org.ejml.dense.row.NormOps_CDRM Maven / Gradle / Ivy

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

There is a newer version: 0.43.1

/*
 * Copyright (c) 2020, 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.row;

import javax.annotation.Generated;
import org.ejml.data.CMatrixRMaj;

/**
 * @author Peter Abeles
 */
@Generated("org.ejml.dense.row.NormOps_ZDRM")
public class NormOps_CDRM {
    /**
     * 
     * Computes the Frobenius matrix norm:

     * 

     * normF = Sqrt{  ∑_i=1:m ∑_j=1:n { a_ij²}   }
     * 
     * 
     * This is equivalent to the element wise p=2 norm.
     * 
     *
     * @param a The matrix whose norm is computed.  Not modified.
     * @return The norm's value.
     */
    public static float normF( CMatrixRMaj a ) {
        float total = 0;

        float scale = CommonOps_CDRM.elementMaxAbs(a);

        if (scale == 0.0f)
            return 0.0f;

        final int size = a.getDataLength();

        for (int i = 0; i < size; i += 2) {
            float real = a.data[i]/scale;
            float imag = a.data[i + 1]/scale;

            total += real*real + imag*imag;
        }

        return scale * (float)Math.sqrt(total);
    }
}