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From l..@apache.org
Subject svn commit: r786467 - in /commons/proper/math/trunk/src/test/org/apache/commons/math/optimization: MultiStartDifferentiableMultivariateVectorialOptimizerTest.java MultiStartMultivariateRealOptimizerTest.java
Date Fri, 19 Jun 2009 12:04:01 GMT
Author: luc
Date: Fri Jun 19 12:04:01 2009
New Revision: 786467

URL: http://svn.apache.org/viewvc?rev=786467&view=rev
Log:
added new tests

Added:
    commons/proper/math/trunk/src/test/org/apache/commons/math/optimization/MultiStartDifferentiableMultivariateVectorialOptimizerTest.java
  (with props)
    commons/proper/math/trunk/src/test/org/apache/commons/math/optimization/MultiStartMultivariateRealOptimizerTest.java
  (with props)

Added: commons/proper/math/trunk/src/test/org/apache/commons/math/optimization/MultiStartDifferentiableMultivariateVectorialOptimizerTest.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/test/org/apache/commons/math/optimization/MultiStartDifferentiableMultivariateVectorialOptimizerTest.java?rev=786467&view=auto
==============================================================================
--- commons/proper/math/trunk/src/test/org/apache/commons/math/optimization/MultiStartDifferentiableMultivariateVectorialOptimizerTest.java
(added)
+++ commons/proper/math/trunk/src/test/org/apache/commons/math/optimization/MultiStartDifferentiableMultivariateVectorialOptimizerTest.java
Fri Jun 19 12:04:01 2009
@@ -0,0 +1,192 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You 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.apache.commons.math.optimization;
+
+import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertTrue;
+import static org.junit.Assert.fail;
+
+import java.io.Serializable;
+
+import org.apache.commons.math.FunctionEvaluationException;
+import org.apache.commons.math.analysis.DifferentiableMultivariateVectorialFunction;
+import org.apache.commons.math.analysis.MultivariateMatrixFunction;
+import org.apache.commons.math.linear.BlockRealMatrix;
+import org.apache.commons.math.linear.RealMatrix;
+import org.apache.commons.math.optimization.general.GaussNewtonOptimizer;
+import org.apache.commons.math.random.GaussianRandomGenerator;
+import org.apache.commons.math.random.JDKRandomGenerator;
+import org.apache.commons.math.random.RandomVectorGenerator;
+import org.apache.commons.math.random.UncorrelatedRandomVectorGenerator;
+import org.junit.Test;
+
+/**
+ * <p>Some of the unit tests are re-implementations of the MINPACK <a
+ * href="http://www.netlib.org/minpack/ex/file17">file17</a> and <a
+ * href="http://www.netlib.org/minpack/ex/file22">file22</a> test files. 
+ * The redistribution policy for MINPACK is available <a
+ * href="http://www.netlib.org/minpack/disclaimer">here</a>, for
+ * convenience, it is reproduced below.</p>
+
+ * <table border="0" width="80%" cellpadding="10" align="center" bgcolor="#E0E0E0">
+ * <tr><td>
+ *    Minpack Copyright Notice (1999) University of Chicago.
+ *    All rights reserved
+ * </td></tr>
+ * <tr><td>
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ * <ol>
+ *  <li>Redistributions of source code must retain the above copyright
+ *      notice, this list of conditions and the following disclaimer.</li>
+ * <li>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.</li>
+ * <li>The end-user documentation included with the redistribution, if any,
+ *     must include the following acknowledgment:
+ *     <code>This product includes software developed by the University of
+ *           Chicago, as Operator of Argonne National Laboratory.</code>
+ *     Alternately, this acknowledgment may appear in the software itself,
+ *     if and wherever such third-party acknowledgments normally appear.</li>
+ * <li><strong>WARRANTY DISCLAIMER. THE SOFTWARE IS SUPPLIED "AS IS"
+ *     WITHOUT WARRANTY OF ANY KIND. THE COPYRIGHT HOLDER, THE
+ *     UNITED STATES, THE UNITED STATES DEPARTMENT OF ENERGY, AND
+ *     THEIR EMPLOYEES: (1) DISCLAIM ANY WARRANTIES, EXPRESS OR
+ *     IMPLIED, INCLUDING BUT NOT LIMITED TO ANY IMPLIED WARRANTIES
+ *     OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE
+ *     OR NON-INFRINGEMENT, (2) DO NOT ASSUME ANY LEGAL LIABILITY
+ *     OR RESPONSIBILITY FOR THE ACCURACY, COMPLETENESS, OR
+ *     USEFULNESS OF THE SOFTWARE, (3) DO NOT REPRESENT THAT USE OF
+ *     THE SOFTWARE WOULD NOT INFRINGE PRIVATELY OWNED RIGHTS, (4)
+ *     DO NOT WARRANT THAT THE SOFTWARE WILL FUNCTION
+ *     UNINTERRUPTED, THAT IT IS ERROR-FREE OR THAT ANY ERRORS WILL
+ *     BE CORRECTED.</strong></li>
+ * <li><strong>LIMITATION OF LIABILITY. IN NO EVENT WILL THE COPYRIGHT
+ *     HOLDER, THE UNITED STATES, THE UNITED STATES DEPARTMENT OF
+ *     ENERGY, OR THEIR EMPLOYEES: BE LIABLE FOR ANY INDIRECT,
+ *     INCIDENTAL, CONSEQUENTIAL, SPECIAL OR PUNITIVE DAMAGES OF
+ *     ANY KIND OR NATURE, INCLUDING BUT NOT LIMITED TO LOSS OF
+ *     PROFITS OR LOSS OF DATA, FOR ANY REASON WHATSOEVER, WHETHER
+ *     SUCH LIABILITY IS ASSERTED ON THE BASIS OF CONTRACT, TORT
+ *     (INCLUDING NEGLIGENCE OR STRICT LIABILITY), OR OTHERWISE,
+ *     EVEN IF ANY OF SAID PARTIES HAS BEEN WARNED OF THE
+ *     POSSIBILITY OF SUCH LOSS OR DAMAGES.</strong></li>
+ * <ol></td></tr>
+ * </table>
+
+ * @author Argonne National Laboratory. MINPACK project. March 1980 (original fortran minpack
tests)
+ * @author Burton S. Garbow (original fortran minpack tests)
+ * @author Kenneth E. Hillstrom (original fortran minpack tests)
+ * @author Jorge J. More (original fortran minpack tests)
+ * @author Luc Maisonobe (non-minpack tests and minpack tests Java translation)
+ */
+public class MultiStartDifferentiableMultivariateVectorialOptimizerTest {
+
+    @Test
+    public void testTrivial() throws FunctionEvaluationException, OptimizationException {
+        LinearProblem problem =
+            new LinearProblem(new double[][] { { 2 } }, new double[] { 3 });
+        DifferentiableMultivariateVectorialOptimizer underlyingOptimizer =
+            new GaussNewtonOptimizer(true);
+        JDKRandomGenerator g = new JDKRandomGenerator();
+        g.setSeed(16069223052l);
+        RandomVectorGenerator generator =
+            new UncorrelatedRandomVectorGenerator(1, new GaussianRandomGenerator(g));
+        MultiStartDifferentiableMultivariateVectorialOptimizer optimizer =
+            new MultiStartDifferentiableMultivariateVectorialOptimizer(underlyingOptimizer,
+                                                                       10, generator);
+        optimizer.setMaxIterations(100);
+        optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
+
+        // no optima before first optimization attempt
+        try {
+            optimizer.getOptima();
+            fail("an exception should have been thrown");
+        } catch (IllegalStateException ise) {
+            // expected
+        }
+        VectorialPointValuePair optimum =
+            optimizer.optimize(problem, problem.target, new double[] { 1 }, new double[]
{ 0 });
+        assertEquals(1.5, optimum.getPoint()[0], 1.0e-10);
+        assertEquals(3.0, optimum.getValue()[0], 1.0e-10);
+        VectorialPointValuePair[] optima = optimizer.getOptima();
+        assertEquals(10, optima.length);
+        for (int i = 0; i < optima.length; ++i) {
+            assertEquals(1.5, optima[i].getPoint()[0], 1.0e-10);
+            assertEquals(3.0, optima[i].getValue()[0], 1.0e-10);
+        }
+        assertTrue(optimizer.getEvaluations() > 20);
+        assertTrue(optimizer.getEvaluations() < 50);
+        assertTrue(optimizer.getIterations() > 20);
+        assertTrue(optimizer.getIterations() < 50);
+        assertTrue(optimizer.getJacobianEvaluations() > 20);
+        assertTrue(optimizer.getJacobianEvaluations() < 50);
+        assertEquals(100, optimizer.getMaxIterations());
+    }
+
+    @Test(expected = OptimizationException.class)
+    public void testNoOptimum() throws FunctionEvaluationException, OptimizationException
{
+        DifferentiableMultivariateVectorialOptimizer underlyingOptimizer =
+            new GaussNewtonOptimizer(true);
+        JDKRandomGenerator g = new JDKRandomGenerator();
+        g.setSeed(12373523445l);
+        RandomVectorGenerator generator =
+            new UncorrelatedRandomVectorGenerator(1, new GaussianRandomGenerator(g));
+        MultiStartDifferentiableMultivariateVectorialOptimizer optimizer =
+            new MultiStartDifferentiableMultivariateVectorialOptimizer(underlyingOptimizer,
+                                                                       10, generator);
+        optimizer.setMaxIterations(100);
+        optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
+        optimizer.optimize(new DifferentiableMultivariateVectorialFunction() {
+                public MultivariateMatrixFunction jacobian() {
+                    return null;
+                }
+                public double[] value(double[] point) throws FunctionEvaluationException
{
+                    throw new FunctionEvaluationException(point[0]);
+                }
+            }, new double[] { 2 }, new double[] { 1 }, new double[] { 0 });
+    }
+
+    private static class LinearProblem implements DifferentiableMultivariateVectorialFunction,
Serializable {
+
+        private static final long serialVersionUID = -8804268799379350190L;
+        final RealMatrix factors;
+        final double[] target;
+        public LinearProblem(double[][] factors, double[] target) {
+            this.factors = new BlockRealMatrix(factors);
+            this.target  = target;
+        }
+
+        public double[] value(double[] variables) {
+            return factors.operate(variables);
+        }
+
+        public MultivariateMatrixFunction jacobian() {
+            return new MultivariateMatrixFunction() {
+                private static final long serialVersionUID = -8387467946663627585L;
+                public double[][] value(double[] point) {
+                    return factors.getData();
+                }
+            };
+        }
+
+    }
+
+}

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Added: commons/proper/math/trunk/src/test/org/apache/commons/math/optimization/MultiStartMultivariateRealOptimizerTest.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/test/org/apache/commons/math/optimization/MultiStartMultivariateRealOptimizerTest.java?rev=786467&view=auto
==============================================================================
--- commons/proper/math/trunk/src/test/org/apache/commons/math/optimization/MultiStartMultivariateRealOptimizerTest.java
(added)
+++ commons/proper/math/trunk/src/test/org/apache/commons/math/optimization/MultiStartMultivariateRealOptimizerTest.java
Fri Jun 19 12:04:01 2009
@@ -0,0 +1,83 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You 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.apache.commons.math.optimization;
+
+import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertTrue;
+
+import org.apache.commons.math.ConvergenceException;
+import org.apache.commons.math.FunctionEvaluationException;
+import org.apache.commons.math.analysis.MultivariateRealFunction;
+import org.apache.commons.math.optimization.direct.NelderMead;
+import org.apache.commons.math.random.GaussianRandomGenerator;
+import org.apache.commons.math.random.JDKRandomGenerator;
+import org.apache.commons.math.random.RandomVectorGenerator;
+import org.apache.commons.math.random.UncorrelatedRandomVectorGenerator;
+import org.junit.Test;
+
+public class MultiStartMultivariateRealOptimizerTest {
+
+  @Test
+  public void testRosenbrock()
+    throws FunctionEvaluationException, ConvergenceException {
+
+    Rosenbrock rosenbrock = new Rosenbrock();
+    NelderMead underlying = new NelderMead();
+    underlying.setStartConfiguration(new double[][] {
+                                         { -1.2,  1.0 }, { 0.9, 1.2 } , {  3.5, -2.3 }
+                                     });
+    JDKRandomGenerator g = new JDKRandomGenerator();
+    g.setSeed(16069223052l);
+    RandomVectorGenerator generator =
+        new UncorrelatedRandomVectorGenerator(2, new GaussianRandomGenerator(g));
+    MultiStartMultivariateRealOptimizer optimizer =
+        new MultiStartMultivariateRealOptimizer(underlying, 10, generator);
+    optimizer.setConvergenceChecker(new SimpleScalarValueChecker(-1, 1.0e-3));
+    optimizer.setMaxIterations(100);
+    RealPointValuePair optimum =
+        optimizer.optimize(rosenbrock, GoalType.MINIMIZE, new double[] { -1.2, 1.0 });
+
+    assertEquals(rosenbrock.getCount(), optimizer.getEvaluations());
+    assertTrue(optimizer.getEvaluations() > 20);
+    assertTrue(optimizer.getEvaluations() < 250);
+    assertTrue(optimum.getValue() < 8.0e-4);
+
+  }
+
+  private class Rosenbrock implements MultivariateRealFunction {
+
+      private int count;
+
+      public Rosenbrock() {
+          count = 0;
+      }
+
+      public double value(double[] x) throws FunctionEvaluationException {
+          ++count;
+          double a = x[1] - x[0] * x[0];
+          double b = 1.0 - x[0];
+          return 100 * a * a + b * b;
+      }
+
+      public int getCount() {
+          return count;
+      }
+
+  }
+
+}

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