package libai.nn.unsupervised;

import libai.common.Precondition;
import libai.common.matrix.Column;
import libai.nn.NeuralNetwork;

import java.util.Random;

 * Created by kronenthaler on 19/03/2017.
public abstract class UnsupervisedLearning extends NeuralNetwork {

    public UnsupervisedLearning() {

    public UnsupervisedLearning(Random rand) {

     * Trains this neural network with the list of {@code patterns} and the
     * expected {@code answers}.
     * <p>
     * Use the learning rate {@code alpha} for many {@code epochs}. Take
     * {@code length} patterns from the position {@code offset}.</p>
     * <p>
     * {@code patterns} must be array of non-{@code null} <b>column</b>
     * matrices</p>
     * @param patterns The patterns to be learned.
     * @param alpha The learning rate.
     * @param epochs The maximum number of iterations
     * @param offset The first pattern position
     * @param length How many patterns will be used.
    public abstract void train(Column[] patterns, double alpha, int epochs, int offset, int length);

    public void train(Column[] patterns, Column[] answers, double alpha, int epochs, int offset, int length, double minerror) {
        train(patterns, alpha, epochs, offset, length);

    protected void validatePreconditions(Column[] patterns, int epochs, int offset, int length) {
        Precondition.check(offset >= 0 && offset < patterns.length, "offset must be in the interval [0, %d), found,  %d", patterns.length, offset);
        Precondition.check(length >= 0 && length <= patterns.length - offset, "length must be in the interval (0, %d], found,  %d", patterns.length - offset, length);
        Precondition.check(epochs > 0, "The number of epochs must be a positive non zero integer");