| gvSIG desktop 1

/* gvSIG. Geographic Information System of the Valencian Government

 *

 * Copyright (C) 2007-2008 Infrastructures and Transports Department

 * of the Valencian Government (CIT)

 * 

 * This program is free software; you can redistribute it and/or

 * modify it under the terms of the GNU General Public License

 * as published by the Free Software Foundation; either version 2

 * of the License, or (at your option) any later version.

 * 

 * This program is distributed in the hope that it will be useful,

 * but WITHOUT ANY WARRANTY; without even the implied warranty of

 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the

 * GNU General Public License for more details.

 * 

 * You should have received a copy of the GNU General Public License

 * along with this program; if not, write to the Free Software

 * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, 

 * MA  02110-1301, USA.

 * 

 */

/*

 * AUTHORS (In addition to CIT):

 * 2008 Prodevelop S.L  main development

 */

package org.gvsig.data.vectorial.filter;

import java.util.ArrayList;

import java.util.List;

import org.opengis.filter.Filter;

import org.opengis.filter.FilterVisitor;

import org.opengis.filter.expression.Expression;

/**

 * class

 * 

 * 

 * @author <a href="mailto:jsanz@prodevelop.es"> Jorge Gaspar Sanz Salinas</a>

 * @author <a href="mailto:vsanjaime@prodevelop.es"> Vicente Sanjaime Calvet</a>

 * 

 */

public class PropertyDistanceImpl implements PropertyDistance {

    private static final String REGEX_TOKENIZER = "\\s";

    private Expression attribute;

    private String pattern;

    private float threshold;

    public PropertyDistanceImpl(Expression arg0, Expression arg1,

            float threshold) {

        this.attribute = arg0;

        this.pattern = arg1.toString();

        this.threshold = threshold;

    }

    public PropertyDistanceImpl(Expression arg0, String arg1, float threshold) {

        this.attribute = arg0;

        this.pattern = arg1;

        this.threshold = threshold;

    }

    public Object accept(FilterVisitor visitor, Object extraData) {

        return visitor.visitNullFilter(extraData);

    }

    /**

     * Evaluate the text distance between a feature attribute value and

     */

    @SuppressWarnings("unchecked")

    public boolean evaluate(Object feature) {

        if (attribute == null) {

            return false;

        }

        Object value = attribute.evaluate(feature);

        if (null == value) {

            return false;

        }

        String[] valueChains = value.toString().split(REGEX_TOKENIZER);

        String[] pattChains = this.pattern.split(REGEX_TOKENIZER);

        DistanceFilterFactory2Impl fact = new DistanceFilterFactory2Impl();

        /**

         * Really evaluate the filter when we have just one string per input

         */

        if (valueChains.length == 1 && pattChains.length == 1) {

            int dist = getLevenshteinDistance(valueChains[0], pattChains[0]);

            double distNorm = 1.0 - dist / (pattern.length() * 1.0);

            return distNorm >= threshold;

        } else {

            /**

             * Otherwise we will iterate to assure all patters appear in one of

             * the value chains

             */

            Filter filtAND;

            List listAND = new ArrayList();

            for (int i = 0; i < pattChains.length; i++) {

                String patt = pattChains[i];

                Filter filtOR;

                List listOR = new ArrayList();

                for (int j = 0; j < valueChains.length; j++) {

                    Expression exp2 = fact.literal(valueChains[j]);

                    PropertyDistance dist = fact.distance(exp2, patt,

                            this.threshold);

                    listOR.add(dist);

                }

                filtOR = fact.or(listOR);

                listAND.add(filtOR);

            }

            filtAND = fact.and(listAND);

            return filtAND.evaluate(null);

        }

    }

    private int getLevenshteinDistance(String s, String t) {

        if (s == null || t == null) {

            throw new IllegalArgumentException("Strings must not be null");

        }

        /*

         * The difference between this impl. and the previous is that, rather

         * than creating and retaining a matrix of size s.length()+1 by

         * t.length()+1, we maintain two single-dimensional arrays of length

         * s.length()+1. The first, d, is the 'current working' distance array

         * that maintains the newest distance cost counts as we iterate through

         * the characters of String s. Each time we increment the index of

         * String t we are comparing, d is copied to p, the second int[]. Doing

         * so allows us to retain the previous cost counts as required by the

         * algorithm (taking the minimum of the cost count to the left, up one,

         * and diagonally up and to the left of the current cost count being

         * calculated). (Note that the arrays aren't really copied anymore, just

         * switched...this is clearly much better than cloning an array or doing

         * a System.arraycopy() each time through the outer loop.)

         * 

         * Effectively, the difference between the two implementations is this

         * one does not cause an out of memory condition when calculating the LD

         * over two very large strings.

         */

        int n = s.length(); // length of s

        int m = t.length(); // length of t

        if (n == 0) {

            return m;

        } else if (m == 0) {

            return n;

        }

        int p[] = new int[n + 1]; // 'previous' cost array, horizontally

        int d[] = new int[n + 1]; // cost array, horizontally

        int _d[]; // placeholder to assist in swapping p and d

        // indexes into strings s and t

        int i; // iterates through s

        int j; // iterates through t

        char t_j; // jth character of t

        int cost; // cost

        for (i = 0; i <= n; i++) {

            p[i] = i;

        }

        for (j = 1; j <= m; j++) {

            t_j = t.charAt(j - 1);

            d[0] = j;

            for (i = 1; i <= n; i++) {

                cost = s.charAt(i - 1) == t_j ? 0 : 1;

                // minimum of cell to the left+1, to the top+1, diagonally left

                // and up +cost

                d[i] = Math.min(Math.min(d[i - 1] + 1, p[i] + 1), p[i - 1]

                        + cost);

            }

            // copy current distance counts to 'previous row' distance counts

            _d = p;

            p = d;

            d = _d;

        }

        // our last action in the above loop was to switch d and p, so p now

        // actually has the most recent cost counts

        return p[n];

    }

}