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arrayfire/examples/machine_learning/bagging.cpp at master · arrayfire/arrayfire · GitHub
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bagging.cpp
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bagging.cpp
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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
<
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
;
}
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