Apache Commons Math 3.6.1 · org.apache.commons.math3.ml.neuralnet.FeatureInitializerFactory

Access	Constructor and Description
private	FeatureInitializerFactory() Class contains only static methods.

Method Summary

Modifier and Type	Method and Description
public static FeatureInitializer	Returns: the initializer. function(final UnivariateFunction Function. f, final double Initial value. init, final double Increment inc) Creates an initializer from a univariate function `f(x)`.
public static FeatureInitializer	Returns: an initializer whose `value` method will return `orig.value() + random.sample()`. randomize(final RealDistribution Random variable distribution. random, final FeatureInitializer Original initializer. orig) Adds some amount of random data to the given initializer.
public static FeatureInitializer	Returns: an initializer such that the features will be initialized with values within the given range. uniform(final RandomGenerator Random number generator used to draw samples from a uniform distribution. rng, final double Lower bound of the range. min, final double Upper bound of the range. max) Uniform sampling of the given range.
public static FeatureInitializer	Returns: an initializer such that the features will be initialized with values within the given range. uniform(final double Lower bound of the range. min, final double Upper bound of the range. max) Uniform sampling of the given range.

Inherited from java.lang.Object:: clone equals finalize getClass hashCode notify notifyAll toString wait wait wait

FeatureInitializerFactory	back to summary
private FeatureInitializerFactory() Class contains only static methods.

function back to summary

function	back to summary
public static FeatureInitializer function(final UnivariateFunction f, final double init, final 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:UnivariateFunction Function. init:double Initial value. inc:double Increment Returns:FeatureInitializer the initializer.

public static FeatureInitializer function(final UnivariateFunction f, final double init, final 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

the initializer.

randomize back to summary

randomize	back to summary
public static FeatureInitializer randomize(final RealDistribution random, final FeatureInitializer orig) Adds some amount of random data to the given initializer. Parameters random:RealDistribution Random variable distribution. orig:FeatureInitializer Original initializer. Returns:FeatureInitializer an initializer whose `value` method will return `orig.value() + random.sample()`.

public static FeatureInitializer randomize(final RealDistribution random, final FeatureInitializer orig)

Adds some amount of random data to the given initializer.

Parameters

an initializer whose value method will return orig.value() + random.sample().

uniform back to summary

uniform	back to summary
public static FeatureInitializer uniform(final RandomGenerator rng, final double min, final double max) Uniform sampling of the given range. Parameters rng:RandomGenerator Random number generator used to draw samples from a uniform distribution. min:double Lower bound of the range. max:double Upper bound of the range. Returns:FeatureInitializer an initializer such that the features will be initialized with values within the given range. Exceptions NumberIsTooLargeException: if `min >= max`.

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

Uniform sampling of the given range.

Parameters

rng:RandomGenerator: Random number generator used to draw samples from a uniform distribution.
min:double: Lower bound of the range.
max:double: Upper bound of the range.

an initializer such that the features will be initialized with values within the given range.

Exceptions

uniform back to summary

uniform	back to summary
public static FeatureInitializer uniform(final double min, final double max) Uniform sampling of the given range. Parameters min:double Lower bound of the range. max:double Upper bound of the range. Returns:FeatureInitializer an initializer such that the features will be initialized with values within the given range. Exceptions NumberIsTooLargeException: if `min >= max`.

public static FeatureInitializer uniform(final double min, final double max)

Uniform sampling of the given range.

Parameters

an initializer such that the features will be initialized with values within the given range.

Exceptions