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    <p>You need to weight your results. You can do that with something like this:</p> <pre><code>private int[] _distribution = new int[] { 0, 1, 2, 3, 4, 5, 6, 6, 7, 7, 8, 8, 8, 9, 9 }; Random _r = new Random(); public int GetWeightedRandom() { return _distribution[_r.Next(0, _distribution.Length)]; } </code></pre> <p>If I knew my range was small and consistent, I'd use the table - it's trivial to make it its own class.</p> <p>For completeness, I'll also add this class in. This class borrows from image processing and uses the gamma correction function: a value between 0 and 1 raised to gamma, which returns a value between 0 and 1 but distributed more to the low end if gamma &lt; 1.0 and more to the high end if gamma > 1.0.</p> <pre><code>public class GammaRandom { double _gamma; Random _r; public GammaRandom(double gamma) { if (gamma &lt;= 0) throw new ArgumentOutOfRangeException("gamma"); _gamma = gamma; _r = new Random(); } public int Next(int low, int high) { if (high &lt;= low) throw new ArgumentOutOfRangeException("high"); double rand = _r.NextDouble(); rand = math.Pow(rand, _gamma); return (int)((high - low) * rand) + low; } } </code></pre> <p>(from comments, moved r out of GetWeightedRandom(). Also added range checking to Next())</p> <p>OK, let's really go to town here. I'm channeling John skeet for this - it's an abstract class with a template property that returns a transform function that maps the range [0..1) to [0..1) and scales the random number to that range. I also reimplemented gamma in terms of it and implemented sin and cos as well.</p> <pre><code>public abstract class DelegatedRandom { private Random _r = new Random(); public int Next(int low, int high) { if (high &gt;= low) throw new ArgumentOutOfRangeException("high"); double rand = _r.NextDouble(); rand = Transform(rand); if (rand &gt;= 1.0 || rand &lt; 0) throw new Exception("internal error - expected transform to be between 0 and 1"); return (int)((high - low) * rand) + low; } protected abstract Func&lt;double, double&gt; Transform { get; } } public class SinRandom : DelegatedRandom { private static double pihalf = Math.PI / 2; protected override Func&lt;double, double&gt; Transform { get { return r =&gt; Math.Sin(r * pihalf); } } } public class CosRandom : DelegatedRandom { private static double pihalf = Math.PI / 2; protected override Func&lt;double, double&gt; Transform { get { return r =&gt; Math.Cos(r * pihalf); } } } public class GammaRandom : DelegatedRandom { private double _gamma; public GammaRandom(double gamma) { if (gamma &lt;= 0) throw new ArgumentOutOfRangeException("gamma"); _gamma = gamma; } protected override Func&lt;double, double&gt; Transform { get { return r =&gt; Math.Pow(r, _gamma); } } } </code></pre>
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    1. POspecial random number
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    1. COAh, you beat me to it with this answer. +1.
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      1. USMark Embling
    2. COThis is fine for relatively small numbers, but if you want a distribution centered on 1000000, you need a really big array... I think it would be better to use a distribution function.
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      1. USThomas Levesque
    3. CO8 was an example, in fact we i have :int deep instead of 10, and nothing instead of 8 (i mean it be near to 8 or 7 or 9)
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      1. USVahid.m
 

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