JarCastingjava-som
Package provides java implementation of self-organizing feature map (Kohonen map)
Install
Add the following dependency to your POM file:
<dependency>
<groupId>com.github.chen0040</groupId>
<artifactId>java-som</artifactId>
<version>1.0.2</version>
</dependency>
Usage
Spatial clustering using SOFM
The sample code below shows how to cluster a set of 2-D points (c1, c2) in space using SOFM:
DataQuery.DataFrameQueryBuilder schema = DataQuery.blank()
.newInput("c1")
.newInput("c2")
.newOutput("designed")
.end();
Sampler.DataSampleBuilder negativeSampler = new Sampler()
.forColumn("c1").generate((name, index) -> randn() * 0.3 + (index % 2 == 0 ? 2 : 4))
.forColumn("c2").generate((name, index) -> randn() * 0.3 + (index % 2 == 0 ? 2 : 4))
.forColumn("designed").generate((name, index) -> 0.0)
.end();
Sampler.DataSampleBuilder positiveSampler = new Sampler()
.forColumn("c1").generate((name, index) -> rand(-4, -2))
.forColumn("c2").generate((name, index) -> rand(-2, -4))
.forColumn("designed").generate((name, index) -> 1.0)
.end();
DataFrame data = schema.build();
data = negativeSampler.sample(data, 50);
data = positiveSampler.sample(data, 50);
System.out.println(data.head(10));
SOFM algorithm = new SOFM();
// create a 1x2 SOM grid for 2-clusters
algorithm.setColumnCount(2);
algorithm.setRowCount(1);
DataFrame learnedData = algorithm.fitAndTransform(data);
for(int i = 0; i < learnedData.rowCount(); ++i){
DataRow tuple = learnedData.row(i);
String clusterId = tuple.getCategoricalTargetCell("cluster");
System.out.println("learned: " + clusterId +"\tknown: "+tuple.target());
}
Image Segmentation (Clustering) using SOFM
The following sample code shows how to use SOFM to perform image segmentation:
BufferedImage img= ImageIO.read(FileUtils.getResource("1.jpg"));
DataFrame dataFrame = ImageDataFrameFactory.dataFrame(img);
SOFM cluster = new SOFM();
cluster.fit(dataFrame);
List<Integer> classColors = new ArrayList<Integer>();
for(int i=0; i < 5; ++i){
for(int j=0; j < 5; ++j){
classColors.add(ImageDataFrameFactory.get_rgb(255, rand.nextInt(255), rand.nextInt(255), rand.nextInt(255)));
}
}
BufferedImage segmented_image = new BufferedImage(img.getWidth(), img.getHeight(), img.getType());
for(int x=0; x < img.getWidth(); x++)
{
for(int y=0; y < img.getHeight(); y++)
{
int rgb = img.getRGB(x, y);
DataRow tuple = ImageDataFrameFactory.getPixelTuple(dataFrame, rgb);
int clusterIndex = cluster.transform(tuple);
rgb = classColors.get(clusterIndex % classColors.size());
segmented_image.setRGB(x, y, rgb);
}
}