machine_learning/knn.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 <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();
 
    timer::start();
    // 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 {
        af::setDevice(device);
        af::info();
        knn_demo(console, perc);
 
    } catch (af::exception &ae) { std::cerr << ae.what() << std::endl; }
 
    return 0;
}