Java线性代数库jblas的应用举例

1.关于jblas

jblas is a fast linear algebra library for Java. jblas is based on BLAS and LAPACK, the de-facto industry standard for matrix computations, and uses state-of-the-art implementations like ATLAS for all its computational routines, making jBLAS very fast.

主页地址：http://jblas.org/
该库的API文档地址：http://jblas.org/javadoc/index.html
在Java工程中我们只要引入jar包文件就可以了。

下面是其小册子：

jblas - Fast matrix computations for Java

class="scribd_iframe_embed" src="https://www.scribd.com/embeds/104872010/content?start_page=1&view_mode=scroll&show_recommendations=true" scrolling="no" id="doc_32955" width="100%" height="600"> ## 2.共轭梯度下降的例子：

import org.jblas.*;
import static org.jblas.DoubleMatrix.*;
import static org.jblas.MatrixFunctions.*;/*** Example of how to implement conjugate gradienst with jblas.** Again, the main objective of this code is to show how to use* jblas, not how to package/modularize numerical code in Java ;)** Closely follows http://en.wikipedia.org/wiki/Conjugate_gradient_method*/
public class CG {public static void main(String[] args) {new CG().runExample();}void runExample() {int n = 100;double w = 1;double lambda = 1e-6;DoubleMatrix[] ds = sincDataset(n, 0.1);//ds[0].print();//ds[1].print();DoubleMatrix A = gaussianKernel(w, ds[0], ds[0]).add( eye(n).mul(lambda) );DoubleMatrix x = zeros(n);DoubleMatrix b = ds[1];cg(A, b, x, lambda);}DoubleMatrix cg(DoubleMatrix A, DoubleMatrix b, DoubleMatrix x, double thresh) {int n = x.length;DoubleMatrix r = b.sub(A.mmul(x));DoubleMatrix p = r.dup();double alpha = 0, beta = 0;DoubleMatrix r2 = zeros(n), Ap = zeros(n);while (true) {A.mmuli(p, Ap);alpha = r.dot(r) / p.dot(Ap);x.addi(p.mul(alpha));r.subi(Ap.mul(alpha), r2);double error = r2.norm2();System.out.printf("Residual error = %f\n", error);if (error < thresh)break;beta = r2.dot(r2) / r.dot(r);r2.addi(p.mul(beta), p);DoubleMatrix temp = r;r = r2;r2 = temp;}return x;}/*** Compute the Gaussian kernel for the rows of X and Z, and kernel width w.*/public static DoubleMatrix gaussianKernel(double w, DoubleMatrix X, DoubleMatrix Z) {DoubleMatrix d = Geometry.pairwiseSquaredDistances(X.transpose(), Z.transpose());return exp(d.div(w).neg());}/*** The sinc function (save version).** This version is save, as it replaces zero entries of x by 1.* Then, sinc(0) = sin(0) / 1 = 1.**/DoubleMatrix safeSinc(DoubleMatrix x) {return sin(x).div(x.add(x.eq(0)));}/*** Create a sinc data set.**   X ~ uniformly from -4..4*   Y ~ sinc(x) + noise * gaussian noise.*/DoubleMatrix[] sincDataset(int n, double noise) {DoubleMatrix X = rand(n).mul(8).sub(4);DoubleMatrix Y = safeSinc(X) .add( randn(n).mul(noise) );return new DoubleMatrix[] {X, Y};}
}

3.岭回归例子

import org.jblas.*;
import static org.jblas.DoubleMatrix.*;
import static org.jblas.MatrixFunctions.*;/*** A simple example which computes kernel ridge regression using jblas.** This code is by no means meant to be an example of how you should do* machine learning in Java, as there is absolutely no encapsulation. It* is rather just an example of how to use jblas to perform different kinds* of computations.*/
public class KRR {public static void main(String[] args) {int n = 1000;double w = Double.valueOf(args[0]);double lambda = Double.valueOf(args[1]);new KRR().run(n, w, lambda);}void run(int n, double w, double lambda) {DoubleMatrix[] ds = sincDataset(n, 0.1);DoubleMatrix alpha = learnKRR(ds[0], ds[1], w, lambda);DoubleMatrix Yh = predictKRR(ds[0], ds[0], w, alpha);DoubleMatrix XE = rand(1000).mul(8).sub(4);System.out.printf("Mean squared error = %.5f\n", mse(Yh, ds[1]));}/*** The sinc function.** This version is not save since it divides by zero if one* of the entries of x are zero.*/DoubleMatrix sinc(DoubleMatrix x) {return sin(x).div(x);}/*** The sinc function (save version).** This version is save, as it replaces zero entries of x by 1.* Then, sinc(0) = sin(0) / 1 = 1.**/DoubleMatrix safeSinc(DoubleMatrix x) {return sin(x).div(x.add(x.eq(0)));}/*** Create a sinc data set.**   X ~ uniformly from -4..4*   Y ~ sinc(x) + noise * gaussian noise.*/DoubleMatrix[] sincDataset(int n, double noise) {DoubleMatrix X = rand(n).mul(8).sub(4);DoubleMatrix Y = safeSinc(X) .add( randn(n).mul(noise) );return new DoubleMatrix[] {X, Y};}/*** Compute the alpha for Kernel Ridge Regression.** Computes alpha = (K + lambda I)^-1 Y.*/DoubleMatrix learnKRR(DoubleMatrix X, DoubleMatrix Y,double w, double lambda) {int n = X.rows;DoubleMatrix K = gaussianKernel(w, X, X);K.addi(eye(n).muli(lambda));DoubleMatrix alpha = Solve.solveSymmetric(K, Y);return alpha;}/*** Compute the Gaussian kernel for the rows of X and Z, and kernel width w.*/DoubleMatrix gaussianKernel(double w, DoubleMatrix X, DoubleMatrix Z) {DoubleMatrix d = Geometry.pairwiseSquaredDistances(X.transpose(), Z.transpose());return exp(d.div(w).neg());}/*** Predict KRR on XE which has been trained on X, w, and alpha.** In a real world application, you would put all the data from training* in a class of its own, of course!*/DoubleMatrix predictKRR(DoubleMatrix XE, DoubleMatrix X, double w, DoubleMatrix alpha) {DoubleMatrix K = gaussianKernel(w, XE, X);return K.mmul(alpha);}double mse(DoubleMatrix Y1, DoubleMatrix Y2) {DoubleMatrix diff = Y1.sub(Y2);return pow(diff, 2).mean();}
}

3.LBFGS的例子

相关例子和库文件jar包下载 http://download.csdn.net/detail/u012176591/8660849

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安装jar包的javadoc文档

加入jblas的jar包的javadoc文档，这样使得我们能够在eclipse中方便地查看方法或对象的参数和用法。
首先将Javadoc压缩文件解压到某处，如下：

Package Explorer视图中，选择jar包，右键单击Properties，在弹出框中作如下设置

在程序中，将鼠标放到类名或方法名上，自动弹出其用法。效果如下：

eclipse设置Java命令行执行参数

如下图所示，程序执行时需要两个参数，需要我们在执行程序之前对其进行设定。

下面是设置方法，首先右键java文件–>运行方式–>运行配置，打开配置文件，如下：

对自变量进行设置，这里设置两个数字，分别对应两个参数。