A high-performance general-purpose compute library

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machine_learning/bagging.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);
}
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;
}
arrayfire.h
af::array
A multi dimensional data container.
Definition array.h:37
af::array::dims
dim4 dims() const
Get dimensions of the array.
af::array::eval
void eval() const
Evaluate any JIT expressions to generate data for the array.
af::array::T
array T() const
Get the transposed the array.
af::array::elements
dim_t elements() const
Get the total number of elements across all dimensions of the array.
af::exception
An ArrayFire exception class.
Definition exception.h:22
af::exception::what
virtual const char * what() const
Returns an error message for the exception in a string format.
Definition exception.h:46
af::seq
seq is used to create sequences for indexing af::array
Definition seq.h:46
af::floor
array floor(const array &in)
C++ Interface to floor numbers.
af::constant
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.
af::info
AFAPI void info()
af::setDevice
AFAPI void setDevice(const int device)
Sets the current device.
af::lookup
AFAPI array lookup(const array &in, const array &idx, const int dim=-1)
Lookup the values of an input array by indexing with another array.
af::moddims
AFAPI array moddims(const array &in, const dim4 &dims)
C++ Interface to modify the dimensions of an input array to a specified shape.
af::randu
AFAPI array randu(const dim4 &dims, const dtype ty, randomEngine &r)
C++ Interface to create an array of random numbers uniformly distributed.
af::max
AFAPI array max(const array &in, const int dim=-1)
C++ Interface to return the maximum along a given dimension.
af::min
AFAPI array min(const array &in, const int dim=-1)
C++ Interface to return the minimum along a given dimension.
af
Definition algorithm.h:15
util.h