I Have a list that contains four item (A, B, C, D). Every item has a probability to be chosen. Let's say for example A has 74% of chance to be picked, B 15%, C 7% ,and D 4%.
I want to create a function that choose randomly an item according to its probability.
Any help please?
Relative weights to choose elements from the list with different probability. First, define the probability for each element. If you specified the probability using the relative weight, the selections are made according to the relative weights. You can set relative weights using the weight parameter.
In order to get a random item from a List instance, you need to generate a random index number and then fetch an item by this generated index number using List. get() method.
random. choice() method to choose elements from the list with different probability. Output: Return the numpy array of random samples. Note: parameter p is probabilities associated with each entry in a(1d-array).
Select randomly n elements from a list using choice() The choice() method is used to return a random number from given sequence. The sequence can be a list or a tuple. This returns a single value from available data that considers duplicate values in the sequence(list).
Define a class for your items like this:
class Items<T>
{
public double Probability { get; set; }
public T Item { get; set; }
}
then initialize it
var initial = new List<Items<string>>
{
new Items<string> {Probability = 74 / 100.0, Item = "A"},
new Items<string> {Probability = 15 / 100.0, Item = "B"},
new Items<string> {Probability = 7 / 100.0, Item = "C"},
new Items<string> {Probability = 4 / 100.0, Item = "D"},
};
then you need to convert it to aggregate a sum of probabilities from 0 to 1
var converted = new List<Items<string>>(initial.Count);
var sum = 0.0;
foreach (var item in initial.Take(initial.Count - 1))
{
sum += item.Probability;
converted.Add(new Items<string> {Probability = sum, Item = item.Item});
}
converted.Add(new Items<string> {Probability = 1.0, Item = initial.Last().Item});
now you can pick an item from converted
collection with respect to probability:
var rnd = new Random();
while (true)
{
var probability = rnd.NextDouble();
var selected = converted.SkipWhile(i => i.Probability < probability).First();
Console.WriteLine($"Selected item = {selected.Item}");
}
NOTE: my implementation have O(n)
complexity. You can optimize it with binary search (because values in converted
collection are sorted)
My apologies for answering this one like this - I'm kinda viewing it as a sort of "Euler.Net" puzzle, and a way of playing around with Generics.
Anyway, here's my go at it:
public class WeightedItem<T>
{
private T value;
private int weight;
private int cumulativeSum;
private static Random rndInst = new Random();
public WeightedItem(T value, int weight)
{
this.value = value;
this.weight = weight;
}
public static T Choose(List<WeightedItem<T>> items)
{
int cumulSum = 0;
int cnt = items.Count();
for (int slot = 0; slot < cnt; slot++)
{
cumulSum += items[slot].weight;
items[slot].cumulativeSum = cumulSum;
}
double divSpot = rndInst.NextDouble() * cumulSum;
WeightedItem<T> chosen = items.FirstOrDefault(i => i.cumulativeSum >= divSpot);
if (chosen == null) throw new Exception("No item chosen - there seems to be a problem with the probability distribution.");
return chosen.value;
}
}
Usage:
WeightedItem<string> alice = new WeightedItem<string>("alice", 1);
WeightedItem<string> bob = new WeightedItem<string>("bob", 1);
WeightedItem<string> charlie = new WeightedItem<string>("charlie", 1);
WeightedItem<string> diana = new WeightedItem<string>("diana", 4);
WeightedItem<string> elaine = new WeightedItem<string>("elaine", 1);
List<WeightedItem<string>> myList = new List<WeightedItem<string>> { alice, bob, charlie, diana, elaine };
string chosen = WeightedItem<string>.Choose(myList);
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