machine_learning/kmeans.cpp

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machine_learning/kmeans.cpp
/*******************************************************
* Copyright (c) 2014, ArrayFire
* All rights reserved.
*
* This file is distributed under 3-clause BSD license.
* The complete license agreement can be obtained at:
* http://arrayfire.com/licenses/BSD-3-Clause
********************************************************/
#include <iostream>
#include <stdio.h>
#include <arrayfire.h>
#include <af/util.h>
#include <cstdlib>
using namespace af;
array distance(array data, array means)
{
int n = data. dims(0); // Number of features
int k = means.dims(1); // Number of means
array data2 = tile(data , 1, k, 1);
array means2 = tile(means, n, 1, 1);
// Currently using manhattan distance
// Can be replaced with other distance measures
return sum(abs(data2 - means2), 2);
}
// Get cluster id of each location in data
array clusterize(const array data, const array means)
{
// Get manhattan distance
array dists = distance(data, means);
// get the locations of minimum distance
array idx, val;
min(val, idx, dists, 1);
// Return cluster IDs
return idx;
}
array new_means(array data, array clusters, int k)
{
int d = data.dims(2);
array means = constant(0, 1, k, d);
array clustersd = tile(clusters, 1, 1, d);
gfor (seq ii, k) {
means(span, ii, span) = sum(data * (clustersd == ii)) / (sum(clusters == ii) + 1e-5);
}
return means;
}
// kmeans(means, clusters, data, k)
// data: input, 1D or 2D (range > [0-1])
// k: input, # desired means (k > 1)
// means: output, vector of means
void kmeans(array &means, array &clusters, const array in, int k, int iter=100)
{
unsigned n = in.dims(0); // Num features
unsigned d = in.dims(2); // feature length
// reshape input
array data = in * 0;
// re-center and scale down data to [0, 1]
array minimum = min(in);
array maximum = max(in);
gfor(seq ii, d) {
data(span, span, ii) = (in(span, span, ii) - minimum(ii)) / maximum(ii);
}
// Initial guess of means
means = randu(1, k, d);
array curr_clusters = constant(0, data.dims(0)) - 1;
array prev_clusters;
// Stop updating after specified number of iterations
for (int i = 0; i < iter; i++) {
// Store previous cluster ids
prev_clusters = curr_clusters;
// Get cluster ids for current means
curr_clusters = clusterize(data, means);
// Break early if clusters not changing
unsigned num_changed = count<unsigned>(prev_clusters != curr_clusters);
if (num_changed < (n/1000) + 1) break;
// Update current means for new clusters
means = new_means(data, curr_clusters, k);
}
// Scale up means
gfor(seq ii, d) {
means(span, span, ii) = maximum(ii) * means(span, span, ii) + minimum(ii);
}
clusters = prev_clusters;
}
// K-Means image recoloring.
// Shifts the hues of an image to the k mean hues.
int kmeans_demo(int k, bool console)
{
printf("** ArrayFire K-Means Demo (k = %d) **\n\n", k);
array img = loadImage(ASSETS_DIR"/examples/images/lena.ppm", true) / 255; // [0-255]
int w = img.dims(0), h = img.dims(1), c = img.dims(2);
array vec = moddims(img, w * h, 1, c);
array means_full, clusters_full;
kmeans(means_full, clusters_full, vec, k);
array means_half, clusters_half;
kmeans(means_half, clusters_half, vec, k / 2);
array means_dbl, clusters_dbl;
kmeans(means_dbl, clusters_dbl, vec, k * 2);
if (!console) {
#if 0
array out_full = moddims(means_full(span, clusters_full, span), img.dims());
array out_half = moddims(means_half(span, clusters_half, span), img.dims());
array out_dbl = moddims(means_dbl (span, clusters_dbl , span), img.dims());
char str_full[32], str_half[32], str_dbl[32];
sprintf(str_full, "%2d clusters", k);
sprintf(str_half, "%2d clusters", k/2);
sprintf(str_dbl , "%2d clusters", k*2);
fig("color","default");
fig("sub",2,2,1); image(img); fig("title","input");
fig("sub",2,2,2); image(out_full); fig("title", str_full);
fig("sub",2,2,3); image(out_half); fig("title", str_half);
fig("sub",2,2,4); image(out_dbl ); fig("title", str_dbl );
printf("Hit enter to finish\n");
getchar();
#else
printf("Graphics not implemented yet\n");
#endif
} else {
means_full = moddims(means_full, means_full.dims(1), means_full.dims(2));
means_half = moddims(means_half, means_half.dims(1), means_half.dims(2));
means_dbl = moddims(means_dbl , means_dbl.dims(1) , means_dbl.dims(2) );
af_print(means_full);
af_print(means_half);
af_print(means_dbl );
}
return 0;
}
int main(int argc, char** argv)
{
int device = argc > 1 ? atoi(argv[1]) : 0;
bool console = argc > 2 ? argv[2][0] == '-' : false;
int k = argc > 3 ? atoi(argv[3]) : 16;
try {
af::setDevice(device);
af::info();
return kmeans_demo(k, console);
} catch (af::exception &ae) {
std::cerr << ae. what() << std::endl;
}
}