see-448. Find All Numbers Disappeared in an Array

Python ROC曲线与AUC计算实战:从二分类到多分类避坑指南 在机器学习分类任务中,模型评估是确保算法有效性的关键环节。ROC曲线与AUC值作为不依赖阈值的评估指标,能有效衡量模型对正负样本的排序能力,尤其适用于不平衡数据场景。其原理基于混淆矩阵推导的TPR与FPR,通过扫描不同阈值绘制曲线,AUC则量化了随机正样本得分高于负样本的概率。技术价值在于避免准确率陷阱,提升模型泛化评估的可靠性。典型应用包括风控、医疗诊断和欺诈检测。Python中利用scikit-learn的roc_curve和auc函数可快速实现,但需注意参数顺序、多分类处理及阈值选择等常见问题。本文围 阅读详情

该题与442. Find All Duplicates in an Array题同理,殊途同归:

置位法和取负法分别如下:

class Solution {
public:
    vector<int> findDisappearedNumbers(vector<int>& nums) {
        vector<int> table;
        //table=place(nums,table);
        table=minus(nums,table);
        return table;
    }
    vector<int> place(vector<int> &nums,vector<int> &table) //置位法
    {
        int i=0;
        while(i<nums.size())
        {
            if(nums[i]!=i+1 && nums[i]!=nums[nums[i]-1]) 
            {
                int temp=nums[i]; // the relatting position
                nums[i]=nums[temp-1];
                nums[temp-1]=temp;
            }
            else
                i++;
        }
        
        for(int i=0;i<nums.size();i++)
        {
            if(i+1!=nums[i])
                table.push_back(i+1);
        }
        return table;
    }
     vector<int> minus(vector<int> &nums,vector<int> &table)  //取负法
     {
         for(int i=0;i<nums.size();i++)
         {
             int flag=abs(nums[i]);
             if(nums[flag-1]>0)
                 nums[flag-1]=-nums[flag-1];
         }
         
         for(int i=0;i<nums.size();i++)
         {
             if(nums[i]>0)
                 table.push_back(i+1);
         }
         return table;
     }
};

 

别再到处找模型了!手把手教你用Xinference+Docker部署本地私有大模型(Llama2/Qwen实战) 本文详细介绍了如何使用Xinference和Docker部署本地私有化大模型(如Llama2/Qwen),涵盖硬件配置、模型管理、Docker部署及性能优化等关键步骤。通过实战案例,帮助开发者快速构建企业级AI推理服务,确保数据安全与高效运行。 阅读详情

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