* Copyright (c) 2014, ArrayFire
* All rights reserved.
* This file is distributed under 3-clause BSD license.
* The complete license agreement can be obtained at:
#include <arrayfire.h>
#include <math.h>
#include <stdio.h>
#include <af/util.h>
#include <string>
#include <vector>
#include "mnist_common.h"
using namespace af;
// Get accuracy of the predicted results
float accuracy(const array &predicted, const array &target) {
return 100 * count<float>(predicted == target) / target.elements();
// Calculate all the distances from testing set to training set
array distance(array train, array test) {
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);
// Iterate over each attribute
for (int ii = 0; ii < feat_len; ii++) {
// Get a attribute vectors
array train_i = train(span, ii);
array test_i = test(span, ii).T();
// Tile the vectors to generate matrices
array train_tiled = tile(train_i, 1, num_test);
array test_tiled = tile(test_i, num_train, 1);
// Add the distance for this attribute
dist = dist + abs(train_tiled - test_tiled);
dist.eval(); // Necessary to free up train_i, test_i
return dist;
array knn(array &train_feats, array &test_feats, array &train_labels) {
// Find distances between training and testing sets
array dist = distance(train_feats, test_feats);
// Find the neighbor producing the minimum distance
array val, idx;
min(val, idx, dist);
// Return the labels
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;
// Load mnist data
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();
// Get the predicted results
array res_labels = knn(train_feats, test_feats, train_labels);
double test_time = timer::stop();
// Results
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);
} catch (af::exception &ae) { std::cerr << ae.what() << std::endl; }
return 0;
AFAPI void info()
array constant(T val, const dim4 &dims, const dtype ty=(af_dtype) dtype_traits< T >::ctype)
AFAPI array moddims(const array &in, const unsigned ndims, const dim_t *const dims)
AFAPI void setDevice(const int device)
Sets the current device.
static AFAPI timer start()
AFAPI array abs(const array &in)
C++ Interface for absolute value.
A multi dimensional data container.
Definition: array.h:35
Definition: algorithm.h:15
dim_t elements() const
Get the total number of elements across all dimensions of the array.
void eval() const
Evaluate any JIT expressions to generate data for the array.
An ArrayFire exception class.
Definition: exception.h:29
AFAPI array tile(const array &in, const unsigned x, const unsigned y=1, const unsigned z=1, const unsigned w=1)
AFAPI seq span
A special value representing the entire axis of an af::array.
dim4 dims() const
Get dimensions of the array.
static AFAPI double stop()
virtual const char * what() const
Returns an error message for the exception in a string format.
Definition: exception.h:60
array T() const
Get the transposed the array.
AFAPI array min(const array &in, const int dim=-1)
C++ Interface for minimum values in an array.