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    <p>I have played around and I write <a href="http://gist.github.com/105929" rel="nofollow noreferrer">"testing environment"</a> for this Rand(7) algorithm. For example if you want to try what distribution gives your algorithm or how much iterations takes to generate all distinct random values (for Rand(7) 1-7), you <a href="http://gist.github.com/105929" rel="nofollow noreferrer">can use it</a>.</p> <p>My core algorithm is this:</p> <pre><code>return (Rand5() + Rand5()) % 7 + 1; </code></pre> <p>Well is no less uniformly distributed then Adam Rosenfield's one. (<a href="http://gist.github.com/105929" rel="nofollow noreferrer">which I included in my snippet code</a>)</p> <pre><code>private static int Rand7WithRand5() { //PUT YOU FAVOURITE ALGORITHM HERE// //1. Stackoverflow winner int i; do { i = 5 * (Rand5() - 1) + Rand5(); // i is now uniformly random between 1 and 25 } while (i &gt; 21); // i is now uniformly random between 1 and 21 return i % 7 + 1; //My 2 cents //return (Rand5() + Rand5()) % 7 + 1; } </code></pre> <p>This "testing environment" can take any Rand(n) algorithm and test and evaluate it (distribution and speed). Just put your code into the "Rand7WithRand5" method and run the snippet.</p> <p>Few observations:</p> <ul> <li>Adam Rosenfield's algorithm is no better distributed then, for example, mine. Anyway, both algorithms distribution is horrible.</li> <li>Native Rand7 (<code>random.Next(1, 8)</code>) is completed as it generated all members in given interval in around 200+ iterations, Rand7WithRand5 algorithms take order of 10k (around 30-70k)</li> <li>Real challenge is not to write a method to generate Rand(7) from Rand(5), but it generate values more or less uniformly distributed.</li> </ul>
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    1. POExpand a random range from 1–5 to 1–7
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    1. USPeter Stegnar
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    1. CONo, your algorithm does not product a uniform distribution. It produces 1..7 with probabilities 4/25, 3/25, 3/25, 3/25, 3/25, 4/25, 5/25, as can easily be verified by counting all 25 possible outcomes. 25 is not divisible by 7. Your test for uniformity is also flawed -- the number of trials needed to get every number has a complicated distribution, see http://is.gd/wntB . You need to perform your test thousands of times, not once. A better test would be to call the RNG thousands of times and compare the number of occurrences of each outcome.
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      1. USAdam Rosenfield
 

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