Timus 1139. City Blocks 题解

本文探讨了一架直升机从城市的最西南角飞往最东北角时所覆盖的城市街区数量。通过数学方法解决这一问题,并提供了一个C++实现示例。

The blocks in the city of Fishburg are of square form. Navenues running south to north and Mstreets running east to west bound them. A helicopter took off in the most southwestern crossroads and flew along the straight line to the most northeastern crossroads. How many blocks did it fly above?
Note.A block is a square of minimum area (without its borders).

Input

The input contains Nand Mseparated by one or more spaces. 1 < N, M< 32000.

Output

The number of blocks the helicopter flew above.

Samples

input output
4 3
4
3 3
2

Hint

The figures for samples:
Problem illustration

Reference - The blog below explaine in detail, but I seem like not quite get it that much:

http://flickeringtubelight.net/blog/2006/08/a-diagonal-through-a-rectangular-grid-of-squares/

But anyway, it's a good explanation. And it should be benefit to read it, maybe you can come up with your own way to understand this approach.

This is a problem that can be approach by many ways. I want to post it because it is very mathematics-related.

I , too, use that formula to solve it.

My suggestion is: if you really have a hard time to deduct that formula, just check the small examples, like n = 2, m = 3, and n = 5, m = 4 and so on.

Below is my C++ solution:

#include <iostream>
using namespace std;

int GCDcityBlocks(int n, int m)
{
	while (m)
	{
		int a = n % m;
		n = m;
		m = a;
	}
	return n;
}

void CityBlocks1139()
{
	int n = 0, m = 0;
	cin>>n>>m;
	n--, m--;
	int k = GCDcityBlocks(n, m);
	int ans = n/k + m/k - 1;
	cout<<ans * k;
}




在车辆工程中,悬架系统的性能评估和优化一直是研究的热点。悬架不仅关乎车辆的乘坐舒适性,还直接影响到车辆的操控性和稳定性。为了深入理解悬架的动态行为,研究人员经常使用“二自由度悬架模型”来简化分析,并运用“传递函数”这一数学工具来描述悬架系统的动态特性。 二自由度悬架模型将复杂的车辆系统简化为两个独立的部分:车轮和车身。这种简化模型能够较准确地模拟出车辆在垂直方向上的运动行为,同时忽略了侧向和纵向的动态影响,这使得工程师能够更加专注于分析与优化与垂直动态相关的性能指标。 传递函数作为控制系统理论中的一种工具,能够描述系统输入和输出之间的关系。在悬架系统中,传递函数特别重要,因为它能够反映出路面不平度如何被悬架系统转化为车内乘员感受到的振动。通过传递函数,我们可以得到一个频率域上的表达式,从中分析出悬架系统的关键动态特性,如系统的振幅衰减特性和共振频率等。 在实际应用中,工程师通过使用MATLAB这类数学软件,建立双质量悬架的数学模型。模型中的参数包括车轮质量、车身质量、弹簧刚度以及阻尼系数等。通过编程求解,工程师可以得到悬架系统的传递函数,并据此绘制出传递函数曲线。这为评估悬架性能提供了一个直观的工具,使工程师能够了解悬架在不同频率激励下的响应情况。
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format( RuntimeError: Error(s) in loading state_dict for PResNet: Missing key(s) in state_dict: "conv1.conv1_1.conv.weight", "conv1.conv1_1.norm.weight", "conv1.conv1_1.norm.bias", "conv1.conv1_1.norm.running_mean", "conv1.conv1_1.norm.running_var", "conv1.conv1_2.conv.weight", "conv1.conv1_2.norm.weight", "conv1.conv1_2.norm.bias", "conv1.conv1_2.norm.running_mean", "conv1.conv1_2.norm.running_var", "conv1.conv1_3.conv.weight", "conv1.conv1_3.norm.weight", "conv1.conv1_3.norm.bias", "conv1.conv1_3.norm.running_mean", "conv1.conv1_3.norm.running_var", "res_layers.0.blocks.0.branch2a.conv.weight", "res_layers.0.blocks.0.branch2a.norm.weight", "res_layers.0.blocks.0.branch2a.norm.bias", "res_layers.0.blocks.0.branch2a.norm.running_mean", "res_layers.0.blocks.0.branch2a.norm.running_var", "res_layers.0.blocks.0.branch2b.conv.weight", "res_layers.0.blocks.0.branch2b.norm.weight", "res_layers.0.blocks.0.branch2b.norm.bias", "res_layers.0.blocks.0.branch2b.norm.running_mean", "res_layers.0.blocks.0.branch2b.norm.running_var", "res_layers.0.blocks.0.branch2c.conv.weight", "res_layers.0.blocks.0.branch2c.norm.weight", "res_layers.0.blocks.0.branch2c.norm.bias", "res_layers.0.blocks.0.branch2c.norm.running_mean", "res_layers.0.blocks.0.branch2c.norm.running_var", "res_layers.0.blocks.0.short.conv.weight", "res_layers.0.blocks.0.short.norm.weight", "res_layers.0.blocks.0.short.norm.bias", "res_layers.0.blocks.0.short.norm.running_mean", "res_layers.0.blocks.0.short.norm.running_var", "res_layers.0.blocks.1.branch2a.conv.weight", "res_layers.0.blocks.1.branch2a.norm.weight", "res_layers.0.blocks.1.branch2a.norm.bias", "res_layers.0.blocks.1.branch2a.norm.running_mean", "res_layers.0.blocks.1.branch2a.norm.running_var", "res_layers.0.blocks.1.branch2b.conv.weight", "res_layers.0.blocks.1.branch2b.norm.weight", "res_layers.0.blocks.1.branch2b.norm.bias", "res_layers.0.blocks.1.branch2b.norm.running_mean", "res_layers.0.blocks.1.branch2b.norm.running_var", "res_layers.0.blocks.1.branch2c.conv.weight", "res_layers.0.blocks.1.branch2c.norm.weight", "res_layers.0.blocks.1.branch2c.norm.bias", "res_layers.0.blocks.1.branch2c.norm.running_mean", "res_layers.0.blocks.1.branch2c.norm.running_var", "res_layers.0.blocks.2.branch2a.conv.weight", "res_layers.0.blocks.2.branch2a.norm.weight", "res_layers.0.blocks.2.branch2a.norm.bias", "res_layers.0.blocks.2.branch2a.norm.running_mean", "res_layers.0.blocks.2.branch2a.norm.running_var", "res_layers.0.blocks.2.branch2b.conv.weight", "res_layers.0.blocks.2.branch2b.norm.weight", "res_layers.0.blocks.2.branch2b.norm.bias", "res_layers.0.blocks.2.branch2b.norm.running_mean", "res_layers.0.blocks.2.branch2b.norm.running_var", "res_layers.0.blocks.2.branch2c.conv.weight", "res_layers.0.blocks.2.branch2c.norm.weight", "res_layers.0.blocks.2.branch2c.norm.bias", "res_layers.0.blocks.2.branch2c.norm.running_mean", "res_layers.0.blocks.2.branch2c.norm.running_var", "res_layers.1.blocks.0.branch2a.conv.weight", "res_layers.1.blocks.0.branch2a.norm.weight", "res_layers.1.blocks.0.branch2a.norm.bias", "res_layers.1.blocks.0.branch2a.norm.running_mean", "res_layers.1.blocks.0.branch2a.norm.running_var", "res_layers.1.blocks.0.branch2b.conv.weight", "res_layers.1.blocks.0.branch2b.norm.weight", "res_layers.1.blocks.0.branch2b.norm.bias", "res_layers.1.blocks.0.branch2b.norm.running_mean", "res_layers.1.blocks.0.branch2b.norm.running_var", "res_layers.1.blocks.0.branch2c.conv.weight", "res_layers.1.blocks.0.branch2c.norm.weight", "res_layers.1.blocks.0.branch2c.norm.bias", "res_layers.1.blocks.0.branch2c.norm.running_mean", "res_layers.1.blocks.0.branch2c.norm.running_var", "res_layers.1.blocks.0.short.conv.conv.weight", "res_layers.1.blocks.0.short.conv.norm.weight", "res_layers.1.blocks.0.short.conv.norm.bias", "res_layers.1.blocks.0.short.conv.norm.running_mean", "res_layers.1.blocks.0.short.conv.norm.running_var", "res_layers.1.blocks.1.branch2a.conv.weight", "res_layers.1.blocks.1.branch2a.norm.weight", "res_layers.1.blocks.1.branch2a.norm.bias", "res_layers.1.blocks.1.branch2a.norm.running_mean", "res_layers.1.blocks.1.branch2a.norm.running_var", "res_layers.1.blocks.1.branch2b.conv.weight", "res_layers.1.blocks.1.branch2b.norm.weight", "res_layers.1.blocks.1.branch2b.norm.bias", "res_layers.1.blocks.1.branch2b.norm.running_mean", "res_layers.1.blocks.1.branch2b.norm.running_var", "res_layers.1.blocks.1.branch2c.conv.weight", "res_layers.1.blocks.1.branch2c.norm.weight", "res_layers.1.blocks.1.branch2c.norm.bias", "res_layers.1.blocks.1.branch2c.norm.running_mean", "res_layers.1.blocks.1.branch2c.norm.running_var", "res_layers.1.blocks.2.branch2a.conv.weight", "res_layers.1.blocks.2.branch2a.norm.weight", "res_layers.1.blocks.2.branch2a.norm.bias", "res_layers.1.blocks.2.branch2a.norm.running_mean", "res_layers.1.blocks.2.branch2a.norm.running_var", "res_layers.1.blocks.2.branch2b.conv.weight", "res_layers.1.blocks.2.branch2b.norm.weight", "res_layers.1.blocks.2.branch2b.norm.bias", "res_layers.1.blocks.2.branch2b.norm.running_mean", "res_layers.1.blocks.2.branch2b.norm.running_var", "res_layers.1.blocks.2.branch2c.conv.weight", "res_layers.1.blocks.2.branch2c.norm.weight", "res_layers.1.blocks.2.branch2c.norm.bias", "res_layers.1.blocks.2.branch2c.norm.running_mean", "res_layers.1.blocks.2.branch2c.norm.running_var", "res_layers.1.blocks.3.branch2a.conv.weight", "res_layers.1.blocks.3.branch2a.norm.weight", "res_layers.1.blocks.3.branch2a.norm.bias", "res_layers.1.blocks.3.branch2a.norm.running_mean", "res_layers.1.blocks.3.branch2a.norm.running_var", "res_layers.1.blocks.3.branch2b.conv.weight", "res_layers.1.blocks.3.branch2b.norm.weight", "res_layers.1.blocks.3.branch2b.norm.bias", "res_layers.1.blocks.3.branch2b.norm.running_mean", "res_layers.1.blocks.3.branch2b.norm.running_var", "res_layers.1.blocks.3.branch2c.conv.weight", "res_layers.1.blocks.3.branch2c.norm.weight", "res_layers.1.blocks.3.branch2c.norm.bias", "res_layers.1.blocks.3.branch2c.norm.running_mean", "res_layers.1.blocks.3.branch2c.norm.running_var", "res_layers.2.blocks.0.branch2a.conv.weight", "res_layers.2.blocks.0.branch2a.norm.weight", "res_layers.2.blocks.0.branch2a.norm.bias", "res_layers.2.blocks.0.branch2a.norm.running_mean", "res_layers.2.blocks.0.branch2a.norm.running_var", "res_layers.2.blocks.0.branch2b.conv.weight", "res_layers.2.blocks.0.branch2b.norm.weight", "res_layers.2.blocks.0.branch2b.norm.bias", "res_layers.2.blocks.0.branch2b.norm.running_mean", "res_layers.2.blocks.0.branch2b.norm.running_var", "res_layers.2.blocks.0.branch2c.conv.weight", "res_layers.2.blocks.0.branch2c.norm.weight", "res_layers.2.blocks.0.branch2c.norm.bias", "res_layers.2.blocks.0.branch2c.norm.running_mean", "res_layers.2.blocks.0.branch2c.norm.running_var", "res_layers.2.blocks.0.short.conv.conv.weight", "res_layers.2.blocks.0.short.conv.norm.weight", "res_layers.2.blocks.0.short.conv.norm.bias", "res_layers.2.blocks.0.short.conv.norm.running_mean", "res_layers.2.blocks.0.short.conv.norm.running_var", "res_layers.2.blocks.1.branch2a.conv.weight", "res_layers.2.blocks.1.branch2a.norm.weight", "res_layers.2.blocks.1.branch2a.norm.bias", "res_layers.2.blocks.1.branch2a.norm.running_mean", "res_layers.2.blocks.1.branch2a.norm.running_var", "res_layers.2.blocks.1.branch2b.conv.weight", "res_layers.2.blocks.1.branch2b.norm.weight", "res_layers.2.blocks.1.branch2b.norm.bias", "res_layers.2.blocks.1.branch2b.norm.running_mean", "res_layers.2.blocks.1.branch2b.norm.running_var", "res_layers.2.blocks.1.branch2c.conv.weight", "res_layers.2.blocks.1.branch2c.norm.weight", "res_layers.2.blocks.1.branch2c.norm.bias", "res_layers.2.blocks.1.branch2c.norm.running_mean", "res_layers.2.blocks.1.branch2c.norm.running_var", "res_layers.2.blocks.2.branch2a.conv.weight", "res_layers.2.blocks.2.branch2a.norm.weight", "res_layers.2.blocks.2.branch2a.norm.bias", "res_layers.2.blocks.2.branch2a.norm.running_mean", "res_layers.2.blocks.2.branch2a.norm.running_var", "res_layers.2.blocks.2.branch2b.conv.weight", "res_layers.2.blocks.2.branch2b.norm.weight", "res_layers.2.blocks.2.branch2b.norm.bias", "res_layers.2.blocks.2.branch2b.norm.running_mean", "res_layers.2.blocks.2.branch2b.norm.running_var", "res_layers.2.blocks.2.branch2c.conv.weight", "res_layers.2.blocks.2.branch2c.norm.weight", "res_layers.2.blocks.2.branch2c.norm.bias", "res_layers.2.blocks.2.branch2c.norm.running_mean", "res_layers.2.blocks.2.branch2c.norm.running_var", "res_layers.2.blocks.3.branch2a.conv.weight", "res_layers.2.blocks.3.branch2a.norm.weight", "res_layers.2.blocks.3.branch2a.norm.bias", "res_layers.2.blocks.3.branch2a.norm.running_mean", "res_layers.2.blocks.3.branch2a.norm.running_var", "res_layers.2.blocks.3.branch2b.conv.weight", "res_layers.2.blocks.3.branch2b.norm.weight", "res_layers.2.blocks.3.branch2b.norm.bias", "res_layers.2.blocks.3.branch2b.norm.running_mean", "res_layers.2.blocks.3.branch2b.norm.running_var", "res_layers.2.blocks.3.branch2c.conv.weight", "res_layers.2.blocks.3.branch2c.norm.weight", "res_layers.2.blocks.3.branch2c.norm.bias", "res_layers.2.blocks.3.branch2c.norm.running_mean", "res_layers.2.blocks.3.branch2c.norm.running_var", "res_layers.2.blocks.4.branch2a.conv.weight", "res_layers.2.blocks.4.branch2a.norm.weight", "res_layers.2.blocks.4.branch2a.norm.bias", "res_layers.2.blocks.4.branch2a.norm.running_mean", "res_layers.2.blocks.4.branch2a.norm.running_var", "res_layers.2.blocks.4.branch2b.conv.weight", "res_layers.2.blocks.4.branch2b.norm.weight", "res_layers.2.blocks.4.branch2b.norm.bias", "res_layers.2.blocks.4.branch2b.norm.running_mean", "res_layers.2.blocks.4.branch2b.norm.running_var", "res_layers.2.blocks.4.branch2c.conv.weight", "res_layers.2.blocks.4.branch2c.norm.weight", "res_layers.2.blocks.4.branch2c.norm.bias", "res_layers.2.blocks.4.branch2c.norm.running_mean", "res_layers.2.blocks.4.branch2c.norm.running_var", "res_layers.2.blocks.5.branch2a.conv.weight", "res_layers.2.blocks.5.branch2a.norm.weight", "res_layers.2.blocks.5.branch2a.norm.bias", "res_layers.2.blocks.5.branch2a.norm.running_mean", "res_layers.2.blocks.5.branch2a.norm.running_var", "res_layers.2.blocks.5.branch2b.conv.weight", "res_layers.2.blocks.5.branch2b.norm.weight", "res_layers.2.blocks.5.branch2b.norm.bias", "res_layers.2.blocks.5.branch2b.norm.running_mean", "res_layers.2.blocks.5.branch2b.norm.running_var", "res_layers.2.blocks.5.branch2c.conv.weight", "res_layers.2.blocks.5.branch2c.norm.weight", "res_layers.2.blocks.5.branch2c.norm.bias", "res_layers.2.blocks.5.branch2c.norm.running_mean", "res_layers.2.blocks.5.branch2c.norm.running_var", "res_layers.3.blocks.0.branch2a.conv.weight", "res_layers.3.blocks.0.branch2a.norm.weight", "res_layers.3.blocks.0.branch2a.norm.bias", "res_layers.3.blocks.0.branch2a.norm.running_mean", "res_layers.3.blocks.0.branch2a.norm.running_var", "res_layers.3.blocks.0.branch2b.conv.weight", "res_layers.3.blocks.0.branch2b.norm.weight", "res_layers.3.blocks.0.branch2b.norm.bias", "res_layers.3.blocks.0.branch2b.norm.running_mean", "res_layers.3.blocks.0.branch2b.norm.running_var", "res_layers.3.blocks.0.branch2c.conv.weight", "res_layers.3.blocks.0.branch2c.norm.weight", "res_layers.3.blocks.0.branch2c.norm.bias", "res_layers.3.blocks.0.branch2c.norm.running_mean", "res_layers.3.blocks.0.branch2c.norm.running_var", "res_layers.3.blocks.0.short.conv.conv.weight", "res_layers.3.blocks.0.short.conv.norm.weight", "res_layers.3.blocks.0.short.conv.norm.bias", "res_layers.3.blocks.0.short.conv.norm.running_mean", "res_layers.3.blocks.0.short.conv.norm.running_var", "res_layers.3.blocks.1.branch2a.conv.weight", "res_layers.3.blocks.1.branch2a.norm.weight", "res_layers.3.blocks.1.branch2a.norm.bias", "res_layers.3.blocks.1.branch2a.norm.running_mean", "res_layers.3.blocks.1.branch2a.norm.running_var", "res_layers.3.blocks.1.branch2b.conv.weight", "res_layers.3.blocks.1.branch2b.norm.weight", "res_layers.3.blocks.1.branch2b.norm.bias", "res_layers.3.blocks.1.branch2b.norm.running_mean", "res_layers.3.blocks.1.branch2b.norm.running_var", "res_layers.3.blocks.1.branch2c.conv.weight", "res_layers.3.blocks.1.branch2c.norm.weight", "res_layers.3.blocks.1.branch2c.norm.bias", "res_layers.3.blocks.1.branch2c.norm.running_mean", "res_layers.3.blocks.1.branch2c.norm.running_var", "res_layers.3.blocks.2.branch2a.conv.weight", "res_layers.3.blocks.2.branch2a.norm.weight", "res_layers.3.blocks.2.branch2a.norm.bias", "res_layers.3.blocks.2.branch2a.norm.running_mean", "res_layers.3.blocks.2.branch2a.norm.running_var", "res_layers.3.blocks.2.branch2b.conv.weight", "res_layers.3.blocks.2.branch2b.norm.weight", "res_layers.3.blocks.2.branch2b.norm.bias", "res_layers.3.blocks.2.branch2b.norm.running_mean", "res_layers.3.blocks.2.branch2b.norm.running_var", "res_layers.3.blocks.2.branch2c.conv.weight", "res_layers.3.blocks.2.branch2c.norm.weight", "res_layers.3.blocks.2.branch2c.norm.bias", "res_layers.3.blocks.2.branch2c.norm.running_mean", "res_layers.3.blocks.2.branch2c.norm.running_var". Unexpected key(s) in state_dict: "ema".跑rt-detr官方代码报错
08-02
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