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A Java based Neuron Modeling framework
There is a newer version: 2.3.3
package model.MARK_II.parameters;

import model.MARK_II.ColumnPosition;

import java.util.Set;

/**
 * Uses the equation found here https://github.com/quinnliu/WalnutiQ/issues/21
 * for computating a score for the SDR created by spatial pooling.
 *  
 * Input from constructor: set of active column positions produced by spatial
 * pooling.
 *  
 * Output from calling public method: a score where the larger the score
 * represents a better sparse distributed representation.
 *
 * @author Quinn Liu ([email protected])
 * @version Apr 15, 2014
 */
public class SDRScoreCalculator {
    private Set columnActivityAfterSeeingImage;
    private double desiredPercentageOfActiveColumns;
    private int totalNumberOfColumnsInRegion;

    /**
     * Computes the SDR score for the given set of column positions.
     *
     * @param columnActivityAfterSeeingImage
     * @param desiredPercentageOfActiveColumns
     * @param totalNumberOfColumnsInRegion
     */
    public SDRScoreCalculator(
            Set columnActivityAfterSeeingImage,
            double desiredPercentageOfActiveColumns,
            int totalNumberOfColumnsInRegion) {
        this.columnActivityAfterSeeingImage = columnActivityAfterSeeingImage;
        this.desiredPercentageOfActiveColumns = desiredPercentageOfActiveColumns;
        this.totalNumberOfColumnsInRegion = totalNumberOfColumnsInRegion;
    }

    public void updateParameters(
            Set columnActivityAfterSeeingImage,
            double desiredPercentageOfActiveColumns,
            int totalNumberOfColumnsInRegion) {
        this.columnActivityAfterSeeingImage = columnActivityAfterSeeingImage;
        this.desiredPercentageOfActiveColumns = desiredPercentageOfActiveColumns;
        this.totalNumberOfColumnsInRegion = totalNumberOfColumnsInRegion;
    }

    /**
     * Please look here for a visual of the followin sparsityScore formula:
     * https://github.com/quinnliu/WalnutiQ/issues/21
     *
     * @return the sparsity score. The smaller the score the more sparsly
     * distributed the active columns are.
     */
    public double computeSparsityScore() {

        double totalDistanceToNearestActiveColumnForAllActiveColumns = 0.0;
        if (this.columnActivityAfterSeeingImage.size() <= 1) {
            return 0;
        }
        // iterate through all column positions
        for (ColumnPosition currentColumnPosition : this.columnActivityAfterSeeingImage) {
            int row1 = currentColumnPosition.getRow();
            int column1 = currentColumnPosition.getColumn();

            Set columnActivity = this.columnActivityAfterSeeingImage;

            // find nearest active column distance(1 million is a random large
            // number)
            double distanceToNearestActiveColumn = 1000000;
            for (ColumnPosition otherColumnPosition : columnActivity) {
                int row2 = otherColumnPosition.getRow();
                int column2 = otherColumnPosition.getColumn();

                // if you find the same columnPosition as (row1, column1) skip
                if (row1 == row2 && column1 == column2) {
                    continue;
                }

                double distanceBetween_row1column1_row2column2 = Math.sqrt(Math.pow(row1 - row2,
                        2) + Math.pow(column1 - column2, 2));
                if (distanceBetween_row1column1_row2column2 < distanceToNearestActiveColumn) {
                    distanceToNearestActiveColumn = distanceBetween_row1column1_row2column2;
                }
            }

            totalDistanceToNearestActiveColumnForAllActiveColumns += distanceToNearestActiveColumn;
        }

        double averageDistanceToNearestActiveColumn = totalDistanceToNearestActiveColumnForAllActiveColumns
                / columnActivityAfterSeeingImage.size();

        // the best averageDistanceToNearestActiveColumn
        return -averageDistanceToNearestActiveColumn;
    }

    /**
     * @return the number of active columns score. The smaller the score the
     * closer the numberOfActiveColumns was to
     * desiredNumberOfActiveColumns.
     */
    public double computeNumberOfActiveColumnsScore() {
        int desiredNumberOfActiveColumns = (int) (this.totalNumberOfColumnsInRegion
                * this.desiredPercentageOfActiveColumns / 100);

        int actualNumberOfActiveColumns = this.columnActivityAfterSeeingImage
                .size();
        double difference = Math.abs(desiredNumberOfActiveColumns
                - actualNumberOfActiveColumns);


        if (difference < 0.1) {
            difference = 0.1; // 1 / 0.1 = 10 which is a great score
            // as it should be since desired was = to actual
        }

        return -(1 / difference);
    }

    /**
     * @return SDR score computed from given set of active column positions.
     */
    public double computeSDRScore() {
        return this.computeSparsityScore()
                + this.computeNumberOfActiveColumnsScore();
    }
}