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(); Apache Commons Math 3.5 发布下载地址
