1096. Consecutive Factors (20)

本文介绍了一种通过分解整数寻找最长连续因子序列的方法,并提供两种实现方案。一种是较为复杂的过程,另一种则更为简洁高效。文章详细展示了如何利用循环和条件判断来找到使整数分解的因子序列最长的起始因子。

没有细细去分析题目,方法有点繁琐,而且还有一个1分的测试点超时。。。

有的地方有些问题,因式有重复的情况,不过对于结果来说不影响

#include<iostream>
#include<vector> 
#include<algorithm>
using namespace std;
vector<int> all_factors,out_factors,temp_factors,fin_factors;
int n;
bool IsFactors(vector<int> &it){
	int prod=1; 
	for(int i=0;i<it.size();i++){
		prod*=it[i];
	}
	if(prod==n) return true;
	else 
        return false;
}
bool GetAllFactors(int begin){//把n分解为若干个大于begin的因子的乘积形式 
	int temp=n;
	for(int i=begin;i*i<=n;i++){
		if(temp%i==0&&temp!=0){//temp!=0为了防止当上一次循环时temp<i导致temp/=i之后temp=0 
			all_factors.push_back(i);
			temp/=i;
		}
		 
	}
	//if(temp!=1) 
	all_factors.push_back(temp);//有重复的情况
	if(IsFactors(all_factors)){
		return true;
	}
	else{
		return false;
	}
}
int LongestFactors(vector<int> &it){
	int count=1,maxcount=0;
	temp_factors.clear();
	temp_factors.push_back(it[0]);
	for(int i=1;i<it.size();i++){
		if(it[i]==it[i-1]+1){
		    temp_factors.push_back(it[i]);
			count++;
		}
		else{
			if(maxcount<count){
				maxcount=count;
				out_factors.clear();
			    out_factors=temp_factors;
			}
			temp_factors.clear();
			temp_factors.push_back(it[i]);
			count=1;
		}
	}
	if(maxcount<count){//只有一个因子 
				maxcount=count;
				out_factors.clear();
			    out_factors=temp_factors;
			}
	return maxcount;
}
int main(){
	cin>>n;
	if(n==2|| n==3){//因为下面i从2开始,且i*i<=n,所以2、3单独讨论 
		printf("1\n%d",n);
		return 0;
	}
	int maxcount=0,count=1;//因为当只有一个因子时,70行的判断条件maxcount<count能进入,所以maxcount=0,count随意 
	for(int i=2;i*i<=n;i++){
		all_factors.clear();//每一轮循环找出若干个乘积等于n的大于i的因子,暂存于all_factors 
		if(GetAllFactors(i)){
			count=LongestFactors(all_factors);
		    if(maxcount<count){
			    maxcount=count;
			    fin_factors.clear(); 
			    fin_factors=out_factors; 
		    }
		}
	}
	cout<<maxcount<<endl;
	for(int i=0;i<fin_factors.size();i++){
		if(i!=0) printf("*");
		printf("%d",fin_factors[i]);
	}
	return 0;
}
题解上的答案,思路比较直接点,简单又好懂

代码贴下

#include<iostream>
#include<vector>
#include<cmath> 
#include<algorithm>
using namespace std;
typedef long long LL;
int main(){
	LL n;
	cin>>n;
	LL sqr = (LL)sqrt(1.0*n);
	LL ansI = 0;//第一个整数 
	LL ansLen = 0;//最长连续整数的长度
	for(LL i = 2; i <= sqr; i++){
		LL temp = 1, j = i;//temp为当前连续整数的乘积
		while(1){
			temp *= j;
			if(n % temp != 0) break;
			if(ansLen < j-i+1){
				ansLen = j-i+1;
				ansI = i;
			}
			j++; 
		} 
	} 
	if(ansLen == 0){
		printf("1\n%lld",n);
	}
	else{
		cout<<ansLen<<endl;
		for(LL i = 0; i < ansLen; i++){
			if(i!=0) printf("*");
			printf("%lld",ansI++);
		}
	}
	return 0;
}



def run_backend(cfg, model, states, keyframes, K): set_global_config(cfg) device = keyframes.device factor_graph = FactorGraph(model, keyframes, K, device) retrieval_database = load_retriever(model) mode = states.get_mode() while mode is not Mode.TERMINATED: mode = states.get_mode() if mode == Mode.INIT or states.is_paused(): time.sleep(0.01) continue if mode == Mode.RELOC: frame = states.get_frame() success = relocalization(frame, keyframes, factor_graph, retrieval_database) if success: states.set_mode(Mode.TRACKING) states.dequeue_reloc() continue idx = -1 with states.lock: if len(states.global_optimizer_tasks) > 0: idx = states.global_optimizer_tasks[0] if idx == -1: time.sleep(0.01) continue # Graph Construction kf_idx = [] # k to previous consecutive keyframes n_consec = 1 for j in range(min(n_consec, idx)): kf_idx.append(idx - 1 - j) frame = keyframes[idx] retrieval_inds = retrieval_database.update( frame, add_after_query=True, k=config["retrieval"]["k"], min_thresh=config["retrieval"]["min_thresh"], ) kf_idx += retrieval_inds lc_inds = set(retrieval_inds) lc_inds.discard(idx - 1) if len(lc_inds) > 0: print("Database retrieval", idx, ": ", lc_inds) kf_idx = set(kf_idx) # Remove duplicates by using set kf_idx.discard(idx) # Remove current kf idx if included kf_idx = list(kf_idx) # convert to list frame_idx = [idx] * len(kf_idx) if kf_idx: factor_graph.add_factors( kf_idx, frame_idx, config["local_opt"]["min_match_frac"] ) with states.lock: states.edges_ii[:] = factor_graph.ii.cpu().tolist() states.edges_jj[:] = factor_graph.jj.cpu().tolist() if config["use_calib"]: factor_graph.solve_GN_calib() else: factor_graph.solve_GN_rays() with states.lock: if len(states.global_optimizer_tasks) > 0: idx = states.global_optimizer_tasks.pop(0)
最新发布
05-31
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