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diff --git a/src/main/java/org/apache/commons/math3/ml/neuralnet/FeatureInitializerFactory.java b/src/main/java/org/apache/commons/math3/ml/neuralnet/FeatureInitializerFactory.java
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+/*
+ * 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.math3.ml.neuralnet;
+
+import org.apache.commons.math3.distribution.RealDistribution;
+import org.apache.commons.math3.distribution.UniformRealDistribution;
+import org.apache.commons.math3.analysis.UnivariateFunction;
+import org.apache.commons.math3.analysis.function.Constant;
+import org.apache.commons.math3.random.RandomGenerator;
+
+/**
+ * Creates functions that will select the initial values of a neuron's
+ * features.
+ *
+ * @since 3.3
+ */
+public class FeatureInitializerFactory {
+    /** Class contains only static methods. */
+    private FeatureInitializerFactory() {}
+
+    /**
+     * Uniform sampling of the given range.
+     *
+     * @param min Lower bound of the range.
+     * @param max Upper bound of the range.
+     * @param rng Random number generator used to draw samples from a
+     * uniform distribution.
+     * @return an initializer such that the features will be initialized with
+     * values within the given range.
+     * @throws org.apache.commons.math3.exception.NumberIsTooLargeException
+     * if {@code min >= max}.
+     */
+    public static FeatureInitializer uniform(final RandomGenerator rng,
+                                             final double min,
+                                             final double max) {
+        return randomize(new UniformRealDistribution(rng, min, max),
+                         function(new Constant(0), 0, 0));
+    }
+
+    /**
+     * Uniform sampling of the given range.
+     *
+     * @param min Lower bound of the range.
+     * @param max Upper bound of the range.
+     * @return an initializer such that the features will be initialized with
+     * values within the given range.
+     * @throws org.apache.commons.math3.exception.NumberIsTooLargeException
+     * if {@code min >= max}.
+     */
+    public static FeatureInitializer uniform(final double min,
+                                             final double max) {
+        return randomize(new UniformRealDistribution(min, max),
+                         function(new Constant(0), 0, 0));
+    }
+
+    /**
+     * Creates an initializer from a univariate function {@code f(x)}.
+     * The argument {@code x} is set to {@code init} at the first call
+     * and will be incremented at each call.
+     *
+     * @param f Function.
+     * @param init Initial value.
+     * @param inc Increment
+     * @return the initializer.
+     */
+    public static FeatureInitializer function(final UnivariateFunction f,
+                                              final double init,
+                                              final double inc) {
+        return new FeatureInitializer() {
+            /** Argument. */
+            private double arg = init;
+
+            /** {@inheritDoc} */
+            public double value() {
+                final double result = f.value(arg);
+                arg += inc;
+                return result;
+            }
+        };
+    }
+
+    /**
+     * Adds some amount of random data to the given initializer.
+     *
+     * @param random Random variable distribution.
+     * @param orig Original initializer.
+     * @return an initializer whose {@link FeatureInitializer#value() value}
+     * method will return {@code orig.value() + random.sample()}.
+     */
+    public static FeatureInitializer randomize(final RealDistribution random,
+                                               final FeatureInitializer orig) {
+        return new FeatureInitializer() {
+            /** {@inheritDoc} */
+            public double value() {
+                return orig.value() + random.sample();
+            }
+        };
+    }
+}