/*
 * Copyright (c) 2009-2020, 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.decomposition.eig;

import org.ejml.data.DMatrixRMaj;
import org.ejml.dense.row.RandomMatrices_DDRM;
import org.ejml.dense.row.decomposition.eig.watched.WatchedDoubleStepQREigen_DDRM;
import org.ejml.dense.row.decomposition.hessenberg.HessenbergSimilarDecomposition_DDRM;
import org.ejml.dense.row.decomposition.hessenberg.TridiagonalDecompositionHouseholder_DDRM;
import org.openjdk.jmh.annotations.*;
import org.openjdk.jmh.runner.Runner;
import org.openjdk.jmh.runner.RunnerException;
import org.openjdk.jmh.runner.options.Options;
import org.openjdk.jmh.runner.options.OptionsBuilder;

import java.util.Random;
import java.util.concurrent.TimeUnit;

@BenchmarkMode(Mode.AverageTime)
@OutputTimeUnit(TimeUnit.MILLISECONDS)
@Warmup(iterations = 2)
@Measurement(iterations = 5)
@State(Scope.Benchmark)
@Fork(value = 2)
public class BenchmarkDecompositionEvd_DDRM {
    //    @Param({"100", "500", "1000", "5000", "10000"})
    @Param({"500"})
    public int size;

    @Param({"false", "true"})
    public boolean vectors;

    public DMatrixRMaj S, A;

    SymmetricQRAlgorithmDecomposition_DDRM eigenSym;
    WatchedDoubleStepQRDecomposition_DDRM eigen;

    @Setup
    public void setup() {
        eigenSym = new SymmetricQRAlgorithmDecomposition_DDRM(new TridiagonalDecompositionHouseholder_DDRM(), vectors);
        eigen = new WatchedDoubleStepQRDecomposition_DDRM(new HessenbergSimilarDecomposition_DDRM(),
                new WatchedDoubleStepQREigen_DDRM(), vectors);

        Random rand = new Random(234);

        S = RandomMatrices_DDRM.symmetric(size, -1, 1, rand);
    }

    @Benchmark
    public void symmetric() {
        DMatrixRMaj A = eigen.inputModified() ? S.copy() : S;
        eigenSym.decompose(A);
    }

    @Benchmark
    public void general() {
        DMatrixRMaj A = eigen.inputModified() ? S.copy() : S;
        A.set(2, 4, 2.0); // break the symmetry
        eigen.decompose(A);
    }

    public static void main( String[] args ) throws RunnerException {
        Options opt = new OptionsBuilder()
                .include(BenchmarkDecompositionEvd_DDRM.class.getSimpleName())
                .build();

        new Runner(opt).run();
    }
}
