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 *      http://www.apache.org/licenses/LICENSE-2.0
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package org.apache.commons.math3.ml.neuralnet.sofm.util;

import org.apache.commons.math3.exception.NotStrictlyPositiveException;
import org.apache.commons.math3.exception.NumberIsTooLargeException;
import org.apache.commons.math3.util.FastMath;

Exponential decay function: a e-x / b, where x is the (integer) independent variable. 

Class is immutable.
Since:
3.3/**
 * Exponential decay function: <code>a e<sup>-x / b</sup></code>,
 * where {@code x} is the (integer) independent variable.
 * <br/>
 * Class is immutable.
 *
 * @since 3.3
 */
public class ExponentialDecayFunction {
    
Factor a. /** Factor {@code a}. */
    private final double a;
    
Factor 1 / b. /** Factor {@code 1 / b}. */
    private final double oneOverB;

    
Creates an instance. It will be such that

 
a = initValue
 
b = -numCall / ln(valueAtNumCall / initValue)

Params:
initValue – Initial value, i.e. value(0).
valueAtNumCall – Value of the function at numCall.
numCall – Argument for which the function returns valueAtNumCall.
Throws:
NotStrictlyPositiveException – if initValue <= 0.
NotStrictlyPositiveException – if valueAtNumCall <= 0.
NumberIsTooLargeException – if valueAtNumCall >= initValue.
NotStrictlyPositiveException – if numCall <= 0./**
     * Creates an instance. It will be such that
     * <ul>
     *  <li>{@code a = initValue}</li>
     *  <li>{@code b = -numCall / ln(valueAtNumCall / initValue)}</li>
     * </ul>
     *
     * @param initValue Initial value, i.e. {@link #value(long) value(0)}.
     * @param valueAtNumCall Value of the function at {@code numCall}.
     * @param numCall Argument for which the function returns
     * {@code valueAtNumCall}.
     * @throws NotStrictlyPositiveException if {@code initValue <= 0}.
     * @throws NotStrictlyPositiveException if {@code valueAtNumCall <= 0}.
     * @throws NumberIsTooLargeException if {@code valueAtNumCall >= initValue}.
     * @throws NotStrictlyPositiveException if {@code numCall <= 0}.
     */
    public ExponentialDecayFunction(double initValue,
                                    double valueAtNumCall,
                                    long numCall) {
        if (initValue <= 0) {
            throw new NotStrictlyPositiveException(initValue);
        }
        if (valueAtNumCall <= 0) {
            throw new NotStrictlyPositiveException(valueAtNumCall);
        }
        if (valueAtNumCall >= initValue) {
            throw new NumberIsTooLargeException(valueAtNumCall, initValue, false);
        }
        if (numCall <= 0) {
            throw new NotStrictlyPositiveException(numCall);
        }

        a = initValue;
        oneOverB = -FastMath.log(valueAtNumCall / initValue) / numCall;
    }

    
Computes a e-numCall / b.
Params:
numCall – Current step of the training task.
Returns:
the value of the function at numCall./**
     * Computes <code>a e<sup>-numCall / b</sup></code>.
     *
     * @param numCall Current step of the training task.
     * @return the value of the function at {@code numCall}.
     */
    public double value(long numCall) {
        return a * FastMath.exp(-numCall * oneOverB);
    }
}