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Posted to commits@commons.apache.org by er...@apache.org on 2022/01/13 14:53:47 UTC
[commons-math] branch master updated (9b0fc1f -> 336811d)
This is an automated email from the ASF dual-hosted git repository.
erans pushed a change to branch master
in repository https://gitbox.apache.org/repos/asf/commons-math.git.
from 9b0fc1f Allow successful build on Java 9+.
new 7bb6190 Remove spurious file.
new 336811d Relax tolerance (unit tests).
The 2 revisions listed above as "new" are entirely new to this
repository and will be described in separate emails. The revisions
listed as "add" were already present in the repository and have only
been added to this reference.
Summary of changes:
.../math4/legacy/stat/StatUtilsTest.java.orig | 556 ---------------------
.../noderiv/std_test_func.simplex.nelder_mead.csv | 4 +-
2 files changed, 2 insertions(+), 558 deletions(-)
delete mode 100644 commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/stat/StatUtilsTest.java.orig
[commons-math] 01/02: Remove spurious file.
Posted by er...@apache.org.
This is an automated email from the ASF dual-hosted git repository.
erans pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/commons-math.git
commit 7bb61905845a0e2e3c2b153d04e1ddd01b07b4f8
Author: Gilles Sadowski <gi...@gmail.com>
AuthorDate: Thu Jan 13 15:50:22 2022 +0100
Remove spurious file.
Thanks to Karl-Philipp Richter.
Closes #201.
---
.../math4/legacy/stat/StatUtilsTest.java.orig | 556 ---------------------
1 file changed, 556 deletions(-)
diff --git a/commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/stat/StatUtilsTest.java.orig b/commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/stat/StatUtilsTest.java.orig
deleted file mode 100644
index dad6cd5..0000000
--- a/commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/stat/StatUtilsTest.java.orig
+++ /dev/null
@@ -1,556 +0,0 @@
-/*
- * 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.math4.stat;
-
-
-import org.apache.commons.math4.TestUtils;
-import org.apache.commons.math4.exception.MathIllegalArgumentException;
-import org.apache.commons.math4.exception.NullArgumentException;
-import org.apache.commons.math4.stat.descriptive.DescriptiveStatistics;
-import org.apache.commons.math4.util.FastMath;
-import org.apache.commons.numbers.core.Precision;
-import org.junit.Assert;
-import org.junit.Test;
-
-/**
- * Test cases for the {@link StatUtils} class.
- */
-
-public final class StatUtilsTest {
-
- private static final double ONE = 1;
- private static final float TWO = 2;
- private static final int THREE = 3;
- private static final double MEAN = 2;
- private static final double SUMSQ = 18;
- private static final double SUM = 8;
- private static final double VAR = 0.666666666666666666667;
- private static final double MIN = 1;
- private static final double MAX = 3;
- private static final double TOLERANCE = 10E-15;
- private static final double NAN = Double.NaN;
-
- /** test stats */
- @Test
- public void testStats() {
- double[] values = new double[] { ONE, TWO, TWO, THREE };
- Assert.assertEquals("sum", SUM, StatUtils.sum(values), TOLERANCE);
- Assert.assertEquals("sumsq", SUMSQ, StatUtils.sumSq(values), TOLERANCE);
- Assert.assertEquals("var", VAR, StatUtils.variance(values), TOLERANCE);
- Assert.assertEquals("var with mean", VAR, StatUtils.variance(values, MEAN), TOLERANCE);
- Assert.assertEquals("mean", MEAN, StatUtils.mean(values), TOLERANCE);
- Assert.assertEquals("min", MIN, StatUtils.min(values), TOLERANCE);
- Assert.assertEquals("max", MAX, StatUtils.max(values), TOLERANCE);
- }
-
- @Test
- public void testN0andN1Conditions() {
- double[] values = new double[0];
-
- Assert.assertTrue(
- "Mean of n = 0 set should be NaN",
- Double.isNaN(StatUtils.mean(values)));
- Assert.assertTrue(
- "Variance of n = 0 set should be NaN",
- Double.isNaN(StatUtils.variance(values)));
-
- values = new double[] { ONE };
-
- Assert.assertTrue(
- "Mean of n = 1 set should be value of single item n1",
- StatUtils.mean(values) == ONE);
- Assert.assertTrue(
- "Variance of n = 1 set should be zero",
- StatUtils.variance(values) == 0);
- }
-
- @Test
- public void testArrayIndexConditions() {
- double[] values = { 1.0, 2.0, 3.0, 4.0 };
-
- Assert.assertEquals(
- "Sum not expected",
- 5.0,
- StatUtils.sum(values, 1, 2),
- Double.MIN_VALUE);
- Assert.assertEquals(
- "Sum not expected",
- 3.0,
- StatUtils.sum(values, 0, 2),
- Double.MIN_VALUE);
- Assert.assertEquals(
- "Sum not expected",
- 7.0,
- StatUtils.sum(values, 2, 2),
- Double.MIN_VALUE);
-
- try {
- StatUtils.sum(values, 2, 3);
- Assert.fail("Expected RuntimeException");
- } catch (RuntimeException e) {
- // expected
- }
-
- try {
- StatUtils.sum(values, -1, 2);
- Assert.fail("Expected RuntimeException");
- } catch (RuntimeException e) {
- // expected
- }
-
- }
-
- @Test
- public void testSumSq() {
- double[] x = null;
-
- // test null
- try {
- StatUtils.sumSq(x);
- Assert.fail("null is not a valid data array.");
- } catch (NullArgumentException ex) {
- // success
- }
-
- try {
- StatUtils.sumSq(x, 0, 4);
- Assert.fail("null is not a valid data array.");
- } catch (NullArgumentException ex) {
- // success
- }
-
- // test empty
- x = new double[] {};
- TestUtils.assertEquals(0, StatUtils.sumSq(x), TOLERANCE);
- TestUtils.assertEquals(0, StatUtils.sumSq(x, 0, 0), TOLERANCE);
-
- // test one
- x = new double[] {TWO};
- TestUtils.assertEquals(4, StatUtils.sumSq(x), TOLERANCE);
- TestUtils.assertEquals(4, StatUtils.sumSq(x, 0, 1), TOLERANCE);
-
- // test many
- x = new double[] {ONE, TWO, TWO, THREE};
- TestUtils.assertEquals(18, StatUtils.sumSq(x), TOLERANCE);
- TestUtils.assertEquals(8, StatUtils.sumSq(x, 1, 2), TOLERANCE);
- }
-
- @Test
- public void testProduct() {
- double[] x = null;
-
- // test null
- try {
- StatUtils.product(x);
- Assert.fail("null is not a valid data array.");
- } catch (NullArgumentException ex) {
- // success
- }
-
- try {
- StatUtils.product(x, 0, 4);
- Assert.fail("null is not a valid data array.");
- } catch (NullArgumentException ex) {
- // success
- }
-
- // test empty
- x = new double[] {};
- TestUtils.assertEquals(1, StatUtils.product(x), TOLERANCE);
- TestUtils.assertEquals(1, StatUtils.product(x, 0, 0), TOLERANCE);
-
- // test one
- x = new double[] {TWO};
- TestUtils.assertEquals(TWO, StatUtils.product(x), TOLERANCE);
- TestUtils.assertEquals(TWO, StatUtils.product(x, 0, 1), TOLERANCE);
-
- // test many
- x = new double[] {ONE, TWO, TWO, THREE};
- TestUtils.assertEquals(12, StatUtils.product(x), TOLERANCE);
- TestUtils.assertEquals(4, StatUtils.product(x, 1, 2), TOLERANCE);
- }
-
- @Test
- public void testSumLog() {
- double[] x = null;
-
- // test null
- try {
- StatUtils.sumLog(x);
- Assert.fail("null is not a valid data array.");
- } catch (NullArgumentException ex) {
- // success
- }
-
- try {
- StatUtils.sumLog(x, 0, 4);
- Assert.fail("null is not a valid data array.");
- } catch (NullArgumentException ex) {
- // success
- }
-
- // test empty
- x = new double[] {};
- TestUtils.assertEquals(0, StatUtils.sumLog(x), TOLERANCE);
- TestUtils.assertEquals(0, StatUtils.sumLog(x, 0, 0), TOLERANCE);
-
- // test one
- x = new double[] {TWO};
- TestUtils.assertEquals(FastMath.log(TWO), StatUtils.sumLog(x), TOLERANCE);
- TestUtils.assertEquals(FastMath.log(TWO), StatUtils.sumLog(x, 0, 1), TOLERANCE);
-
- // test many
- x = new double[] {ONE, TWO, TWO, THREE};
- TestUtils.assertEquals(FastMath.log(ONE) + 2.0 * FastMath.log(TWO) + FastMath.log(THREE), StatUtils.sumLog(x), TOLERANCE);
- TestUtils.assertEquals(2.0 * FastMath.log(TWO), StatUtils.sumLog(x, 1, 2), TOLERANCE);
- }
-
- @Test
- public void testMean() {
- double[] x = null;
-
- try {
- StatUtils.mean(x, 0, 4);
- Assert.fail("null is not a valid data array.");
- } catch (NullArgumentException ex) {
- // success
- }
-
- // test empty
- x = new double[] {};
- TestUtils.assertEquals(Double.NaN, StatUtils.mean(x, 0, 0), TOLERANCE);
-
- // test one
- x = new double[] {TWO};
- TestUtils.assertEquals(TWO, StatUtils.mean(x, 0, 1), TOLERANCE);
-
- // test many
- x = new double[] {ONE, TWO, TWO, THREE};
- TestUtils.assertEquals(2.5, StatUtils.mean(x, 2, 2), TOLERANCE);
- }
-
- @Test
- public void testVariance() {
- double[] x = null;
-
- try {
- StatUtils.variance(x, 0, 4);
- Assert.fail("null is not a valid data array.");
- } catch (NullArgumentException ex) {
- // success
- }
-
- // test empty
- x = new double[] {};
- TestUtils.assertEquals(Double.NaN, StatUtils.variance(x, 0, 0), TOLERANCE);
-
- // test one
- x = new double[] {TWO};
- TestUtils.assertEquals(0.0, StatUtils.variance(x, 0, 1), TOLERANCE);
-
- // test many
- x = new double[] {ONE, TWO, TWO, THREE};
- TestUtils.assertEquals(0.5, StatUtils.variance(x, 2, 2), TOLERANCE);
-
- // test precomputed mean
- x = new double[] {ONE, TWO, TWO, THREE};
- TestUtils.assertEquals(0.5, StatUtils.variance(x,2.5, 2, 2), TOLERANCE);
- }
-
- @Test
- public void testPopulationVariance() {
- double[] x = null;
-
- try {
- StatUtils.variance(x, 0, 4);
- Assert.fail("null is not a valid data array.");
- } catch (NullArgumentException ex) {
- // success
- }
-
- // test empty
- x = new double[] {};
- TestUtils.assertEquals(Double.NaN, StatUtils.populationVariance(x, 0, 0), TOLERANCE);
-
- // test one
- x = new double[] {TWO};
- TestUtils.assertEquals(0.0, StatUtils.populationVariance(x, 0, 1), TOLERANCE);
-
- // test many
- x = new double[] {ONE, TWO, TWO, THREE};
- TestUtils.assertEquals(0.25, StatUtils.populationVariance(x, 0, 2), TOLERANCE);
-
- // test precomputed mean
- x = new double[] {ONE, TWO, TWO, THREE};
- TestUtils.assertEquals(0.25, StatUtils.populationVariance(x, 2.5, 2, 2), TOLERANCE);
- }
-
-
- @Test
- public void testMax() {
- double[] x = null;
-
- try {
- StatUtils.max(x, 0, 4);
- Assert.fail("null is not a valid data array.");
- } catch (NullArgumentException ex) {
- // success
- }
-
- // test empty
- x = new double[] {};
- TestUtils.assertEquals(Double.NaN, StatUtils.max(x, 0, 0), TOLERANCE);
-
- // test one
- x = new double[] {TWO};
- TestUtils.assertEquals(TWO, StatUtils.max(x, 0, 1), TOLERANCE);
-
- // test many
- x = new double[] {ONE, TWO, TWO, THREE};
- TestUtils.assertEquals(THREE, StatUtils.max(x, 1, 3), TOLERANCE);
-
- // test first nan is ignored
- x = new double[] {NAN, TWO, THREE};
- TestUtils.assertEquals(THREE, StatUtils.max(x), TOLERANCE);
-
- // test middle nan is ignored
- x = new double[] {ONE, NAN, THREE};
- TestUtils.assertEquals(THREE, StatUtils.max(x), TOLERANCE);
-
- // test last nan is ignored
- x = new double[] {ONE, TWO, NAN};
- TestUtils.assertEquals(TWO, StatUtils.max(x), TOLERANCE);
-
- // test all nan returns nan
- x = new double[] {NAN, NAN, NAN};
- TestUtils.assertEquals(NAN, StatUtils.max(x), TOLERANCE);
- }
-
- @Test
- public void testMin() {
- double[] x = null;
-
- try {
- StatUtils.min(x, 0, 4);
- Assert.fail("null is not a valid data array.");
- } catch (NullArgumentException ex) {
- // success
- }
-
- // test empty
- x = new double[] {};
- TestUtils.assertEquals(Double.NaN, StatUtils.min(x, 0, 0), TOLERANCE);
-
- // test one
- x = new double[] {TWO};
- TestUtils.assertEquals(TWO, StatUtils.min(x, 0, 1), TOLERANCE);
-
- // test many
- x = new double[] {ONE, TWO, TWO, THREE};
- TestUtils.assertEquals(TWO, StatUtils.min(x, 1, 3), TOLERANCE);
-
- // test first nan is ignored
- x = new double[] {NAN, TWO, THREE};
- TestUtils.assertEquals(TWO, StatUtils.min(x), TOLERANCE);
-
- // test middle nan is ignored
- x = new double[] {ONE, NAN, THREE};
- TestUtils.assertEquals(ONE, StatUtils.min(x), TOLERANCE);
-
- // test last nan is ignored
- x = new double[] {ONE, TWO, NAN};
- TestUtils.assertEquals(ONE, StatUtils.min(x), TOLERANCE);
-
- // test all nan returns nan
- x = new double[] {NAN, NAN, NAN};
- TestUtils.assertEquals(NAN, StatUtils.min(x), TOLERANCE);
- }
-
- @Test
- public void testPercentile() {
- double[] x = null;
-
- // test null
- try {
- StatUtils.percentile(x, .25);
- Assert.fail("null is not a valid data array.");
- } catch (NullArgumentException ex) {
- // success
- }
-
- try {
- StatUtils.percentile(x, 0, 4, 0.25);
- Assert.fail("null is not a valid data array.");
- } catch (NullArgumentException ex) {
- // success
- }
-
- // test empty
- x = new double[] {};
- TestUtils.assertEquals(Double.NaN, StatUtils.percentile(x, 25), TOLERANCE);
- TestUtils.assertEquals(Double.NaN, StatUtils.percentile(x, 0, 0, 25), TOLERANCE);
-
- // test one
- x = new double[] {TWO};
- TestUtils.assertEquals(TWO, StatUtils.percentile(x, 25), TOLERANCE);
- TestUtils.assertEquals(TWO, StatUtils.percentile(x, 0, 1, 25), TOLERANCE);
-
- // test many
- x = new double[] {ONE, TWO, TWO, THREE};
- TestUtils.assertEquals(2.5, StatUtils.percentile(x, 70), TOLERANCE);
- TestUtils.assertEquals(2.5, StatUtils.percentile(x, 1, 3, 62.5), TOLERANCE);
- }
-
- @Test
- public void testDifferenceStats() {
- double sample1[] = {1d, 2d, 3d, 4d};
- double sample2[] = {1d, 3d, 4d, 2d};
- double diff[] = {0d, -1d, -1d, 2d};
- double small[] = {1d, 4d};
- double meanDifference = StatUtils.meanDifference(sample1, sample2);
- Assert.assertEquals(StatUtils.sumDifference(sample1, sample2), StatUtils.sum(diff), TOLERANCE);
- Assert.assertEquals(meanDifference, StatUtils.mean(diff), TOLERANCE);
- Assert.assertEquals(StatUtils.varianceDifference(sample1, sample2, meanDifference),
- StatUtils.variance(diff), TOLERANCE);
- try {
- StatUtils.meanDifference(sample1, small);
- Assert.fail("Expecting MathIllegalArgumentException");
- } catch (MathIllegalArgumentException ex) {
- // expected
- }
- try {
- StatUtils.varianceDifference(sample1, small, meanDifference);
- Assert.fail("Expecting MathIllegalArgumentException");
- } catch (MathIllegalArgumentException ex) {
- // expected
- }
- try {
- double[] single = {1.0};
- StatUtils.varianceDifference(single, single, meanDifference);
- Assert.fail("Expecting MathIllegalArgumentException");
- } catch (MathIllegalArgumentException ex) {
- // expected
- }
- }
-
- @Test
- public void testGeometricMean() {
- double[] test = null;
- try {
- StatUtils.geometricMean(test);
- Assert.fail("Expecting NullArgumentException");
- } catch (NullArgumentException ex) {
- // expected
- }
- test = new double[] {2, 4, 6, 8};
- Assert.assertEquals(FastMath.exp(0.25d * StatUtils.sumLog(test)),
- StatUtils.geometricMean(test), Double.MIN_VALUE);
- Assert.assertEquals(FastMath.exp(0.5 * StatUtils.sumLog(test, 0, 2)),
- StatUtils.geometricMean(test, 0, 2), Double.MIN_VALUE);
- }
-
-
- /**
- * Run the test with the values 50 and 100 and assume standardized values
- */
-
- @Test
- public void testNormalize1() {
- double sample[] = { 50, 100 };
- double expectedSample[] = { -25 / FastMath.sqrt(1250), 25 / FastMath.sqrt(1250) };
- double[] out = StatUtils.normalize(sample);
- for (int i = 0; i < out.length; i++) {
- Assert.assertTrue(Precision.equals(out[i], expectedSample[i], 1));
- }
-
- }
-
- /**
- * Run with 77 random values, assuming that the outcome has a mean of 0 and a standard deviation of 1 with a
- * precision of 1E-10.
- */
-
- @Test
- public void testNormalize2() {
- // create an sample with 77 values
- int length = 77;
- double sample[] = new double[length];
- for (int i = 0; i < length; i++) {
- sample[i] = FastMath.random();
- }
- // normalize this sample
- double standardizedSample[] = StatUtils.normalize(sample);
-
- DescriptiveStatistics stats = new DescriptiveStatistics();
- // Add the data from the array
- for (int i = 0; i < length; i++) {
- stats.addValue(standardizedSample[i]);
- }
- // the calculations do have a limited precision
- double distance = 1E-10;
- // check the mean an standard deviation
- Assert.assertEquals(0.0, stats.getMean(), distance);
- Assert.assertEquals(1.0, stats.getStandardDeviation(), distance);
-
- }
-
- @Test
- public void testMode() {
- final double[] singleMode = {0, 1, 0, 2, 7, 11, 12};
- final double[] modeSingle = StatUtils.mode(singleMode);
- Assert.assertEquals(0, modeSingle[0], Double.MIN_VALUE);
- Assert.assertEquals(1, modeSingle.length);
-
- final double[] twoMode = {0, 1, 2, 0, 2, 3, 7, 11};
- final double[] modeDouble = StatUtils.mode(twoMode);
- Assert.assertEquals(0, modeDouble[0], Double.MIN_VALUE);
- Assert.assertEquals(2, modeDouble[1], Double.MIN_VALUE);
- Assert.assertEquals(2, modeDouble.length);
-
- final double[] nanInfested = {0, 0, 0, Double.NaN, Double.NaN, Double.NaN, Double.NaN, 2, 2, 2, 3, 5};
- final double[] modeNan = StatUtils.mode(nanInfested);
- Assert.assertEquals(0, modeNan[0], Double.MIN_VALUE);
- Assert.assertEquals(2, modeNan[1], Double.MIN_VALUE);
- Assert.assertEquals(2, modeNan.length);
-
- final double[] infInfested = {0, 0, Double.POSITIVE_INFINITY, Double.POSITIVE_INFINITY,
- Double.NEGATIVE_INFINITY, Double.NEGATIVE_INFINITY, 2, 2, 3, 5};
- final double[] modeInf = StatUtils.mode(infInfested);
- Assert.assertEquals(Double.NEGATIVE_INFINITY, modeInf[0], Double.MIN_VALUE);
- Assert.assertEquals(0, modeInf[1], Double.MIN_VALUE);
- Assert.assertEquals(2, modeInf[2], Double.MIN_VALUE);
- Assert.assertEquals(Double.POSITIVE_INFINITY, modeInf[3], Double.MIN_VALUE);
- Assert.assertEquals(4, modeInf.length);
-
- final double[] noData = {};
- final double[] modeNodata = StatUtils.mode(noData);
- Assert.assertEquals(0, modeNodata.length);
-
- final double[] nansOnly = {Double.NaN, Double.NaN};
- final double[] modeNansOnly = StatUtils.mode(nansOnly);
- Assert.assertEquals(0, modeNansOnly.length);
-
- final double[] nullArray = null;
- try {
- StatUtils.mode(nullArray);
- Assert.fail("Expecting NullArgumentException");
- } catch (NullArgumentException ex) {
- // Expected
- }
- }
-
-}
[commons-math] 02/02: Relax tolerance (unit tests).
Posted by er...@apache.org.
This is an automated email from the ASF dual-hosted git repository.
erans pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/commons-math.git
commit 336811dff561ee2100874a700b60e289b0122bc7
Author: Gilles Sadowski <gi...@gmail.com>
AuthorDate: Thu Jan 13 15:51:32 2022 +0100
Relax tolerance (unit tests).
---
.../nonlinear/scalar/noderiv/std_test_func.simplex.nelder_mead.csv | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/commons-math-legacy/src/test/resources/org/apache/commons/math4/legacy/optim/nonlinear/scalar/noderiv/std_test_func.simplex.nelder_mead.csv b/commons-math-legacy/src/test/resources/org/apache/commons/math4/legacy/optim/nonlinear/scalar/noderiv/std_test_func.simplex.nelder_mead.csv
index 50cf143..1dcceb5 100644
--- a/commons-math-legacy/src/test/resources/org/apache/commons/math4/legacy/optim/nonlinear/scalar/noderiv/std_test_func.simplex.nelder_mead.csv
+++ b/commons-math-legacy/src/test/resources/org/apache/commons/math4/legacy/optim/nonlinear/scalar/noderiv/std_test_func.simplex.nelder_mead.csv
@@ -48,11 +48,11 @@ TWO_AXES, 2, 0 0, 3, 4, 1e-4, 120, false
CIG_TAB, 2, 0 0, 3, 4, 1e-3, 100, false
#CIG_TAB, 11, 0 0 0 0 0 0 0 0 0 0 0, 3, 4, 1e-3, 100, false
TABLET, 2, 0 0, 3, 4, 2e-4, 100, false
-TABLET, 11, 0 0 0 0 0 0 0 0 0 0 0, 3, 4, 2e-4, 3000, false
+TABLET, 11, 0 0 0 0 0 0 0 0 0 0 0, 3, 4, 2e-4, 3500, false
SUM_POW, 2, 0 0, 3, 4, 1e-2, 75, false
#SUM_POW, 11, 0 0 0 0 0 0 0 0 0 0 0, 3, 4, 1e-2, 75, false
ACKLEY, 2, 0 0, 2, 4, 1e-6, 145, false
-ACKLEY, 6, 0 0 0 0 0 0, 2, 4, 1e-6, 660, false
+ACKLEY, 6, 0 0 0 0 0 0, 2, 4, 1e-6, 700, false
RASTRIGIN, 2, 0 0, 6, 10, 5e-5, 105, false
GRIEWANK, 2, 0 0, 2, 3, 1e-1, 80, false
GRIEWANK, 11, 0 0 0 0 0 0 0 0 0 0 0, 2, 3, 1e-1, 1200, false