alias cd=rm -rf

（如果不懂这个梗，给一个小提示，alias命令是给某个命令重命名，这里把cd命令改成了rm -rf命令，你每次进入目录其实是删除了目录）

https://www.keithschwarz.com/darts-dice-coins/

### 一. 原理

1. 当随机数位于0~0.3时，请求服务器A
2. 当随机数位于0.3~0.5时，请求服务器B
3. 当随机数位于0.5~1时，请求服务器C

T123
PDF0.30.20.5
Prob0.90.61
Alias33NULL

### 二. 代码实现

import java.util.*;

/**
* @description 权重算法
*/
public final class AliasMethod {
/**
* The random number generator used to sample from the distribution.
*/
private final Random random;
/**
* The probability and alias tables.
*/
private final int[] alias;
private final double[] probability;

/**
* Constructs a new AliasMethod to sample from a discrete distribution and
* hand back outcomes based on the probability distribution.
* <p>
* Given as input a list of probabilities corresponding to outcomes 0, 1,
* ..., n - 1, this constructor creates the probability and alias tables
* needed to efficiently sample from this distribution.
*
* @param probabilities The list of probabilities.
*/
public AliasMethod(List<Double> probabilities) {
this(probabilities, new Random());
}

/**
* Constructs a new AliasMethod to sample from a discrete distribution and
* hand back outcomes based on the probability distribution.
* <p>
* Given as input a list of probabilities corresponding to outcomes 0, 1,
* ..., n - 1, along with the random number generator that should be used
* as the underlying generator, this constructor creates the probability
* and alias tables needed to efficiently sample from this distribution.
*
* @param probabilities The list of probabilities.
* @param random        The random number generator
*/
public AliasMethod(List<Double> probabilities, Random random) {
/* Begin by doing basic structural checks on the inputs. */
if (probabilities == null || random == null) {
throw new NullPointerException();
}
if (probabilities.size() == 0) {
throw new IllegalArgumentException("Probability vector must be nonempty.");
}
/* Allocate space for the probability and alias tables. */
probability = new double[probabilities.size()];
alias = new int[probabilities.size()];
/* Store the underlying generator. */
this.random = random;
/* Compute the average probability and cache it for later use. */
final double average = 1.0 / probabilities.size();
/* Make a copy of the probabilities list, since we will be making
* changes to it.
*/
probabilities = new ArrayList<>(probabilities);
/* Create two stacks to act as worklists as we populate the tables. */
Deque<Integer> small = new ArrayDeque<>();
Deque<Integer> large = new ArrayDeque<>();
/* Populate the stacks with the input probabilities. */
for (int i = 0; i < probabilities.size(); ++i) {
/* If the probability is below the average probability, then we add
* it to the small list; otherwise we add it to the large list.
*/
if (probabilities.get(i) >= average) {
} else {
}
}
/* As a note: in the mathematical specification of the algorithm, we
* will always exhaust the small list before the big list. However,
* due to floating point inaccuracies, this is not necessarily true.
* Consequently, this inner loop (which tries to pair small and large
* elements) will have to check that both lists aren't empty.
*/
while (!small.isEmpty() && !large.isEmpty()) {
/* Get the index of the small and the large probabilities. */
int less = small.removeLast();
int more = large.removeLast();
/* These probabilities have not yet been scaled up to be such that
* 1/n is given weight 1.0. We do this here instead.
*/
probability[less] = probabilities.get(less) * probabilities.size();
alias[less] = more;
/* Decrease the probability of the larger one by the appropriate
* amount.
*/
probabilities.set(more,
(probabilities.get(more) + probabilities.get(less)) - average);
/* If the new probability is less than the average, add it into the
* small list; otherwise add it to the large list.
*/
if (probabilities.get(more) >= 1.0 / probabilities.size()) {
} else {
}
}
/* At this point, everything is in one list, which means that the
* remaining probabilities should all be 1/n. Based on this, set them
* appropriately. Due to numerical issues, we can't be sure which
* stack will hold the entries, so we empty both.
*/
while (!small.isEmpty()) {
probability[small.removeLast()] = 1.0;
}
while (!large.isEmpty()) {
probability[large.removeLast()] = 1.0;
}
}

/**
* Samples a value from the underlying distribution.
*
* @return A random value sampled from the underlying distribution.
*/
public int next() {
/* Generate a fair die roll to determine which column to inspect. */
int column = random.nextInt(probability.length);
/* Generate a biased coin toss to determine which option to pick. */
boolean coinToss = random.nextDouble() < probability[column];
/* Based on the outcome, return either the column or its alias. */
return coinToss ? column : alias[column];
}
}


### 三. 最后

Q.E.D.

Talk is cheap, show me the code.