/**
* WARNING:
*
* Any code here may be moved to the h2o-droplets repository in the future!
*/
package main.java.droplets;
import water.H2O;
import water.Key;
import water.MRTask;
import water.fvec.Chunk;
import water.fvec.Frame;
import water.fvec.NFSFileVec;
import water.fvec.Vec;
import java.io.File;
import java.text.DecimalFormat;
import java.util.Random;
/**
* Simplified version of H2O k-means algorithm for better readability.
*/
public class KMeansDroplet {
public static void initCloud() {
// Setup cloud name
String[] args = new String[] { "-name", "h2o_test_cloud"};
// Build a cloud of 1
H2O.main(args);
H2O.waitForCloudSize(1, 10*1000 /* ms */);
}
public static void main(String[] args) throws Exception {
initCloud();
// Load and parse a file. Data is distributed to other nodes in a round-robin way
File f = new File("smalldata/glm_test/gaussian.csv");
NFSFileVec nfs = NFSFileVec.make(f);
Frame frame = water.parser.ParseDataset.parse(Key.make(),nfs._key);
// Optionally create a frame with fewer columns, e.g. skip first
frame.remove(0);
// Create k centers as arrays of doubles
int k = 7;
double[][] centers = new double[k][frame.vecs().length];
// Initialize first cluster center to random row
Random rand = new Random();
for( int cluster = 0; cluster < centers.length; cluster++ ) {
long row = Math.max(0, (long) (rand.nextDouble() * frame.vecs().length) - 1);
for( int i = 0; i < frame.vecs().length; i++ ) {
Vec v = frame.vecs()[i];
centers[cluster][i] = v.at(row);
}
}
// Iterate over the dataset and show error for each step
int NUM_ITERS = 10;
for( int i = 0; i < NUM_ITERS; i++ ) {
KMeans task = new KMeans();
task._centers = centers;
task.doAll(frame);
for( int c = 0; c < centers.length; c++ ) {
if( task._size[c] > 0 ) {
for( int v = 0; v < frame.vecs().length; v++ ) {
double value = task._sums[c][v] / task._size[c];
centers[c][v] = value;
}
}
}
System.out.println("Error is " + task._error);
}
System.out.println("Cluster Centers:");
DecimalFormat df = new DecimalFormat("#.00");
for (double[] center : centers) {
for (int v = 0; v < frame.vecs().length; v++)
System.out.print(df.format(center[v]) + ", ");
System.out.println("");
}
System.exit(0);
}
/**
* For more complex tasks like this one, it is useful to marks fields that are provided by the
* caller (IN), and fields generated by the task (OUT). IN fields can then be set to null when the
* task is done using them, so that they do not get serialized back to the caller.
*/
public static class KMeans extends MRTask<KMeans> {
double[][] _centers; // IN: Centroids/cluster centers
double[][] _sums; // OUT: Sum of features in each cluster
int[] _size; // OUT: Row counts in each cluster
double _error; // OUT: Total sqr distance
@Override public void map(Chunk[] chunks) {
_sums = new double[_centers.length][chunks.length];
_size = new int[_centers.length];
// Find nearest cluster for each row
for( int row = 0; row < chunks[0]._len; row++ ) {
int nearest = -1;
double minSqr = Double.MAX_VALUE;
for( int cluster = 0; cluster < _centers.length; cluster++ ) {
double sqr = 0; // Sum of dimensional distances
for( int column = 0; column < chunks.length; column++ ) {
double delta = chunks[column].at0(row) - _centers[cluster][column];
sqr += delta * delta;
}
if( sqr < minSqr ) {
nearest = cluster;
minSqr = sqr;
}
}
_error += minSqr;
// Add values and increment counter for chosen cluster
for( int column = 0; column < chunks.length; column++ )
_sums[nearest][column] += chunks[column].at0(row);
_size[nearest]++;
}
_centers = null;
}
@Override public void reduce(KMeans task) {
for( int cluster = 0; cluster < _size.length; cluster++ ) {
for( int column = 0; column < _sums[0].length; column++ )
_sums[cluster][column] += task._sums[cluster][column];
_size[cluster] += task._size[cluster];
}
_error += task._error;
}
}
}