Kohonen Map
Jump to navigation
Jump to search
This is about all I can remember on how Kohonen maps work, you may tweak this if you like. Fix any bugs that keeps it from actually working in a robot, etc.
package chase.meh;
/**
* This is a basic stand alone Kohonen map for use in Succubus, this allows me to worry
* about Kohonen Map stuff in the Kohonen Map, and robot stuff in the robot.
*
* @author Chase
*/
public class KohonenMap {
private KohonenNode[] map;
private int mapSize;
private long time;
/**
* The dimensions in the dim, are only used with the neighborhood function. The true
* dimensionality that makes Kohonen Maps work is the input and output weights.<br><br>
* The input weights are used to determine the BMU, while the output weights are unused
* @param dim Sets the size of the internal map. Where the length is the number of dimensions and
* each index determines the size of that particular dimension. So {8,4} would initialize a 2
* dimensional map, 8 wide, and 4 high.
* @param inputL The lower range of the input weights that each node contains. The number of entries
* determines the number of input weights.
* @param inputU The upper range of the input weights that each node contains. The number of entries
* determines the number of input weights.
* @param outputL The lower range of the output weights that each node contains. The number of entries
* determines the number of output weights.
* @param outputU The upper range of the output weights that each node contains. The number of entries
* determines the number of output weights.
*/
public KohonenMap(int[] dim, double[] inputL, double[] inputU,
double[] outputL, double[] outputU) {
mapSize = 1;
for(int i=0; i<dim.length; ++i)
mapSize *= dim[i];
map = new KohonenNode[mapSize];
//Logarithms have this thing against zero :)
time = 1;
/**
* Initialize each node.
*/
int tDim[] = new int[dim.length];
for(int i=0; i<mapSize; ++i) {
map[i] = new KohonenNode(inputL,inputU,outputL,outputU);
/** !! VERY IMPORTANT !! VERY IMPORTANT !!
* Here we are setting up the neighborhood,
* so it doesn't need to be calculated later
* which makes things much simpler.
*/
map[i].my_pos = tDim.clone();
++tDim[0];
for(int j=0;j<dim.length-1;++j) {
if(tDim[j] >= dim[j]) {
tDim[j+1]++;
tDim[j] = 0;
}
}
}
}
/**
* Returns the best matching unit for the given data
* @param input Data used to locate the BMU
* @return the best matching BMU for this input data
*/
public KohonenNode getBMU(double input[]) {
KohonenNode out = null;
double distance = Double.POSITIVE_INFINITY;
for(int i=0;i<mapSize;++i) {
KohonenNode n = map[i];
double dist = distance(input,n.input);
if(dist < distance) {
distance = dist;
out = n;
}
}
return out;
}
/**
* Updates this KohonenMap with the given data.
* @param input The input data that will be updated to the map
* @param output The output data that will be updated to the map
*/
public void update(double input[], double output[]) {
KohonenNode bmu = getBMU(input);
for(int i=0;i<mapSize;++i) {
map[i].update(bmu.my_pos, input, output);
}
++time;
}
/**
* Sets the internal time used for weighting the update data by time.
* The internal time automatically gets incremented with each update,
* calling this is unnecessary.
* @param time
*/
public void setTime(long time) {
if(time < 1) time = 1;
this.time = time;
}
/**
* This is a KohonenNode, it just contains the data
* @author Chase
*/
public class KohonenNode {
/** this is the location of this node in the neighborhood. */
private int[] my_pos;
public double[] input;
public double[] output;
/**
* @param inputL The lower range of the input weights that this node contains. The number of entries
* determines the number of input weights.
* @param inputU The upper range of the input weights that this node contains. The number of entries
* determines the number of input weights.
* @param outputL The lower range of the output weights that this node contains. The number of entries
* determines the number of output weights.
* @param outputU The upper range of the output weights that this node contains. The number of entries
* determines the number of output weights.
*/
public KohonenNode(double[] inputL, double[] inputU,
double[] outputL, double[] outputU) {
input = new double[inputL.length];
output = new double[outputL.length];
/** !! VERY IMPORTANT !! VERY IMPORTANT !!
* Randomize the startup data
*/
for(int i=0;i<input.length;++i) {
double range = inputU[i]-inputL[i];
range = Math.sqrt(range*range);
input[i] = Math.random()*range+inputL[i];
}
for(int i=0;i<output.length;++i) {
double range = outputU[i]-outputL[i];
range = Math.sqrt(range*range);
output[i] = Math.random()*range+outputL[i];
}
}
private double gaussian(double u) {
/** This normalizes it so that 0 = 1 (not ~0.4), about 2.5 */
final double oos2p = (1.0/Math.sqrt(2.0*Math.PI));
return oos2p*Math.pow(Math.E,-0.5*u*u);
}
private double weight(double current, double target, double neighborhood, double time) {
return current + neighborhood * time * (target - current);
}
private void update(int[] bmu, double input[], double output[]) {
double distance = neighborDistance(my_pos,bmu);
double neighborhood = gaussian(distance);
double timeFunc = 1.0/(Math.log(time)+0.01);
/** Beyond this point its benefit is negligible. */
if(neighborhood*timeFunc < 0.0001) return;
/** Update the input and output weights */
for(int i=0;i<input.length;++i) {
this.input[i] = weight(this.input[i], input[i], neighborhood, timeFunc);
}
for(int i=0;i<output.length;++i) {
this.output[i] = weight(this.output[i], output[i], neighborhood, timeFunc);
}
}
}
/**
* The weight distance function.
*/
public static final double distance(double[] p, double[] q) {
if(p == null || q == null) return Double.POSITIVE_INFINITY;
double k, d = 0;
for(int i = 0; i < p.length; i++) {
k = p[i] - q[i];
d += k*k;
}
return d;
}
/**
* The neighborhood distance function.
*/
public static final double neighborDistance(int[] p, int[] q) {
double k, d = 0;
for(int i = 0; i < p.length; i++) {
k = p[i] - q[i];
d += k*k;
}
return d;
}
}
