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Showing content from http://www.arrayfire.org/docs/machine_learning_2kmeans_8cpp-example.htm below:

ArrayFire: machine_learning/kmeans.cpp

#include <stdio.h>

#include <cstdlib>

#include <iostream>

#include <string>

data2 = tile(data, 1, k, 1);

means2 = tile(means, n, 1, 1);

return sum(abs(data2 - means2), 2);

}

dists = distance(data, means);

min(val, idx, dists, 1);

return idx;

}

means(span, ii, span) =

(data * (clustersd == ii)) / (

(clusters == ii) + 1e-5);

}

return means;

}

int iter = 100) {

n = in.

(0);

d = in.

(2);

data(span, span, ii) =

(in(span, span, ii) - minimum(ii).

<

>()) /

maximum(ii).

<

>();

}

for (int i = 0; i < iter; i++) {

prev_clusters = curr_clusters;

curr_clusters = clusterize(data, means);

unsigned num_changed = count<unsigned>(prev_clusters != curr_clusters);

if (num_changed < (n / 1000) + 1) break;

means = new_means(data, curr_clusters, k);

}

means(span, span, ii) =

maximum(ii) * means(span, span, ii) + minimum(ii);

}

clusters = prev_clusters;

}

int kmeans_demo(int k, bool console) {

printf("** ArrayFire K-Means Demo (k = %d) **\n\n", k);

(ASSETS_DIR

) / 255;

w = img.

(0), h = img.

(1), c = img.

(2);

means_full, clusters_full;

kmeans(means_full, clusters_full, vec, k);

means_half, clusters_half;

kmeans(means_half, clusters_half, vec, k / 2);

means_dbl, clusters_dbl;

kmeans(means_dbl, clusters_dbl, vec, k * 2);

if (!console) {

(means_full(span, clusters_full, span), img.

());

(means_half(span, clusters_half, span), img.

());

(means_dbl(span, clusters_dbl, span), img.

());

wnd(800, 800,

);

wnd.grid(2, 2);

std::stringstream out_full_caption, out_half_caption, out_dbl_caption;

out_full_caption << "k = " << k;

out_half_caption << "k = " << k / 2;

out_dbl_caption << "k = " << k * 2;

while (!wnd.close()) {

wnd(0, 0).image(img, "Input Image");

wnd(0, 1).image(out_full, out_full_caption.str().c_str());

wnd(1, 0).image(out_half, out_half_caption.str().c_str());

wnd(1, 1).image(out_dbl, out_dbl_caption.str().c_str());

wnd.show();

}

} else {

means_full =

means_half =

}

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]) : 8;

try {

return kmeans_demo(k, console);

return 0;

}

Window object to render af::arrays.

A multi dimensional data container.

T scalar() const

Get the first element of the array as a scalar.

dim4 dims() const

Get dimensions of the array.

An ArrayFire exception class.

virtual const char * what() const

Returns an error message for the exception in a string format.

seq is used to create sequences for indexing af::array

array constant(T val, const dim4 &dims, const dtype ty=(af_dtype) dtype_traits< T >::ctype)

C++ Interface to generate an array with elements set to a specified value.

AFAPI void setDevice(const int device)

Sets the current device.

AFAPI array loadImage(const char *filename, const bool is_color=false)

C++ Interface for loading an image.

AFAPI array moddims(const array &in, const dim4 &dims)

C++ Interface to modify the dimensions of an input array to a specified shape.

AFAPI array tile(const array &in, const unsigned x, const unsigned y=1, const unsigned z=1, const unsigned w=1)

C++ Interface to generate a tiled array.

AFAPI array randu(const dim4 &dims, const dtype ty, randomEngine &r)

C++ Interface to create an array of random numbers uniformly distributed.

AFAPI array max(const array &in, const int dim=-1)

C++ Interface to return the maximum along a given dimension.

AFAPI array min(const array &in, const int dim=-1)

C++ Interface to return the minimum along a given dimension.

AFAPI array sum(const array &in, const int dim=-1)

C++ Interface to sum array elements over a given dimension.

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