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Showing content from http://www.arrayfire.org/docs/machine_learning_2knn_8cpp-example.htm below:

ArrayFire: machine_learning/knn.cpp

#include <math.h>

#include <stdio.h>

#include <string>

#include <vector>

#include "mnist_common.h"

float

accuracy(

const array

&predicted,

const array

&target) {

return

100 * count<float>(predicted == target) / target.

elements

();

}

const int

feat_len = train.

dims

(1);

const int

num_train = train.

dims

(0);

const int

num_test = test.

dims

(0);

array

dist = constant(0, num_train, num_test);

for (int ii = 0; ii < feat_len; ii++) {

array

train_i = train(span, ii);

array

test_i = test(span, ii).

T

();

array

train_tiled = tile(train_i, 1, num_test);

array

test_tiled = tile(test_i, num_train, 1);

dist = dist + abs(train_tiled - test_tiled);

}

return dist;

}

array

dist = distance(train_feats, test_feats);

return train_labels(idx);

}

void knn_demo(bool console, int perc) {

array

train_images, train_labels;

array

test_images, test_labels;

int num_train, num_test, num_classes;

float frac = (float)(perc) / 100.0;

setup_mnist<false>(&num_classes, &num_train, &num_test, train_images,

test_images, train_labels, test_labels, frac);

int

feature_length = train_images.

elements

() / num_train;

array

train_feats =

moddims

(train_images, feature_length, num_train).

T

();

array

test_feats =

moddims

(test_images, feature_length, num_test).

T

();

timer::start();

array

res_labels = knn(train_feats, test_feats, train_labels);

double test_time = timer::stop();

printf("Accuracy on testing data: %2.2f\n",

accuracy(res_labels, test_labels));

printf("Prediction time: %4.4f\n", test_time);

if (!console) {

display_results<false>(test_images, res_labels, test_labels, 20);

}

}

int main(int argc, char **argv) {

int device = argc > 1 ? atoi(argv[1]) : 0;

bool console = argc > 2 ? argv[2][0] == '-' : false;

int perc = argc > 3 ? atoi(argv[3]) : 60;

try {

knn_demo(console, perc);

return 0;

}

A multi dimensional data container.

dim4 dims() const

Get dimensions of the array.

void eval() const

Evaluate any JIT expressions to generate data for the array.

array T() const

Get the transposed the array.

dim_t elements() const

Get the total number of elements across all dimensions of the array.

An ArrayFire exception class.

virtual const char * what() const

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

AFAPI void setDevice(const int device)

Sets the current device.

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

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


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