sparsematrixn.java

来自「mallet是自然语言处理、机器学习领域的一个开源项目。」· Java 代码 · 共 375 行

/* Copyright (C) 2003 Univ. of Massachusetts Amherst, Computer Science Dept.
   This file is part of "MALLET" (MAchine Learning for LanguagE Toolkit).
   http://www.cs.umass.edu/~mccallum/mallet
   This software is provided under the terms of the Common Public License,
   version 1.0, as published by http://www.opensource.org.  For further
   information, see the file `LICENSE' included with this distribution. */

package edu.umass.cs.mallet.base.types;

import java.io.Serializable;
import java.io.IOException;
import java.io.ObjectInputStream;
import java.io.ObjectOutputStream;

// Generated package name


/**
 *  Implementation of Matrix that allows arbitrary
 *   number of dimensions.  This implementation
 *   simply uses a flat array.
 *
 * Created: Tue Sep 16 14:52:37 2003
 *
 * @author <a href="mailto:[e-mail]">Charles Sutton</a>
 * @version $Id: SparseMatrixn.java,v 1.2 2005/12/15 22:41:45 casutton Exp $
 */
public class SparseMatrixn implements Matrix, Cloneable, Serializable {

  private SparseVector values;
	private int numDimensions;
	private int[] sizes;
  private int singleSize;

	/**
	 *  Create a 1-d dense matrix with the given values.
	 */
	public SparseMatrixn(double[] vals) {
		numDimensions = 1;
		sizes = new int[1];
		sizes [0] = vals.length;
		values = new SparseVector (vals);
    computeSingleSIze ();
	}
	
	/**
	 *  Create a dense matrix with the given dimensions.
	 *
	 *  @param szs An array containing the maximum for
	 *      each dimension.
	 */
	public SparseMatrixn (int szs[]) {
		numDimensions = szs.length;
		sizes = (int[])szs.clone();
		int total = 1;
		for (int j = 0; j < numDimensions; j++) {
	    total *= sizes [j];
		}
    values = new SparseVector (new double [total]);
    computeSingleSIze ();
	}


  public SparseMatrixn (int[] szs, double[] vals) {
    numDimensions = szs.length;
    sizes = (int[])szs.clone ();
    values = new SparseVector (vals);
    computeSingleSIze ();
  }

	/**
	 *  Create a sparse matrix with the given dimensions and
	 *   the given values.
	 *
	 *  @param szs An array containing the maximum for
	 *      each dimension.
   *  @param idxs An array containing the single index
   *     for each entry of the matrix.  A single index is
   *     an integer computed from the indices of each dimension.
   *     as returned by {@link Matrixn#singleIndex}.
	 *  @param vals A flat array of the entries of the
	 *      matrix, in row-major order.
	 */
	public SparseMatrixn (int[] szs, int[] idxs, double[] vals) {
		numDimensions = szs.length;
		sizes = (int[])szs.clone();
    values = new SparseVector (idxs, vals, true, true);
    computeSingleSIze ();
	}

  private void computeSingleSIze ()
  {
    int product = 1;
    for (int i = 0; i < sizes.length; i++) {
      int size = sizes[i];
      product *= size;
    }
    singleSize = product;
  }

  public int getNumDimensions () { return numDimensions; };
	
	public int getDimensions (int [] szs) {
		for ( int i = 0; i < numDimensions; i++ ) {
	    szs [i] = this.sizes [i];
		} 
		return numDimensions;
	}
	
	public double value (int[] indices) {
		return values.value (singleIndex (indices));
	}
	
	public void setValue (int[] indices, double value) {
		values.setValue (singleIndex (indices), value);
	}

  /**
   * Returns an array of all the present indices.
   * Callers must not modify the return value.
   */
  public int[] getIndices () {
    return values.getIndices ();
  }

  public ConstantMatrix cloneMatrix () {
		/* The Matrixn constructor will clone the arrays. */
		return new SparseMatrixn (sizes, values.getIndices (), values.getValues ());
	}

	public Object clone () {
		return cloneMatrix(); 
	}

	public int singleIndex (int[] indices) 
	{
		return Matrixn.singleIndex (sizes, indices);
	}

	// This is public static so it will be useful as a general
	// dereferencing utility for multidimensional arrays.
	public static int singleIndex (int[] szs, int[] indices)
	{
		int idx = 0;
		for ( int dim = 0; dim < indices.length; dim++ ) {
			idx = (idx * szs[dim]) + indices [dim];	   
		} 
		return idx;
	}

	public void singleToIndices (int single, int[] indices) {
    Matrixn.singleToIndices (single, indices, sizes);
	}

	public boolean equals (Object o) {
		if (o instanceof SparseMatrixn) {
			/* This could be extended to work for all Matrixes. */
			SparseMatrixn m2 = (SparseMatrixn) o;
			return 
				(numDimensions == m2.numDimensions) &&
				(sizes.equals (m2.sizes)) &&
				(values.equals (m2.values));
		} else {
			return false;
		}
	}

  /**
   * Returns a one-dimensional array representation of the matrix.
   *   Caller must not modify the return value.
   * @return An array of the values where index 0 is the major index, etc.
   */
  public double[] toArray () {
    return values.getValues ();
  }

  // Methods from Matrix

  public double singleValue (int i)
  {
    return values.singleValue (i);
  }

  public int singleSize ()
  {
    return singleSize;
  }

  // Access by index into sparse array, efficient for sparse and dense matrices
  public int numLocations ()
  {
    return values.numLocations ();
  }

  public int location (int index)
  {
    return values.location (index);
  }

  public double valueAtLocation (int location)
  {
    return values.valueAtLocation (location);
  }

  public void setValueAtLocation (int location, double value)
  {
    values.setValueAtLocation (location, value);
  }

  // Returns a "singleIndex"
  public int indexAtLocation (int location)
  {
    return values.indexAtLocation (location);
  }

  public double dotProduct (ConstantMatrix m)
  {
    return values.dotProduct (m);
  }

  public double absNorm ()
  {
    return values.absNorm ();
  }

  public double oneNorm ()
  {
    return values.oneNorm ();
  }

  public double twoNorm ()
  {
    return values.twoNorm ();
  }

  public double infinityNorm ()
  {
    return values.infinityNorm ();
  }

  public void print()
  {
    values.print ();
  }

  public boolean isNaN()
  {
    return values.isNaN ();
  }

  public void setSingleValue (int i, double value)
  {
    values.setValue (i, value);
  }

  public void incrementSingleValue (int i, double delta)
  {
    double value = values.value (i);
    values.setValue (i, value + delta);

  }

  public void setAll (double v)
  {
    values.setAll (v);
  }

  public void set (ConstantMatrix m)
  {
    throw new UnsupportedOperationException ("Not yet implemented.");
  }

  public void setWithAddend (ConstantMatrix m, double addend)
  {
    throw new UnsupportedOperationException ("Not yet implemented.");
  }

  public void setWithFactor (ConstantMatrix m, double factor)
  {
    throw new UnsupportedOperationException ("Not yet implemented.");
  }

  public void plusEquals (ConstantMatrix m)
  {
    values.plusEqualsSparse ((SparseVector) m);
  }

  public void plusEquals (ConstantMatrix m, double factor)
  {
    values.plusEqualsSparse ((SparseVector) m, factor);
  }

  public void equalsPlus (double factor, ConstantMatrix m)
  {
    throw new UnsupportedOperationException ("Not yet implemented.");
  }

  public void timesEquals (double factor)
  {
    values.timesEquals (factor);
  }

  public void elementwiseTimesEquals (ConstantMatrix m)
  {
    throw new UnsupportedOperationException ("Not yet implemented.");
  }

  public void elementwiseTimesEquals (ConstantMatrix m, double factor)
  {
    throw new UnsupportedOperationException ("Not yet implemented.");
  }

  public void divideEquals (double factor)
  {
    values.timesEquals (1 / factor);
  }

  public void elementwiseDivideEquals (ConstantMatrix m)
  {
    throw new UnsupportedOperationException ("Not yet implemented.");
  }

  public void elementwiseDivideEquals (ConstantMatrix m, double factor)
  {
    throw new UnsupportedOperationException ("Not yet implemented.");
  }

  public double oneNormalize ()
  {
    double norm = values.oneNorm();
    values.timesEquals (1 / norm);
    return norm;
  }

  public double twoNormalize ()
  {
    double norm = values.twoNorm();
    values.timesEquals (1 / norm);
    return norm;
  }

  public double absNormalize ()
  {
    double norm = values.absNorm();
    values.timesEquals (1 / norm);
    return norm;
  }

  public double infinityNormalize ()
  {
    double norm = values.infinityNorm();
    values.timesEquals (1 / norm);
    return norm;
  }

  	// Serialization garbage

  private static final long serialVersionUID = 1;
  private static final int CURRENT_SERIAL_VERSION = 1;

  private void writeObject (ObjectOutputStream out) throws IOException
  {
    out.defaultWriteObject ();
    out.writeInt (CURRENT_SERIAL_VERSION);
  }


  private void readObject (ObjectInputStream in) throws IOException, ClassNotFoundException
  {
    in.defaultReadObject ();
    int version = in.readInt ();
  }

}