All Downloads are FREE. Search and download functionalities are using the official Maven repository.

no.uib.cipr.matrix.distributed.Reduction Maven / Gradle / Ivy

Go to download

A comprehensive collection of matrix data structures, linear solvers, least squares methods, eigenvalue, and singular value decompositions.

There is a newer version: 1.0.4
Show newest version
/*
 * Copyright (C) 2003-2006 Bjørn-Ove Heimsund
 * 
 * This file is part of MTJ.
 * 
 * This library is free software; you can redistribute it and/or modify it
 * under the terms of the GNU Lesser General Public License as published by the
 * Free Software Foundation; either version 2.1 of the License, or (at your
 * option) any later version.
 * 
 * This library 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 Lesser General Public License
 * for more details.
 * 
 * You should have received a copy of the GNU Lesser General Public License
 * along with this library; if not, write to the Free Software Foundation,
 * Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
 */

package no.uib.cipr.matrix.distributed;

/**
 * Performs a reduction operation. When performing a reduction, start with the
 * value return by the init function, for example:
 * 
 * 
 * int[] x, y;
 * Reduction r;
 * // ...
 * r.initInt(x);
 * r.opInt(x, y);
 * 
* *

* Many predefined reductions are available in * {@link no.uib.cipr.matrix.distributed.Reductions}. *

* * @deprecated the no.uib.cipr.matrix.distributed package has been deprecated because * of a number of hard to fix concurrency bugs. It is distributed only for backwards compatibility, * but is not recommended. The utility of this package is questionable, as it does not allow * distribution of computation between JVMs or across a network. For many people, distributed * computing of multiple matrices can be achieved at a user-level through the * JPPF Framework. * Users who need to deal with few very large matrices may wish to implement their own storage classes * and solvers using JPPF, but this will not be supported directly in matrix-toolkits-java. */ @Deprecated public abstract class Reduction { /** * Sets up the output data */ public void init(Object x) { if (x instanceof double[]) initDouble((double[]) x); else if (x instanceof int[]) initInt((int[]) x); else if (x instanceof boolean[]) initBoolean((boolean[]) x); else if (x instanceof byte[]) initByte((byte[]) x); else if (x instanceof char[]) initChar((char[]) x); else if (x instanceof short[]) initShort((short[]) x); else if (x instanceof long[]) initLong((long[]) x); else if (x instanceof float[]) initFloat((float[]) x); else throw new IllegalArgumentException("Datatype is not supported"); } /** * Adds to the output data * * @param x * Output data * @param y * New input data */ public void op(Object x, Object y) { if (x instanceof double[]) opDouble((double[]) x, (double[]) y); else if (x instanceof int[]) opInt((int[]) x, (int[]) y); else if (x instanceof boolean[]) opBoolean((boolean[]) x, (boolean[]) y); else if (x instanceof byte[]) opByte((byte[]) x, (byte[]) y); else if (x instanceof char[]) opChar((char[]) x, (char[]) y); else if (x instanceof short[]) opShort((short[]) x, (short[]) y); else if (x instanceof long[]) opLong((long[]) x, (long[]) y); else if (x instanceof float[]) opFloat((float[]) x, (float[]) y); else throw new IllegalArgumentException("Datatype is not supported"); } protected abstract void initBoolean(boolean[] x); protected abstract void initByte(byte[] x); protected abstract void initChar(char[] x); protected abstract void initShort(short[] x); protected abstract void initInt(int[] x); protected abstract void initFloat(float[] x); protected abstract void initLong(long[] x); protected abstract void initDouble(double[] x); protected abstract void opBoolean(boolean[] x, boolean[] y); protected abstract void opByte(byte[] x, byte[] y); protected abstract void opChar(char[] x, char[] y); protected abstract void opShort(short[] x, short[] y); protected abstract void opInt(int[] x, int[] y); protected abstract void opFloat(float[] x, float[] y); protected abstract void opLong(long[] x, long[] y); protected abstract void opDouble(double[] x, double[] y); }




© 2015 - 2024 Weber Informatics LLC | Privacy Policy