './test_data' predict_folder(model_path, json_path, folder_path) ``` 请注意,这个函数的参数需要您自己根据您的实际情况进行设置,以匹配模型、类别标签和测试集的路径。
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