quickstart.txt

来自「Java方面的数值算法」· 文本 代码 · 共 48 行

Once you have downloaded the library, you can start using it in you Java 
programs. For now this guide features some basis sample programs that should
get you under way.

+----------------------------------------------+
| 1. Creating a dataset                        |
| 2. Creating and using a clustering algorithm |
+----------------------------------------------+

1. Creating a dataset
=====================
The code snippter below will create a two-dimensional dataset with four clusters of 
objects, one in each corner.

Dataset data = new SimpleDataset();
double small=1.0f/4.0f;
double large=3.0f/4.0f;
Random rg = new Random(System.currentTimeMillis());
for (int i = 0; i < itemsPerCluster; i++) {
    // lower left
	double[] vec1 = {  ((rg.nextGaussian() * clusterSpread)  + space * small),
    	 ((rg.nextGaussian() * clusterSpread)  + space * small) };
    data.addInstance(new SimpleInstance(vec1));
	// upper left
    double[] vec2 = {  ((rg.nextGaussian() * clusterSpread)  + space * small),
    	 ((rg.nextGaussian() * clusterSpread)  + space * large) };
    data.addInstance(new SimpleInstance(vec2));
	// lower righ
    double[] vec3 = {  ((rg.nextGaussian() * clusterSpread)  + space * large),
    	 ((rg.nextGaussian() * clusterSpread)  + space * small) };
    data.addInstance(new SimpleInstance(vec3));
    // upper right
    double[] vec4= {  ((rg.nextGaussian() * clusterSpread)  + space * large),
    	 ((rg.nextGaussian() * clusterSpread)  + space * large) };
    data.addInstance(new SimpleInstance(vec4));
}

2. Creating and using a clustering algorithm
============================================
The following code snippet will take as input the dataset created in the 
previous section, or any other dataset. 

XMeans km = new XMeans();
Dataset[] clusters = km.executeClustering(data);

In the array clusters, you find the different clusters that are the output of 
the algorithm.