SparseVector.java

/*
Copyright 2008-2010 Gephi
Authors : Mathieu Bastian <mathieu.bastian@gephi.org>
Website : http://www.gephi.org

This file is part of Gephi.

DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS HEADER.

Copyright 2011 Gephi Consortium. All rights reserved.

The contents of this file are subject to the terms of either the GNU
General Public License Version 3 only ("GPL") or the Common
Development and Distribution License("CDDL") (collectively, the
"License"). You may not use this file except in compliance with the
License. You can obtain a copy of the License at
http://gephi.org/about/legal/license-notice/
or /cddl-1.0.txt and /gpl-3.0.txt. See the License for the
specific language governing permissions and limitations under the
License.  When distributing the software, include this License Header
Notice in each file and include the License files at
/cddl-1.0.txt and /gpl-3.0.txt. If applicable, add the following below the
License Header, with the fields enclosed by brackets [] replaced by
your own identifying information:
"Portions Copyrighted [year] [name of copyright owner]"

If you wish your version of this file to be governed by only the CDDL
or only the GPL Version 3, indicate your decision by adding
"[Contributor] elects to include this software in this distribution
under the [CDDL or GPL Version 3] license." If you do not indicate a
single choice of license, a recipient has the option to distribute
your version of this file under either the CDDL, the GPL Version 3 or
to extend the choice of license to its licensees as provided above.
However, if you add GPL Version 3 code and therefore, elected the GPL
Version 3 license, then the option applies only if the new code is
made subject to such option by the copyright holder.

Contributor(s):

Portions Copyrighted 2011 Gephi Consortium.
*/
package org.gephi.clustering.plugin.mcl;

import java.util.HashMap;
import java.util.Iterator;

/**
 * SparseVector represents a sparse vector.
 * <p>
 * Conventions: except for the inherited methods and normalise(double),
 * operations leave <tt>this</tt> ummodified (immutable) if there is a return
 * value. Within operations, no pruning of values close to zero is done. Pruning
 * can be controlled via the prune() method.
 */
//Original author Gregor Heinrich
public class SparseVector extends HashMap<Integer, Double> {

    private static final long serialVersionUID = 1L;
    private int length = 0;

    /**
     * create empty vector
     */
    public SparseVector() {
        super();
    }

    /**
     * create empty vector with length
     */
    public SparseVector(int i) {
        this();
        length = i;
    }

    /**
     * create vector from dense vector
     *
     * @param x
     */
    public SparseVector(double[] x) {
        this(x.length);
        for (int i = 0; i < x.length; i++) {
            if (x[i] != 0) {
                put(i, x[i]);
            }
        }
    }

    /**
     * copy constructor
     *
     * @param v
     */
    public SparseVector(SparseVector v) {
        super(v);
        this.length = v.length;
    }

    /**
     * get ensures it returns 0 for empty hash values or if index exceeds
     * length.
     *
     * @param key
     * @return val
     */
    @Override
    public Double get(Object key) {
        Double b = super.get(key);
        if (b == null) {
            return 0.;
        }
        return b;
    }

    /**
     * put increases the matrix size if the index exceeds the current size.
     *
     * @param key
     * @param value
     * @return
     */
    @Override
    public Double put(Integer key, Double value) {
        length = Math.max(length, key + 1);
        if (value == 0) {
            return remove(key);
        }
        return super.put(key, value);
    }

    /**
     * normalises the vector to 1.
     */
    public void normalise() {
        double invsum = 1. / sum();
        for (int i : keySet()) {
            mult(i, invsum);
        }
    }

    /**
     * normalises the vector to newsum
     *
     * @param the value to which the element sum
     * @return the old element sum
     */
    public double normalise(double newsum) {
        double sum = sum();
        double invsum = newsum / sum;
        for (int i : keySet()) {
            mult(i, invsum);
        }
        return sum;
    }

    /**
     * sum of the elements
     *
     * @return
     */
    private double sum() {
        double sum = 0;
        for (double a : values()) {
            sum += a;
        }
        return sum;
    }

    /**
     * power sum of the elements
     *
     * @return
     */
    public double sum(double s) {
        double sum = 0;
        for (double a : values()) {
            sum += Math.pow(a, s);
        }
        return sum;
    }

    /**
     * mutable add
     *
     * @param v
     */
    public void add(SparseVector v) {
        for (int i : keySet()) {
            add(i, v.get(i));
        }
    }

    /**
     * mutable mult
     *
     * @param i index
     * @param a value
     */
    public void mult(int i, double a) {
        Double c = get(i);
        c *= a;
        put(i, c);
    }

    /**
     * mutable factorisation
     *
     * @param a
     */
    public void factor(double a) {
        SparseVector s = copy();
        for (int i : keySet()) {
            s.mult(i, a);
        }
    }

    /**
     * immutable scalar product
     *
     * @param v
     * @return scalar product
     */
    public double times(SparseVector v) {
        double sum = 0;
        for (int i : keySet()) {
            sum += get(i) * v.get(i);
        }
        return sum;
    }

    /**
     * mutable Hadamard product (elementwise multiplication)
     *
     * @param v
     */
    public void hadamardProduct(SparseVector v) {
        for (int i : keySet()) {
            put(i, v.get(i) * get(i));
        }
    }

    /**
     * mutable Hadamard power
     *
     * @param s
     */
    public void hadamardPower(double s) {
        for (int i : keySet().toArray(new Integer[0])) {
            put(i, Math.pow(get(i), s));
        }
    }

    /**
     * mutable add
     *
     * @param i
     * @param a
     */
    public void add(int i, double a) {
        length = Math.max(length, i + 1);
        double c = get(i);
        c += a;
        put(i, c);
    }

    /**
     * get the length of the vector
     *
     * @return
     */
    public final int getLength() {
        return length;
    }

    /**
     * set the new length of the vector (regardless of the maximum index).
     *
     * @param length
     */
    public final void setLength(int length) {
        this.length = length;
    }

    /**
     * copy the contents of the sparse vector
     *
     * @return
     */
    public SparseVector copy() {
        return new SparseVector(this);
    }

    @Override
    public String toString() {
        StringBuffer sb = new StringBuffer();
        for (int i : keySet()) {
            sb.append(i).append("->").append(get(i)).append(", ");
        }
        return sb.toString();
    }

    /**
     * get dense represenation
     *
     * @return
     */
    public double[] getDense() {
        double[] a = new double[length];
        for (int i : keySet()) {
            a[i] = get(i);
        }
        return a;
    }

    /**
     * maximum element value
     *
     * @return
     */
    public double max() {
        double max = 0;
        for (int i : keySet()) {
            max = Math.max(get(i), max);
        }
        return max;
    }

    /**
     * exponential sum, i.e., sum (elements^p)
     *
     * @param p
     * @return
     */
    public double expSum(int p) {
        double sum = 0;
        for (double a : values()) {
            sum += Math.pow(a, p);
        }
        return sum;
    }

    /**
     * remove all elements whose magnitude is < threshold
     *
     * @param threshold
     */
    public void prune(double threshold) {
        for (Iterator<Integer> it = keySet().iterator(); it.hasNext();) {
            int key = it.next();
            if (Math.abs(get(key)) < threshold) {
                it.remove();
            }
        }
    }
}