org.apache.commons.math3.ml.neuralnet

## Class FeatureInitializerFactory

- java.lang.Object
- - org.apache.commons.math3.ml.neuralnet.FeatureInitializerFactory

-     public class FeatureInitializerFactory
      extends Object

  Creates functions that will select the initial values of a neuron's features.
  Since:
    3.3

- - ### Method Summary

    Methods
    Modifier and Type | Method and Description
    --- | ---
    `static FeatureInitializer` | `function(UnivariateFunction f, double init, double inc)` Creates an initializer from a univariate function `f(x)`.
    `static FeatureInitializer` | `randomize(RealDistribution random, FeatureInitializer orig)` Adds some amount of random data to the given initializer.
    `static FeatureInitializer` | `uniform(double min, double max)` Uniform sampling of the given range.
    `static FeatureInitializer` | `uniform(RandomGenerator rng, double min, double max)` Uniform sampling of the given range.

    - ### Methods inherited from class java.lang.Object

      `clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`

- - ### Method Detail

    - #### uniform

          public static FeatureInitializer uniform(RandomGenerator rng,
                                   double min,
                                   double max)

      Uniform sampling of the given range.
      Parameters:
        `min` - Lower bound of the range.
        `max` - Upper bound of the range.
        `rng` - Random number generator used to draw samples from a uniform distribution.
      Returns:
        an initializer such that the features will be initialized with values within the given range.
      Throws:
        `NumberIsTooLargeException` - if `min >= max`.

    - #### uniform

          public static FeatureInitializer uniform(double min,
                                   double max)

      Uniform sampling of the given range.
      Parameters:
        `min` - Lower bound of the range.
        `max` - Upper bound of the range.
      Returns:
        an initializer such that the features will be initialized with values within the given range.
      Throws:
        `NumberIsTooLargeException` - if `min >= max`.

    - #### function

          public static FeatureInitializer function(UnivariateFunction f,
                                    double init,
                                    double inc)

      Creates an initializer from a univariate function `f(x)`. The argument `x` is set to `init` at the first call and will be incremented at each call.
      Parameters:
        `f` - Function.
        `init` - Initial value.
        `inc` - Increment
      Returns:
        the initializer.

    - #### randomize

          public static FeatureInitializer randomize(RealDistribution random,
                                     FeatureInitializer orig)

      Adds some amount of random data to the given initializer.
      Parameters:
        `random` - Random variable distribution.
        `orig` - Original initializer.
      Returns:
        an initializer whose `value` method will return `orig.value() + random.sample()`.

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