CLSS.Types.WeightedSampler
1.1.0
dotnet add package CLSS.Types.WeightedSampler --version 1.1.0
NuGet\Install-Package CLSS.Types.WeightedSampler -Version 1.1.0
<PackageReference Include="CLSS.Types.WeightedSampler" Version="1.1.0" />
paket add CLSS.Types.WeightedSampler --version 1.1.0
#r "nuget: CLSS.Types.WeightedSampler, 1.1.0"
// Install CLSS.Types.WeightedSampler as a Cake Addin
#addin nuget:?package=CLSS.Types.WeightedSampler&version=1.1.0
// Install CLSS.Types.WeightedSampler as a Cake Tool
#tool nuget:?package=CLSS.Types.WeightedSampler&version=1.1.0
CLSS.Types.WeightedSampler
Problem
Sampling a list of elements with weight is a common use case without built-in support in the standard library.
var rng = new System.Random();
var weights = new double[collection.Count];
for (int i = 0; i < weights.Length; ++i)
weights[i] = ConvertElementToWeights(collection[i]);
var weightStages = weights
.Select((w, i) => weights.Take(i + 1).Sum());
var roll = rng.NextDouble() * weights.Sum();
int selectedIndex = 0;
foreach (var ws in weightStages)
{
if (ws > roll) break;
++selectedIndex;
}
Above is a seemingly correct weighted randomization implementation that contains some obvious and non-obvious performance and correctness issues (negative weights are accepted and added to the weight sum). These pitfalls are often overlooked when you have to write weighted randomization on the fly.
Solution
WeightedSampler<T>
is a struct type that encapsulates around an IList<T>
collection to efficiently sample its elements. At factory construction, it takes in a weight selector function with a Func<T, double>
signature.
using CLSS;
using System.Linq;
public struct District
{
public string Name;
public int Population;
}
var districts = new List<District>()
{
new District { Name = "A", Population = 200 },
new District { Name = "B", Population = 600 },
new District { Name = "C", Population = 400 }
};
var districtSampler = WeightedSampler<District>
.From(districts, d => d.Population);
// Distribution test
var samples = new District[12000];
for (int i = 0; i < samples.Length; ++i)
samples[i] = districtSampler.Sample();
Console.WriteLine($"District A: {samples.Count(s => s.Name == "A")}"); // District A: 1987
Console.WriteLine($"District B: {samples.Count(s => s.Name == "B")}"); // District B: 6081
Console.WriteLine($"District C: {samples.Count(s => s.Name == "C")}"); // District C: 3932
The probability of each element being chosen for each roll is its own weight divided by the sum of all the element's weights. If the specified weight selector function returns a negative weight, it will be treated no differently than 0 weight.
WeightedSampler<T>
can also call SampleIndex
to select only the index number, not the weighted element itself.
Usage Notes
- Under the hood,
WeightedSampler<T>
relies on an array that is a snapshot of respective weights (matching by index number) at a point in time. This array is snapshotted once at creation of aWeightedSampler<T>
. If runtime condition causes the source list to mutate or the weights to change, it is necessary to callRefreshWeights
from aWeightedSampler<T>
to continue getting correct sampling results.
districts.Add(new District { Name = "D", Population = 500 });
districtSampler.RefreshWeights(); // source list mutated, taking another snapshot
RefreshWeights
contains an allocation and is intentionally not automatically done. You should be mindful of where to call this.Each sampling call from a
WeightedSampler<T>
instance creates no garbage and is safe to use in hot code path. But be mindful of weight correctness.Advanced Manual Mode: By omitting the weight selector function at construction or leaving it
null
, you can still use the sampling methods ofWeightedSampler<T>
, but you are on your own to ensure the correctness of theWeights
array and theWeightSum
field yourself. Both are modifiable at will. CallingRefreshWeights
while having anull
weight selector will throw an exception.
Internally, this package uses and depends on the DefaultRandom
package in CLSS to save on the allocation of a new System.Random
instance.
Optionally, Sample
and SampleIndex
also take in a System.Random
of your choosing in case you want a custom-seeded random number generator:
using CLSS;
var districtSampler = WeightedSampler<District>
.From(districts, d => d.Population, customrng);
If you are on .NET 6, you can pass in System.Random.Shared
.
GetWeightedRandom
and the WeightedSampler<T>
type fulfill similar roles. They have their own trade-offs. The table below compares their key differences:
Factors | GetWeightedRandom |
WeightedSampler<T> |
---|---|---|
Memory allocation per invocation | 1 array equal in length to source list. | No allocation. |
Syntax | Extension method, called directly from IList<T> types. |
Wrapper struct around a list. |
Reflect changes | All list and member mutations are reflected. | Changes in element weights and list mutations are not reflected until manually refreshed. |
Note: GetWeightedRandom
works on all types implementing the IList<T>
interface, including raw C# array.
This package is a part of the C# Language Syntactic Sugar suite.
Product | Versions Compatible and additional computed target framework versions. |
---|---|
.NET | net5.0 was computed. net5.0-windows was computed. net6.0 was computed. net6.0-android was computed. net6.0-ios was computed. net6.0-maccatalyst was computed. net6.0-macos was computed. net6.0-tvos was computed. net6.0-windows was computed. net7.0 was computed. net7.0-android was computed. net7.0-ios was computed. net7.0-maccatalyst was computed. net7.0-macos was computed. net7.0-tvos was computed. net7.0-windows was computed. net8.0 was computed. net8.0-android was computed. net8.0-browser was computed. net8.0-ios was computed. net8.0-maccatalyst was computed. net8.0-macos was computed. net8.0-tvos was computed. net8.0-windows was computed. |
.NET Core | netcoreapp1.0 was computed. netcoreapp1.1 was computed. netcoreapp2.0 was computed. netcoreapp2.1 was computed. netcoreapp2.2 was computed. netcoreapp3.0 was computed. netcoreapp3.1 was computed. |
.NET Standard | netstandard1.0 is compatible. netstandard1.1 was computed. netstandard1.2 was computed. netstandard1.3 was computed. netstandard1.4 was computed. netstandard1.5 was computed. netstandard1.6 was computed. netstandard2.0 is compatible. netstandard2.1 was computed. |
.NET Framework | net45 was computed. net451 was computed. net452 was computed. net46 was computed. net461 was computed. net462 was computed. net463 was computed. net47 was computed. net471 was computed. net472 was computed. net48 was computed. net481 was computed. |
MonoAndroid | monoandroid was computed. |
MonoMac | monomac was computed. |
MonoTouch | monotouch was computed. |
Tizen | tizen30 was computed. tizen40 was computed. tizen60 was computed. |
Universal Windows Platform | uap was computed. uap10.0 was computed. |
Windows Phone | wp8 was computed. wp81 was computed. wpa81 was computed. |
Windows Store | netcore was computed. netcore45 was computed. netcore451 was computed. |
Xamarin.iOS | xamarinios was computed. |
Xamarin.Mac | xamarinmac was computed. |
Xamarin.TVOS | xamarintvos was computed. |
Xamarin.WatchOS | xamarinwatchos was computed. |
-
.NETStandard 1.0
- CLSS.Constants.DefaultRandom (>= 1.2.0)
- NETStandard.Library (>= 1.6.1)
-
.NETStandard 2.0
- CLSS.Constants.DefaultRandom (>= 1.2.0)
NuGet packages
This package is not used by any NuGet packages.
GitHub repositories
This package is not used by any popular GitHub repositories.
- Automatically use the latest CLSS dependency version.