Hard-core programmers operating | exploit code program to create a new virus propagation model crown

Bie crazy? In the family home, not go? I want to breathe out of?

Do not!

While you are numb to the epidemic, the feel okay.

But the reality is not the case, the fight against SARS fighting continues, has not yet reached Zhong Nanshan and other experts said the outbreak period.
If you go out now and if, really in epidemic prevention and control chaos!

Do not believe? A programmer, a computer simulation of the night to create a program to simulate the new crown spread of the virus in the B station has reached millions of players:

video link:

https://www.bilibili.com/video/av86478875

It tells us:

If we go out shopping, to meet us, it is more and more difficult to control the epidemic situation.

In this paper, the B station emulator hot in-depth interpretation of:

A, JFrame layout panel assembly

Second, the mathematical concepts: Gaussian (normal) distribution

Why talk about the Gaussian distribution?

Spread of the virus code is the most essential part, the most exciting part of the code is the best embodies the simulation of the Gaussian distribution, as far as possible in this chapter also the most simple words for everyone brief look at the theoretical Gaussian distribution.

2.1 Gaussian distribution concept

A very common continuous probability distributions. Normal distribution statistically very important, often used in natural and social sciences to represent an unknown random variable.

For example, say a person's weight, height, age, the prevalence of a disease, the urban population distribution, basically in line with statistical Gaussian distribution. Gaussian distribution formula is as follows:

Throughout the Gauss formula, there are two very important parameters: μ (may homonym for "absurd"), σ (pronounced sigma); then to explain to everybody what is the average μ, σ is the standard difference.

标准差决定了整个样本数据的分布密度，标准差越小，数据越集中，如下图非常直观的描述的μ与σ的关系，以及σ对整个概率分布的影响。

另外，如果μ=0, σ=1的时候，就称之为标准高斯分布。而X轴落在整个⾼斯曲线内的值，专业的说法为服从高斯分布。

2.2 正态变量的标准化

正态变量的标准化是高斯公式的一个重要推导，这里直接给出结论：

v 为服从高斯分布的数据

σ 为标准差

μ 为平均值

W 对v进行标准化处理后的数据，依然是服从高斯分布的

正态变量的标准化公式：

W = (V - μ) / σ

那么V值的计算公式为：

V = W * σ + μ

2.3 高斯分布在Java中的应用

java.util.Random函数有个方法叫做nextGaussian()函数，定义如下：

/*

* @return the next pseudorandom, Gaussian ("normally") distributed

* {@code double} value with mean {@code 0.0} and

* standard deviation {@code 1.0} from this random number

* generator's sequence

*/

synchronized public double nextGaussian() {

// See Knuth, ACP, Section 3.4.1 Algorithm C. if (haveNextNextGaussian) {

haveNextNextGaussian = false; return nextNextGaussian;

} else {


double v1, v2, s;

do {

v1 = 2 * nextDouble() - 1; // between -1 and 1

v2 = 2 * nextDouble() - 1; // between -1 and 1 s = v1 * v1 + v2 * v2;

} while (s >= 1 || s == 0);

double multiplier = StrictMath.sqrt(-2 * StrictMath.log(s)/s); nextNextGaussian = v2 * multiplier;

haveNextNextGaussian = true;

return v1 * multiplier;

}

}

根据API的描述：返回一个double类型的值，这个值服从均值为0，均方差为1的标准正态分布。

然后根据正态变量的标准化推导公式：、

double value = sigma * new Random().nextGaussian() + 0.99;

三、核心代码解读

3.1启动类函数：

3.2画布相关代码

初始化画布：

如上图所示的MyPanel 类实现了Runnable接口：

重写的run方法如下：

如上图所示的MyPanel.this.repaint()方法为一个钩子函数，会调用MyPanel类中法paint方法，paint方法会重新设置人员的状态，数据的变更，医院的床位等。

在paint方法中会调用这个person.update()方法，这个方法至关重要。在后续有一节专门介绍。

3.3初始化感染人员

代码中会看到PersonPool这个类，这个类中有人员池这样一个个静态变量，在加载时候去初始化城市人口：

PersonPool的构造函数：

3.4Person类

Person constructor:

For easy understanding, this figure is given below:

realized Person class wantMove Method:

implement distance Person class method to be infected is determined whether:

action methods 3.5Person

The method of action is an essential method, it is proposed as a separate section, which determines the coordinates of the mobile user, as follows:

/**

* 不同状态下的单个人实例运动行为

*/

private void action() {

if (state == State.FREEZE || state == State.DEATH) { return;

//如果处于隔离或者死亡状态，则无发行动

}

if (!wantMove()) {

//如果不想移动

return;

}

//存在流动意愿的，将进人流动，流动位移仍然遵循标准正态分布

  if (moveTarget == null || moveTarget.isArrived()) {

// 如果人员没有目标的话，可能就是在家里呆烦了，他又想出⻔，那就在其目前移动的位置在随机移动


double targetX = targetSig * new Random().nextGaussian() + targetXU; double targetY = targetSig * new Random().nextGaussian() + targetYU;

// 最终想要到达的目的地

moveTarget = new MoveTarget((int) targetX, (int) targetY);

}

//计算运动位移

int dX = moveTarget.getX() - x; int dY = moveTarget.getY() - y;

// 勾股定理

double length = Math.sqrt(Math.pow(dX, 2) + Math.pow(dY, 2));//

与目标点的距离

if (length < 1) {

//判断是否到达目标点

moveTarget.setArrived(true); return;

}

/**

* 如果没有到达目标，一步一步的走，根据坐标方向，如果为正方向，就往前移动

1,

* 如果坐标轴的反方向，就移动

-1

* udx的结果只可能为两种三种情况：

-1 1 0

*/

int udX = (int) (dX / length); 

//x轴移动步，符号为沿x轴前进方向

if (udX == 0 && dX != 0) {

if (dX > 0) {

udX = 1;

} else { udX = -1;

}

}

int udY = (int) (dY / length);

//y轴移动步，符号为沿x轴前进方向

if (udY == 0 && dY != 0) {

if (dY > 0) { udY = 1;

} else { udY = -1;

}

} 

// 如果超过边界，就往回走

if (x > 700) {

//这个700也许是x方向边界的意思，因为画布大小

1000x800

//TODO:如果是边界那么似乎边界判断还差一个y方向

moveTarget = null;

if (udX > 0) { udX = -udX;

}

}

update method 3.6Person

The update method is a method essential, it is proposed as a separate section, which decides how to handle the decision depending on the state of the user, as follows:

/**

* 对各种状态的人进行不同的处理

*/

public void update() {

//@TODO找时间改为状态机

if (state == State.FREEZE || state == State.DEATH) { return;

//如果已经隔离或者死亡了，就不需要处理了

}

//处理已经确诊的感染者（即患者）

if (state == State.CONFIRMED && dieMoment == 0) {

int destiny = new Random().nextInt(10000)+1;

//命运数字[1,10000]随机数

if (1 <= destiny && destiny <= (int)(Constants.FATALITY_RATE * 10000))

{

//如果命运数字落在死亡区间

int dieTime = (int) (Constants.DIE_VARIANCE * new Random().nextGaussian()+Constants.DIE_TIME);

dieMoment = confirmedTime + dieTime;

//发病后确定死亡时刻

//System.out.printf("%d,%f,%d\n",destiny,Constants.FATALITY_RATE * 10000,dieTime);

}

else {

dieMoment = -1;

//逃过了死神的魔手

}

}

//*/

if (state == State.CONFIRMED && MyPanel.worldTime - confirmedTime >= Constants.HOSPITAL_RECEIVE_TIME) {

//如果患者已经确诊，且（世界时刻-确诊时刻）大于医院响应时间，即医院准备好病床了，可以抬走了


Bed bed = Hospital.getInstance().pickBed();

//查找空床位

if (bed == null) {

//没有床位了

// System.out.println("隔离区没有空床位");

} else {

//安置病人

state = State.FREEZE; x = bed.getX();

y = bed.getY(); bed.setEmpty(false);

}

}

//处理病死者

if((state == State.CONFIRMED || state == State.FREEZE )&& MyPanel.worldTime >= dieMoment && dieMoment > 0) {

state = State.DEATH;

//患者死亡

}

//处理发病的潜伏期感染者

if (MyPanel.worldTime - infectedTime > Constants.SHADOW_TIME && state == State.SHADOW) {

state = State.CONFIRMED;

//潜伏者发病

confirmedTime = MyPanel.worldTime;

//刷新时间

}

//处理病死者

if((state == State.CONFIRMED || state == State.FREEZE )&& MyPanel.worldTime >= dieMoment && dieMoment > 0) {

state = State.DEATH;

//患者死亡

}

//处理发病的潜伏期感染者

if (MyPanel.worldTime - infectedTime > Constants.SHADOW_TIME && state == State.SHADOW) {

state = State.CONFIRMED;

//潜伏者发病

confirmedTime = MyPanel.worldTime;

//刷新时间

}

//处理未隔离者的移动问题

action();

//处理健康⼈被感染的问题

List<Person> people = PersonPool.getInstance().personList; if (state >= State.SHADOW) {

return;

}

// 循环判断该⽤户是否会被其他人感染

for (Person person : people) {

//如果其他⼈为健康的，就继续判断下一个人

if (person.getState() == State.NORMAL) {

continue;

}

// 随机生成一个值

float random = new Random().nextFloat();

// 如果概率大于感染概率，并且小于安全距离，那么当前这个人肯定会被感染

if (random > Constants.BROAD_RATE && distance(person) < SAFE_DIST) { this.beInfected();

// 如果被感染了，就继续判断下一个

break;

}

}
}

Finally, I hope that we can more patience, good health is most important. And other epidemics in the past, then Sahuan children play ~