Verify distribution uniformity/Naive: Difference between revisions
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distribution potentially skewed for 0: expected result around 50000, got 95040</pre> |
distribution potentially skewed for 0: expected result around 50000, got 95040</pre> |
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=={{header|Kotlin}}== |
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<lang scala>// version 1.1.3 |
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import java.util.Random |
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val r = Random() |
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fun dice5() = 1 + r.nextInt(5) |
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fun checkDist(gen: () -> Int, nRepeats: Int, tolerance: Double = 0.5) { |
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val occurs = mutableMapOf<Int, Int>() |
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for (i in 1..nRepeats) { |
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val d = gen() |
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if (occurs.containsKey(d)) |
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occurs[d] = occurs[d]!! + 1 |
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else |
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occurs.put(d, 1) |
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} |
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val expected = (nRepeats.toDouble()/ occurs.size).toInt() |
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val maxError = (expected * tolerance / 100.0).toInt() |
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println("Repetitions = $nRepeats, Expected = $expected") |
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println("Tolerance = $tolerance%, Max Error = $maxError\n") |
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println("Integer Occurrences Error Acceptable") |
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val f = " %d %5d %5d %s" |
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var allAcceptable = true |
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for ((k,v) in occurs.toSortedMap()) { |
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val error = Math.abs(v - expected) |
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val acceptable = if (error <= maxError) "Yes" else "No" |
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if (acceptable == "No") allAcceptable = false |
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println(f.format(k, v, error, acceptable)) |
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} |
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println("\nAcceptable overall: ${if (allAcceptable) "Yes" else "No"}") |
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} |
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fun main(args: Array<String>) { |
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checkDist(::dice5, 1_000_000) |
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println() |
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checkDist(::dice5, 100_000) |
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}</lang> |
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Sample output: |
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<pre> |
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Repetitions = 1000000, Expected = 200000 |
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Tolerance = 0.5%, Max Error = 1000 |
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Integer Occurrences Error Acceptable |
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1 200074 74 Yes |
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2 200497 497 Yes |
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3 199295 705 Yes |
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4 199822 178 Yes |
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5 200312 312 Yes |
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Acceptable overall: Yes |
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Repetitions = 100000, Expected = 20000 |
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Tolerance = 0.5%, Max Error = 100 |
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Integer Occurrences Error Acceptable |
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1 20265 265 No |
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2 20229 229 No |
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3 19836 164 No |
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4 19931 69 Yes |
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5 19739 261 No |
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Acceptable overall: No |
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</pre> |
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=={{header|Liberty BASIC}}== |
=={{header|Liberty BASIC}}== |
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