/*
 * Zorbage: an algebraic data hierarchy for use in numeric processing.
 *
 * Copyright (C) 2016-2019 Barry DeZonia
 * 
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions are met:
 * 
 * 1. Redistributions of source code must retain the above copyright notice,
 *    this list of conditions and the following disclaimer.
 * 2. Redistributions in binary form must reproduce the above copyright notice,
 *    this list of conditions and the following disclaimer in the documentation
 *    and/or other materials provided with the distribution.
 * 
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
 * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
 * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
 * ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE
 * LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
 * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
 * SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
 * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
 * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
 * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
 * POSSIBILITY OF SUCH DAMAGE.
 */
package nom.bdezonia.zorbage.type.data.float64.complex;

import org.junit.Test;

import nom.bdezonia.zorbage.algebras.G;
import nom.bdezonia.zorbage.algorithm.MatrixConstantDiagonal;
import nom.bdezonia.zorbage.type.algebra.Addition;
import nom.bdezonia.zorbage.type.algebra.Algebra;
import nom.bdezonia.zorbage.type.algebra.Multiplication;
import nom.bdezonia.zorbage.type.algebra.Scale;
import nom.bdezonia.zorbage.type.algebra.Unity;
import nom.bdezonia.zorbage.type.ctor.StorageConstruction;

/**
 * 
 * @author Barry DeZonia
 *
 */
public class TestComplexSpeed {

	// Comparing raw speed of my code versus C++:
	//
	// On BDZ's test machine C++ code is 20% slower than this Java code. Surprising.
	// Relevant C++ code compiled with g++ -O3

/*
	#include <iostream>
	#include <complex>
	#include <sys/time.h>

	std::complex<double> factor(1,1);

	inline std::complex<double>
	f(const std::complex<double> &z)
	{
		return z*z + factor*z - 1.0;
	}

	int main()
	{
		struct timeval start, end;

		long mtime, seconds, useconds;    

		gettimeofday(&start, NULL);

		std::complex<double> z = 0.1;
		for (int i = 0; i < 1000000000; i++) {
			z = f(z);
		}
	 
		gettimeofday(&end, NULL);
	 
		seconds  = end.tv_sec  - start.tv_sec;
		useconds = end.tv_usec - start.tv_usec;

		mtime = ((seconds) * 1000 + useconds/1000.0) + 0.5;

		std::cout << mtime << " " << z;
	}
*/

	private static <T extends Algebra<T,U> & Addition<U> & Multiplication<U> & Unity<U> & Scale<U,V>,
					U,
					V>
		void func(T memberAlg, V linTerm, U z, U res)
	{
		// f(z) = z*z + (1,1)*z - 1
		U t1 = memberAlg.construct();
		U t2 = memberAlg.construct();
		memberAlg.multiply().call(z, z, t1);
		memberAlg.scale().call(linTerm, z, t2);
		// This ctor gives the target matrix the right shape. This causes a little extra initialization
		// that could be avoided with two separate algos or by defining a streamlined constructor for
		// the algebra. I guess the second idea is a TODO.
		U t3 = memberAlg.construct(t2);
		memberAlg.unity().call(t3);
		memberAlg.add().call(t1, t2, t1);
		memberAlg.subtract().call(t1, t3, res);
	}

	// From the 15 min mark of this talk: http://cds.cern.ch/record/2157242?ln=de

	@Test
	public void test1() {
		long a = System.currentTimeMillis();
		ComplexFloat64Member linTerm = new ComplexFloat64Member(1,1);
		ComplexFloat64Member z = new ComplexFloat64Member(0.1,0);
		for (long i = 0; i < 1000000000; i++) {
			func(G.CDBL, linTerm, z, z);
		}
		long b = System.currentTimeMillis();
		System.out.println((b - a) + " millisecs " + z);
	}

	// From the 17 min mark of this talk: http://cds.cern.ch/record/2157242?ln=de
	//
	// This matrix version takes 3.5 times as long as the 1d Java version above.
	// The C++ example it was based on in the video took 2.5 times as long as the 1d C++ version.
	// 2.5 * local C++ time (18.5 secs) is comparable to 3.5 * local Java time (21.0 secs).
	// Also for reference PyPy and NumPy was taking 200-220 seconds for the same work.
	
	@Test
	public void test2() {
		long a = System.currentTimeMillis();
		ComplexFloat64Member linTerm = new ComplexFloat64Member(1,1);
		ComplexFloat64MatrixMember z = new ComplexFloat64MatrixMember(StorageConstruction.MEM_ARRAY, 6, 6);
		MatrixConstantDiagonal.compute(G.CDBL, new ComplexFloat64Member(0.1, 0), z);
		for (long i = 0; i < 10000000; i++) {
			func(G.CDBL_MAT, linTerm, z, z);
		}
		long b = System.currentTimeMillis();
		System.out.println((b - a) + " millisecs " + z);
	}
}

@@ -2917,19 +2917,19 @@
 %0A%09%09void 
-fun
+pro
 c(T memb
@@ -3839,35 +3839,35 @@
 0000; i++) %7B%0A%09%09%09
-fun
+pro
 c(G.CDBL, linTer
@@ -4766,11 +4766,11 @@
 %0A%09%09%09
-fun
+pro
 c(G.

Rename something to be more idiomatic for zorbage