Apache Commons Math 3.5 发布

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　　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

　　>　　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();