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Apache Commons Math 3.5 发布

4月18日武汉源创会开始报名，送华为开发板

Apache Commons Math 3.5 发布，此版本是个小版本发布，主要是一些 bug 修复和新特性，现有特性的修改都向后兼容，允许替代 v3.4.1 JAR 文件。

值得关注的特性：DescriptiveStatistics SummaryStatistics 新增 getQuadraticMean 方法；还有大量的 bug 修复；最低要求 Java 5。强烈建议所有用户升级到最新版本！

请注意，这个版本标记弃用的大量的类和方法会在下一个重大版本 4.0 全部移除！

这是一个小更新版本，改进包括：

New features:

o Added a way to build polyhedrons sets from a list of vertices and

facets specified using vertices indices.

o Simplified "FastMath#exp(double)" in order to avoid a potential

Java 1.5 JIT bug when calling with negative infinity as argument. Issue: MATH-1198.

o Added method "getQuadraticMean()" to "DescriptiveStatistics"

and "SummaryStatistics" which calculates the root mean square. Issue: MATH-1199.

Fixed Bugs:

o Moved FastMathTestPerformance out of the main test tree, as is is

a benchmark rather than a test. Issue: MATH-1195.

o Fixed ignored method parameters in QRDecomposition protected methods. Issue: MATH-1191.

o Fixed wrong selection of line/polyhedron intersection point. Issue: MATH-1211. Thanks to Mike Zimmerman.

o Improved fix for corner cases in BSP-tree merging, when cut sub-hyperplanes vanish. Issue: MATH-1162.

o Fixed link to algorithm description in "PoissonDistribution#sample()". Issue: MATH-1209. Thanks to Jonathan Ogilvie.

o EmpiricalDistribution cumulativeProbability can return NaN when evaluated within a constant bin. Issue: MATH-1208.

o EmpiricalDistribution getKernel fails for buckets with only multiple instances of the same value. Issue: MATH-1203.

o "UnivariateSolverUtils#bracket(...)" sometimes failed to bracket

if a reached the lower bound. Issue: MATH-1204.

Changes:

o Added Laguerre complex solve methods taking maxEval parameters. Issue: MATH-1213.

完整改进请看发行说明：

http://www.apache.org/dist/commons/math/RELEASE-NOTES.txt

下载：

http://commons.apache.org/proper/commons-math/download_math.cgi

Commons Math 是 Apache 上一个轻量级自容器的数学和统计计算方法包，包含大多数常用的数值算法。

示例代码：

// Create a real matrix with two rows and three columns double[][] matrixData = { {1d,2d,3d}, {2d,5d,3d}}; RealMatrix m = new Array2DRowRealMatrix(matrixData);   // One more with three rows, two columns double[][] matrixData2 = { {1d,2d}, {2d,5d}, {1d, 7d}}; RealMatrix n = new Array2DRowRealMatrix(matrixData2);   // Note: The constructor copies  the input double[][] array.   // Now multiply m by n RealMatrix p = m.multiply(n); System.out.println(p.getRowDimension());    // 2 System.out.println(p.getColumnDimension()); // 2   // Invert p, using LU decomposition RealMatrix pInverse = new LUDecompositionImpl(p).getSolver().getInverse();

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