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  1. PO
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    <p>A solution I figured out: Firstly, I generate 100,000,000 perlin noises and store them in an array. I sort it, and afterwards I can take every 10,000 value as a threshold for one per mille. Now I can hardcode these thresholds, so I've just an array with 1,000 floats for lookup at runtime.</p> <p>Advantages:</p> <p>It's <em>really</em> fast, as it's just one array access at runtime.</p> <p>Drawbacks:</p> <p>If you change the algorithm, you have to regenerate your threshold array. Secondly, the mean scales to about 10 per mille, making a 50% threshold either 49.5% or 50.5% (depending on whether you use &lt; or &lt;= comperator). Thirdly, the increased memory footprint (4kb with per mill precision). You can reduce it by using percent precision or a logarithmic precision scale.</p> <p>Generation code:</p> <pre><code>final PerlinNoiseGenerator perlin = new PerlinNoiseGenerator(new Random().nextInt()); final int size = 10000; //Size gets sqared, so it's actually 100,000,000 final float[] values = new float[size * size]; for (int x = 0; x &lt; size; x++) for (int y = 0; y &lt; size; y++) { final float value = perlin.noise2(x / 10f, y / 10f); values[x * size + y] = value; } System.out.println("Calculated"); Arrays.sort(values); System.out.println("Sorted"); final float[] steps = new float[1000]; steps[999] = 1; for (int i = 0; i &lt; 999; i++) steps[i] = values[size * size / 1000 * (i + 1)]; System.out.println("Calculated steps"); for (int i = 0; i &lt; 10; i++) { System.out.println(); for (int j = 0; j &lt; 100; j++) System.out.print(steps[i * 100 + j] + "f, "); //Output usuable for array initialization System.out.println(); System.out.println(); } </code></pre> <p>Lookup code:</p> <pre><code>public final static float[] perlinThresholds = new float[]{}; //Initialize it with the generated thresholds. public static float getThreshold(float percent) { return perlinThresholds[(int)(percent * 1000)]; } public static float getThreshold(int promill) { return perlinThresholds[promill]; } X </code></pre>
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    1. POPerlin Noise to Percentage
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    1. USDiddiZ
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    1. COwould you care to share that resulting array somewhere? Maybe someone can figure out a "good enough" formula for its real cumulative distribution function.
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      1. USAlnitak
    2. COSure: http://pastebin.com/R71w8Ere
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    3. COAs no better solution came up, I accepted my answer
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