/*
* Copyright (c) 2012-2013, Peter Abeles. All Rights Reserved.
*
* This file is part of DDogleg (http://ddogleg.org).
*
* 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.ddogleg.optimization.wrap;
import org.ddogleg.optimization.UnconstrainedMinimization;
import org.ddogleg.optimization.functions.FunctionNtoN;
import org.ddogleg.optimization.functions.FunctionNtoS;
import org.ddogleg.optimization.impl.NumericalGradientForward;
import org.ddogleg.optimization.impl.TrivialFunctionNtoS;
import org.junit.Test;
import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertTrue;
/**
* Tests which make sure the {@link org.ddogleg.optimization.UnconstrainedMinimization} contract is followed.
*
* @author Peter Abeles
*/
public abstract class GenericUnconstrainedMinimizationTests {
public abstract UnconstrainedMinimization createAlgorithm();
/**
* Simple optimization which checks several different aspects of its behavior
*/
@Test
public void basicTest() {
FunctionNtoS residual = new TrivialFunctionNtoS();
FunctionNtoN jacobian = new NumericalGradientForward(residual);
UnconstrainedMinimization alg = createAlgorithm();
alg.setFunction(residual,jacobian,0);
alg.initialize(new double[]{1,1,1},1e-10,1e-10);
double[] prev = new double[]{1,1,1};
int i;
for( i = 0; i < 200 && !alg.iterate(); i++ ) {
double found[] = alg.getParameters();
// check updated flag
if( alg.isUpdated() ) {
boolean changed = false;
for( int j = 0; j < found.length; j++ ) {
if( found[j] != prev[j] )
changed = true;
}
assertTrue(changed);
} else {
for( int j = 0; j < found.length; j++ ) {
assertTrue(found[j]==prev[j]);
}
}
prev = found.clone();
}
// should converge way before this
assertTrue(i != 200);
assertTrue(alg.isConverged());
double found[] = alg.getParameters();
assertEquals(0, found[0], 1e-4);
assertEquals(0, found[1], 1e-4);
assertEquals(1, found[2], 1e-4); // no change expected in last parameter
}
/**
* Makes sure if the jacobian is null that the numerical differentiation is done and that it uses the expected
* one
*/
@Test
public void checkNumerical() {
FunctionNtoS residual = new TrivialFunctionNtoS();
FunctionNtoN jacobian = new NumericalGradientForward(residual);
UnconstrainedMinimization alg = createAlgorithm();
alg.setFunction(residual,jacobian,0);
alg.initialize(new double[]{1,1,1},1e-10,1e-10);
for( int i = 0; i < 200 && !alg.iterate(); i++ ) {}
double expected[] = alg.getParameters().clone();
alg.setFunction(residual,null,0);
alg.initialize(new double[]{1,1,1},1e-10,1e-10);
for( int i = 0; i < 200 && !alg.iterate(); i++ ) {}
double found[] = alg.getParameters().clone();
for( int i = 0; i < found.length; i++ ) {
assertTrue(found[i]==expected[i]);
}
}
/**
* The function can modify the input parameters and
*/
@Test
public void checkAcceptModified() {
ModifyInputFunctions residuals = new ModifyInputFunctions();
UnconstrainedMinimization alg = createAlgorithm();
alg.setFunction(residuals,null,0);
alg.initialize(new double[]{1,0.5,9.5},1e-10,1e-10);
for( int i = 0; i < 200 && !alg.iterate(); i++ ) {}
double found[] = alg.getParameters().clone();
double expected[] = new double[]{1,2,3};
for( int i = 0; i < found.length; i++ ) {
assertTrue(found[i]==expected[i]);
}
}
private class ModifyInputFunctions implements FunctionNtoS {
@Override
public int getNumOfInputsN() {
return 3;
}
@Override
public double process(double[] input ) {
for( int i = 0; i < input.length; i++ ) {
input[i] = 1+i;
}
return 0;
}
}
}
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