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

org.ejml.dense.fixed.NormOps_FDF4 Maven / Gradle / Ivy

Go to download

A fast and easy to use dense and sparse matrix linear algebra library written in Java.

There is a newer version: 0.43.1
Show newest version
/*
 * Copyright (c) 2009-2017, Peter Abeles. All Rights Reserved.
 *
 * This file is part of Efficient Java Matrix Library (EJML).
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *   http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package org.ejml.dense.fixed;

import org.ejml.data.FMatrix4;
import org.ejml.data.FMatrix4x4;

/**
 * 

Matrix norm related operations for fixed sized matrices of size 4.

*

DO NOT MODIFY. Automatically generated code created by GenerateFixedNormOps

* * @author Peter Abeles */ public class NormOps_FDF4 { public static void normalizeF( FMatrix4x4 M ) { float val = normF(M); CommonOps_FDF4.divide(M,val); } public static void normalizeF( FMatrix4 M ) { float val = normF(M); CommonOps_FDF4.divide(M,val); } public static float fastNormF( FMatrix4x4 M ) { float sum = 0; sum += M.a11*M.a11 + M.a12*M.a12 + M.a13*M.a13 + M.a14*M.a14; sum += M.a21*M.a21 + M.a22*M.a22 + M.a23*M.a23 + M.a24*M.a24; sum += M.a31*M.a31 + M.a32*M.a32 + M.a33*M.a33 + M.a34*M.a34; sum += M.a41*M.a41 + M.a42*M.a42 + M.a43*M.a43 + M.a44*M.a44; return (float)Math.sqrt(sum); } public static float fastNormF( FMatrix4 M ) { float sum = M.a1*M.a1 + M.a2*M.a2 + M.a3*M.a3 + M.a4*M.a4; return (float)Math.sqrt(sum); } public static float normF( FMatrix4x4 M ) { float scale = CommonOps_FDF4.elementMaxAbs(M); if( scale == 0.0f ) return 0.0f; float a11 = M.a11/scale, a12 = M.a12/scale, a13 = M.a13/scale, a14 = M.a14/scale; float a21 = M.a21/scale, a22 = M.a22/scale, a23 = M.a23/scale, a24 = M.a24/scale; float a31 = M.a31/scale, a32 = M.a32/scale, a33 = M.a33/scale, a34 = M.a34/scale; float a41 = M.a41/scale, a42 = M.a42/scale, a43 = M.a43/scale, a44 = M.a44/scale; float sum = 0; sum += a11*a11 + a12*a12 + a13*a13 + a14*a14; sum += a21*a21 + a22*a22 + a23*a23 + a24*a24; sum += a31*a31 + a32*a32 + a33*a33 + a34*a34; sum += a41*a41 + a42*a42 + a43*a43 + a44*a44; return scale * (float)Math.sqrt(sum); } public static float normF( FMatrix4 M ) { float scale = CommonOps_FDF4.elementMaxAbs(M); if( scale == 0.0f ) return 0.0f; float a1 = M.a1/scale, a2 = M.a2/scale, a3 = M.a3/scale, a4 = M.a4/scale; float sum = a1*a1 + a2*a2 + a3*a3 + a4*a4; return scale * (float)Math.sqrt(sum); } }




© 2015 - 2024 Weber Informatics LLC | Privacy Policy