杭电OJ题 1444 Maya Calendar解题报告

POJ 1008 Maya Calendar 此程序我运用的都是C++基础知识,方便新手学习理解,在VC6.0上测试0错误0警告,可放心使用,在此衷心希望大家学习进步 以下是题目 Description During his last sabbatical, professor M. A. Ya made a surprising discovery about the old Maya calendar. From an old knotted message, professor discovered that the Maya civilization used a 365 day long year, called Haab, which had 19 months. Each of the first 18 months was 20 days long, and the names of the months were pop, no, zip, zotz, tzec, xul, yoxkin, mol, chen, yax, zac, ceh, mac, kankin, muan, pax, koyab, cumhu. Instead of having names, the days of the months were denoted by numbers starting from 0 to 19. The last month of Haab was called uayet and had 5 days denoted by numbers 0, 1, 2, 3, 4. The Maya believed that this month was unlucky, the court of justice was not in session, the trade stopped, people did not even sweep the floor. For religious purposes, the Maya used another calendar in which the year was called Tzolkin (holly year). The year was divided into thirteen periods, each 20 days long. Each day was denoted by a pair consisting of a number and the name of the day. They used 20 names: imix, ik, akbal, kan, chicchan, cimi, manik, lamat, muluk, ok, chuen, eb, ben, ix, mem, cib, caban, eznab, canac, ahau and 13 numbers; both in cycles. Notice that each day has an unambiguous description. For example, at the beginning of the year the days were described as follows: 1 imix, 2 ik, 3 akbal, 4 kan, 5 chicchan, 6 cimi, 7 manik, 8 lamat, 9 muluk, 10 ok, 11 chuen, 12 eb, 13 ben, 1 ix, 2 mem, 3 cib, 4 caban, 5 eznab, 6 canac, 7 ahau, and again in the next period 8 imix, 9 ik, 10 akbal . . . Years (both Haab and Tzolkin) were denoted by numbers 0, 1, : : : , where the number 0 was the beginning of the world. Thus, the first day was: Haab: 0. pop 0 Tzolkin: 1 imix 0 Help professor M. A. Ya and write a program for him to convert the dates from the Haab calendar to the Tzolkin calendar. Input The date in Haab is given in the following format: NumberOfTheDay. Month Year The first line of the input file contains the number of the input dates in the file. The next n lines contain n dates in the Haab calendar format, each in separate line. The year is smaller then 5000. Output The date in Tzolkin should be in the following format: Number NameOfTheDay Year The first line of the output file contains the number of the output dates. In the next n lines, there are dates in the Tzolkin calendar format, in the order corresponding to the input dates. Sample Input 3 10. zac 0 0. pop 0 10. zac 1995 Sample Output 3 3 chuen 0 1 imix 0 9 cimi 2801 立即下载

Maya Calendar

Time Limit: 2000/1000 MS (Java/Others)    Memory Limit: 65536/32768 K (Java/Others)
Total Submission(s): 359    Accepted Submission(s): 158



Problem Description
During his last sabbatical, professor M. A. Ya made a surprising discovery about the old Maya calendar. From an old knotted message, professor discovered that the Maya civilization used a 365 day long year, called Haab, which had 19 months. Each of the first 18 months was 20 days long, and the names of the months were pop, no, zip, zotz, tzec, xul, yoxkin, mol, chen, yax, zac, ceh, mac, kankin, muan, pax, koyab, cumhu. Instead of having names, the days of the months were denoted by numbers starting from 0 to 19. The last month of Haab was called uayet and had 5 days denoted by numbers 0, 1, 2, 3, 4. The Maya believed that this month was unlucky, the court of justice was not in session, the trade stopped, people did not even sweep the floor.

For religious purposes, the Maya used another calendar in which the year was called Tzolkin (holly year). The year was divided into thirteen periods, each 20 days long. Each day was denoted by a pair consisting of a number and the name of the day. They used 20 names: imix, ik, akbal, kan, chicchan, cimi, manik, lamat, muluk, ok, chuen, eb, ben, ix, mem, cib, caban, eznab, canac, ahau and 13 numbers; both in cycles.

Notice that each day has an unambiguous description. For example, at the beginning of the year the days were described as follows:

1 imix, 2 ik, 3 akbal, 4 kan, 5 chicchan, 6 cimi, 7 manik, 8 lamat, 9 muluk, 10 ok, 11 chuen, 12 eb, 13 ben, 1 ix, 2 mem, 3 cib, 4 caban, 5 eznab, 6 canac, 7 ahau, and again in the next period 8 imix, 9 ik, 10 akbal . . .

Years (both Haab and Tzolkin) were denoted by numbers 0, 1, : : : , where the number 0 was the beginning of the world. Thus, the first day was:

 Haab: 0. pop 0
 Tzolkin: 1 imix 0

Help professor M. A. Ya and write a program for him to convert the dates from the Haab calendar to the Tzolkin calendar.

 

Input
The date in Haab is given in the following format:
NumberOfTheDay. Month Year

The first line of the input file contains the number of the input dates in the file. The next n lines contain n dates in the Haab calendar format, each in separate line. The year is smaller then 5000.

 

Output
The date in Tzolkin should be in the following format:
Number NameOfTheDay Year

The first line of the output file contains the number of the output dates. In the next n lines, there are dates in the Tzolkin calendar format, in the order corresponding to the input dates.

 

Sample Input
3 10. zac 0 0. pop 0 10. zac 1995
 
——————————————————————————————————————————————————————
先将时间转化为天数,然后再转化为对应格式的日期

/****************************
 *Name:Maya Calendar.c
 *Tags:ACM water
 ****************************/

#include <stdio.h>
#include <string.h>

int main()
{
      char haab[19][10] = {"pop", "no", "zip", "zotz", "tzec", "xul", "yoxkin", "mol", "chen", "yax", "zac", "ceh", "mac", "kankin", "muan", "pax", "koyab", "cumhu", "uayet"};
      char holly[20][10] = {"imix", "ik", "akbal", "kan", "chicchan", "cimi", "manik", "lamat", "muluk", "ok", "chuen", "eb", "ben", "ix", "mem", "cib", "caban", "eznab", "canac", "ahau"};
      int year, num, days, n, i;
      char dayname[10];
      scanf("%d", &n);
      printf("%d\n", n);
      while(n--) {
	    days = 0;
	    scanf("%d. %s %d", &num, dayname, &year);
	    /* printf("%d. %s %d\n", num, dayname, year); */
	    for(i = 0; i < 19; i++) {
		  if(strcmp(dayname, haab[i]) == 0) {
			days = 20 * i + 365 * year + num;
			break;
		  }
	    }
	    /* printf("days = %d\n", days); */
	    year = days / 260;
	    num = days % 13 + 1;
	    i = days % 20;
	    /* printf("i = %d\n", i); */
	    printf("%d %s %d\n", num, holly[i], year);
      }
      return 0;
}


NR RRC协议(2)---RRC连接建立流程 RRC连接建立目的是建立SRB1 RRC连接建立还用于将初始NAS消息从UE传送到网络 RRC连接的初始阶段期间,不激活AS安全性 RRC连接建立由UE侧发起 阅读详情

相关推荐

PTA 学生信息的那些操作:添加记录(结构体)

这是河南工业大学自主命题的一道基础题,题目如下: 有一学生成绩表,包括学号、姓名、3门课程成绩。已知该成绩表按学号升序排序。请编程实现,添加一个新的学生信息,且使成绩表仍按学号有序;若待添加的学号与已有学号重复,则输出错误信息,拒绝添加。 输入格式: 首先输入一个整数n(1<=n<=100),表示学生人数; 然后输入n行,每行包含一个学生的信息:学号(12位)、姓名(不含空格且不超过20位),以及3个整数,表示3门课成绩,数据之间用空格隔开。 最后一行输入一个待添加的学生信息,包括学号

weixin_63617665的博客 3156

Maya Calendar(年月日换算问题)

Problem: During his last sabbatical, professor M. A. Ya made a surprising discovery about the old Maya calendar. From an old knotted message, professor discovered that the Maya civilization used a 365 day long year, called Haab, which had 19 months. Each o

startoveroo的博客 409

从零到一:AnythingLLM与Ollama的本地知识库搭建实战

本文详细介绍了如何使用AnythingLLM与Ollama从零开始搭建本地知识库,涵盖技术选型、环境部署、系统配置及优化策略。通过RAG技术架构,实现安全高效的文档管理与智能问答,特别适合企业及个人构建私有化知识管理系统。

weixin_29079643的博客 562

POJ - 1008 Maya Calendar

简单模拟,现将第一种日历表示的天数计算出来,再转换为第二种日历。注意边写边检查 POJ-1008MayaCalendar TimeLimit:1000MS MemoryLimit:10000KB 64bitIOFormat:%I64d&%I64u Description...

weixin_30535843的博客 217

MPU6050数据融合避坑指南:当欧拉角遇到万向节死锁,我们该如何处理?

本文深入探讨了MPU6050在姿态解算中遇到的欧拉角万向节死锁问题,提供了四元数解决方案及工程实践策略。通过分析死锁现象的本质,介绍四元数的优势与实现方法,并给出必须使用欧拉角时的三种应对方案,帮助开发者有效规避数据跳变和解算失效问题。

weixin_30415113的博客 541

UDP_千兆光通信(四)Tri Mode Ethernet MAC ip核

其中 Transmit Engine 从 AXI-Stream TX 接口接收数据,并添加前导码 Preamble、帧起始界定符 SFD、帧校验序列 FSC 等,并在必要时(长度小于最小 MAC 帧长度)填充数据,因此在使用 TEMAC 时,MAC 帧的这几个部分就不需要我们自己添加了(不过其他的 MAC 帧部分,可选的帧过滤器 Frame Filter 用于过滤与一组可配置过滤器匹配的帧,一般用于过滤掉目的 MAC 地址与本设备不一致的帧,TEMAC 默认开启帧过滤器。默认值为使用64位宽的计数器。

lzr232的博客 1767

解析:Maya Calendar POJ 1008,UVA 300

题目: 上周末,M. A. Ya教授对古老的玛雅有了一个重大发现。从一个古老的节绳(玛雅人用于记事的工具)中,教授发现玛雅人使用Haab历法,一年有365天。Haab历法每年有19个月,在前18个月,每月有20天,月份的名字分别是pop, no, zip, zotz, tzec, xul, yoxkin, mol, chen, yax, zac, ceh, mac, kankin, muan, pax, koyab, cumhu。这些月份中的日期用0到19表示;Haab历的最后一个月叫做uayet,它只有5

qq_46470984的博客 442

芒果YOLO11改进106:检测头篇DynamicHead动态检测头:基于DynamicHead,二次原创提出SATNeXt模块,提出全新的动态DynamicSatDetect

检测头篇DynamicHead动态检测头:基于DynamicHead,二次原创提出SATNeXt模块,提出全新的动态DynamicSatDetect

包括YOLOv5、YOLOv7、YOLOv8等模型改进 772

Python数据结构与算法(poj):P01008——Maya Calendar

我们首先根据题意算出对应的天数,然后换至另一个日历,这里需要注意日期(因为是通过取模得到的)可能为0,这时需要改为13,如果这时星期对应的还恰好是第一个星期,那么说明应该倒退一年,也就是年份数减一。同时别忘了,需要在最开始处输出日期的数量。这道题主要考察不同日历之间的变换,并进行符合要求的输出。

2301_79402523的博客 297

手把手教你学Simulink——基于Simulink的Boost变换器电流模式控制(峰值电流控制)建模示例

本文详细介绍了基于Simulink的Boost变换器峰值电流模式控制建模方法。首先分析了Boost变换器在CCM模式下的工作原理及电流控制优势,重点阐述了斜坡补偿的必要性。通过分步指导搭建Simulink模型,包括主电路、电流采样、RS触发器和电压外环PI控制器等核心模块。仿真验证了系统在启动、负载突变和输入电压跌落等工况下的优异性能,输出电压稳定在48V±0.5V。文章还总结了工程实践要点,并提出了数字实现等扩展方向。该建模方法为电力电子高级控制策略的工程验证提供了有效途径。

MHD0815的博客 248

C#单例模式在工控上位机的实战应用:一台设备只连一次,稳定运行7×24小时

在工控行业,稳定性比功能更重要。一台上位机需要7×24小时运行,任何一次因资源冲突导致的停机,都可能给工厂带来巨大损失。单例模式的核心价值,就是通过保证资源唯一性,避免PLC连接冲突、串口占用、日志写入错乱等问题,为工控上位机的稳定运行保驾护航。按需使用:不是所有类都要单例,只有资源唯一性的类才用;线程安全:多线程场景下,必须用双重检查锁定或静态内部类写法;手动释放:单例类中的硬件资源(PLC、串口),程序退出时必须手动释放,避免资源泄漏。

这里只有干货:从 Modbus 通信到 MES 对接,从 YOLO 训练到工控机部署,带你搞定工业软件开发全流程。 549

百练:1008:Maya Calendar

1008:Maya Calendar 总时间限制: 1000ms 内存限制: 65536kB 描述 During his last sabbatical, professor M. A. Ya made a surprising discovery about the old Maya calendar. From an old knotted message, professor discovered that the Maya civilization used a 365 day long year,

ffsdfg的博客 229

COMSOL有限元仿真深度指南:为结构件添加预应变

一、什么是预应力和预应变? 我们知道,结构力学问题中,结构件在外载荷作用下产生变形响应以吸收外载荷做功带来的能量;而在结构内部,整体的受力和变形则是通过固体微元的应力与应变传递的。 对于线弹性材料来说,应力与应变的关系可以线性表达,也即 σ=Dεσ = Dεσ=Dε ;在该式中,无论固体微元的应变程度多大,弹性矩阵DDD都是一个不变的常值。而对于超弹性材料来说,应力与应变的关系则需要通过应变能密度函数WWW来表征(详细参加博客《COMSOL有限元仿真深度指南:超弹性模型有限元分析入门》)。 从上面我们也不难

忘了面孔的Batou 7170

fjnu 1203 Maya Calendar

DescriptionDuring his last sabbatical, professor M. A. Ya made a surprising discovery about the old Maya calendar. From an old knotted message, professor discovered that the Maya civilization used

异リ度ュ空ウ間(镜像站) 1144

LinePhone3.2.7(android端最新版本) sip客户端

SIP客户端For android:LinePhone3.2.7(最新版本),官网地址:http://www.linphone.org/

深度学习工具包 DeepLearningToolbox

深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/)I would suggest you use one of the tools mentioned above rather than use this toolbox.Best, Rasmus.DeepLearnToolbox================A Matlab toolbox for Deep Learning.Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.It is inspired by the human brain's apparent deep (layered, hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)```@MASTERSTHESIS\{IMM2012-06284, author = "R. B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")

【POJ】1008 Maya Calendar

Maya Calendar Time Limit: 1000MS   Memory Limit: 10000K Total Submissions: 49534   Accepted: 15194 Description During his last sabbatical, professor M. A. Ya made a sur

Manjushri的知识库 780

efm32 入门.pdf

本PDF共计22页,介绍的是操作驱动API,下载与否自己斟酌。

Maya Calendar

Description During his last sabbatical, professor M. A. Ya made a surprising discovery about the old Maya calendar. From an old knotted message, professor discovered that the Maya civilization used a

海绵的博客 1296

老版QQ原生表情gif动态

这是老版的QQ表情,不过全是动态的的,Windows照片查看器是不会动的,可以右击用浏览器打开查看,再说一遍是老版的,只是分享给宁愿用老版也要动态的人。

pku 1008实现与感想

1008  Maya Calendar<br />DescriptionDuring his last sabbatical, professor M. A. Ya made a surprising discovery about the old Maya calendar. From an old knotted message, professor discovered that the Maya civilization used a 365 day long year, called Haab,

icipher2010的专栏 538

**POJ 1008 Maya Calendar解题报告

//POJ 1008 Maya Calendar解题报告 // #include #include #include #include #include #include #include using namespace std; /*pop, no, zip, zotz, tzec, xul, yoxkin, mol, chen, yax, zac, ceh, mac

verylonglongago的博客 485

UVa300 - Maya Calendar

#include #include #include #include #include using namespace std; const int N = 19; const int M = 10; const char Haab[N][M] = {"pop", "no", "zip", "zotz", "tzec", "xul", "yoxkin", "mol", "chen

多看多听多总结 1360

POJ1008-Maya Calendar

全解题报告索引目录 -> 【北大ACM – POJ试题分类】 转载请注明出处:http://exp-blog.com ------------------------------------------------------------------------- 大致题意: 有中文版= = 我不多说 解题思路: 模拟题,细心点就好了,没难度。 Habb历一年...

ζёСяêτ - 小優YoU 8682

POJ1088 Maya Calendar

题目描述: Description During his last sabbatical, professor M. A. Ya made a surprising discovery about the old Maya calendar. From an old knotted message, professor discovered that the Maya civiliza

淡然坊 1356

北大 1008 Maya Calendar——做到这里的一点心得

Maya Calendar Time Limit: 1000MS   Memory Limit: 10000K Total Submissions: 60309   Accepted: 18596 Description During his last sabbatical, professor M. A. Ya made a surpris

GodLike 2005

UVa 300 - Maya Calendar

題目:瑪雅的兩套力法轉換,第一套為19個月前18個月,每個月20天,最後一個5天;             第二套曆法類似中國古代的干支紀年法,13個天干和20個地支獨立循環260天一週期。 分析:入門題,數論。進制轉化題目。             先將曆法一的時間轉化成到第1天的總天數:sum=365*year+20*(month-1)+day;             然後轉化成第二

小白菜又菜 1936

水题—— B - Maya Calendar

B - Maya Calendar During his last sabbatical, professor M. A. Ya made a surprising discovery about the old Maya calendar. From an old knotted message, professor discovered that the Maya civilization u...

lemail0406的博客 409
上一篇: 杭电OJ题 1412 {A} + {B} 解题报告
下一篇: 杭电OJ题 1491 Octorber 21st 解题报告
Rufeng-Blog
博客等级 码龄14年 18粉丝 82原创
评论
成就一亿技术人!
拼手气红包6.0元
还能输入1000个字符
 
 条评论被折叠 查看
添加红包

请填写红包祝福语或标题

个

红包个数最小为10个

元

红包金额最低5元

当前余额3.43元 前往充值 >
需支付:10.00元
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付元
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值