Github user kinow commented on a diff in the pull request:
https://github.com/apache/commons-rng/pull/5#discussion_r143630250
--- Diff:
commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/ZigguratGaussianSampler.java
---
@@ -0,0 +1,152 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.commons.rng.sampling.distribution;
+
+import org.apache.commons.rng.UniformRandomProvider;
+
+/**
+ * Gaussian Sampling by
+ * <a href="https://en.wikipedia.org/wiki/Ziggurat_algorithm";>Ziggurat
algorithm</a>
+ *
+ * <p>Based on
+ * "The Ziggurat Method for Generating Random Variables"<br>
+ * by George Marsaglia and Wai Wan Tsang</p>
+ *
+ * @see <a
href="http://www.jstatsoft.org/article/view/v005i08/ziggurat.pdf";>Ziggurat
Method for Generating Random Variables</a>
+ *
+ * @since 1.1
+ */
+
+public class ZigguratGaussianSampler
+ extends SamplerBase
+ implements NormalizedGaussianSampler {
+
+ /**
+ * Generates values from Gaussian (normal) probability distribution
+ * It uses two tables, integers KN and reals WN. Some 99% of the time,
+ * the required x is produced by:
+ * generate a random 32-bit integer j and let i be the index formed
from
+ * the rightmost 8 bits of j. If j < k_i return x = j * w_i.
+ */
+
+ private static final int[] KN = new int[128];
+ private static final double[] WN = new double[128];
+ private static final double[] FN = new double[128];
+
+ /**
+ * Initialize tables.
+ */
+ static {
+ /**
+ * Filling the tables.
+ * k_0 = 2^32 * r * f(dn) / vn
+ * k_i = 2^32 * ( x_{i-1} / x_i )
+ * w_0 = .5^32 * vn / f(dn)
+ * w_i = .5^32 * x_i
+ * where dn - the rightmost x_i
+ * vn - the area of the rectangle
+ * f(dn) = exp(-.5 * dn * dn)
+ */
+ final double m = 2147483648.0; // 2^31
+
+ /* provides z(r) = 0, where z(r): x_255 = r, vn =
r*f(r)+integral_r^inf f(x)dx */
+ final double vn = 9.91256303526217e-3;
+
+ double dn = 3.442619855899;
+ double tn = dn;
+ double e = Math.exp(-.5 * dn * dn);
+ final double q = vn / e;
+
+ KN[0] = (int) ((dn / q) * m);
+ KN[1] = 0;
+
+ WN[0] = q / m;
+ WN[127] = dn / m;
+
+ FN[0] = 1.0;
+ FN[127] = e;
+
+ for (int i = 126; i >= 1; i--){
+ e = Math.exp(-.5 *dn * dn);
+ dn = Math.sqrt(-2. * Math.log(vn / dn + e));
+ KN[i+1] = (int) ((dn / tn) * m);
+ tn = dn;
+ FN[i] = e;
+ WN[i] = dn / m;
+ }
+ }
+
+ /**
+ * @param rng Generator of uniformly distributed random numbers.
+ */
+ public ZigguratGaussianSampler(UniformRandomProvider rng) {
+ super(rng);
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public double sample() {
+ int j = nextInt();
+ int i = j & 127;
+ return (j < KN[i]) ? j * WN[i] : nfix(j,i);
+ }
+
+ /** get the value from the tail of the distribution
--- End diff --
Normally it's in the next line, starting with upper case. Not really
important though. Some components have user guides, but many point to the
Javadocs as user guide :-) that's why we focus so much on the text that is put
here @cur4so
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