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/*******************************************************
* 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 <stdio.h>
#include <vector>
#include <string>
#include <af/util.h>
#include <math.h>
#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);
}
array bagging(array &train_feats, array &test_feats, array &train_labels,
int num_classes, int num_models, int sample_size)
{
int num_train = train_feats.dims(0);
int num_test = test_feats.dims(0);
array idx = floor(randu(sample_size, num_models) * num_train);
array labels_all = constant(0, num_test, num_classes);
array off = seq(num_test);
for (int i = 0; i < num_models; i++) {
array ii = idx(span, i);
array train_feats_ii = lookup(train_feats, ii, 0);
array train_labels_ii = train_labels(ii);
// Get the predicted results
array labels_ii = knn(train_feats_ii, test_feats, train_labels_ii);
array lidx = labels_ii * num_test + off;
labels_all(lidx) = labels_all(lidx) + 1;
}
array val, labels;
max(val, labels, labels_all, 1);
return labels;
}
void bagging_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();
int num_models = 10;
int sample_size = 1000;
timer::start();
// Get the predicted results
array res_labels = bagging(train_feats, test_feats, train_labels,
num_classes, num_models, sample_size);
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 (false && !console) {
display_results<false>(test_images, res_labels, test_labels.T(), 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 {
setDevice(device);
af::info();
bagging_demo(console, perc);
} catch (af::exception &ae) {
std::cerr << ae.what() << std::endl;
}
return 0;
}
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