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Merged
umar456 merged 1 commit into from
Feb 11, 2020
Merged

Ensure examples are compatible with older versions of C++ #2760

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3 changes: 2 additions & 1 deletion examples/CMakeLists.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -8,9 +8,10 @@
cmake_minimum_required(VERSION 3.0)
cmake_policy(VERSION 3.5)
project(ArrayFire-Examples
VERSION 3.5.0
VERSION 3.7.0
LANGUAGES CXX)

set(CMAKE_CXX_STANDARD 98)
if(NOT EXISTS "${ArrayFire_SOURCE_DIR}/CMakeLists.txt")
set(ASSETS_DIR "${CMAKE_CURRENT_SOURCE_DIR}/..")
endif()
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2 changes: 1 addition & 1 deletion examples/image_processing/confidence_connected_components.cpp
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Expand Up @@ -39,7 +39,7 @@ int main(int argc, char* argv[]) {

seedx = 15;
seedy = 15;
unsigned seedcoords[]{15, 15};
unsigned seedcoords[] = {15, 15};
array seeds(dim4(1, 2), seedcoords);
array background =
confidenceCC(A, seeds, radius, multiplier, iter, 255);
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26 changes: 14 additions & 12 deletions examples/machine_learning/kmeans.cpp
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Expand Up @@ -113,8 +113,7 @@ int kmeans_demo(int k, bool console) {
printf("** ArrayFire K-Means Demo (k = %d) **\n\n", k);

array img =
loadImage(ASSETS_DIR "/examples/images/spider.jpg") /
255; // [0-255]
loadImage(ASSETS_DIR "/examples/images/spider.jpg") / 255; // [0-255]

int w = img.dims(0), h = img.dims(1), c = img.dims(2);
array vec = moddims(img, w * h, 1, c);
Expand All @@ -129,23 +128,26 @@ int kmeans_demo(int k, bool console) {
kmeans(means_dbl, clusters_dbl, vec, k * 2);

if (!console) {
array out_full = moddims(means_full(span, clusters_full, span), img.dims());
array out_half = moddims(means_half(span, clusters_half, span), img.dims());
array out_dbl = moddims(means_dbl (span, clusters_dbl , span), img.dims());
array out_full =
moddims(means_full(span, clusters_full, span), img.dims());
array out_half =
moddims(means_half(span, clusters_half, span), img.dims());
array out_dbl =
moddims(means_dbl(span, clusters_dbl, span), img.dims());

af::Window wnd(800, 800, "ArrayFire K-Means Demo");
wnd.grid(2, 2);
std::string out_full_caption = "k = " + std::to_string(k);
std::string out_half_caption = "k = " + std::to_string(k / 2);
std::string out_dbl_caption = "k = " + std::to_string(k * 2);
std::stringstream out_full_caption, out_half_caption, out_dbl_caption;
out_full_caption << "k = " << k;
out_half_caption << "k = " << k / 2;
out_dbl_caption << "k = " << k * 2;
while (!wnd.close()) {
wnd(0, 0).image(img, "Input Image");
wnd(0, 1).image(out_full, out_full_caption.c_str());
wnd(1, 0).image(out_half, out_half_caption.c_str());
wnd(1, 1).image(out_dbl, out_dbl_caption.c_str());
wnd(0, 1).image(out_full, out_full_caption.str().c_str());
wnd(1, 0).image(out_half, out_half_caption.str().c_str());
wnd(1, 1).image(out_dbl, out_dbl_caption.str().c_str());
wnd.show();
}

} else {
means_full =
moddims(means_full, means_full.dims(1), means_full.dims(2));
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2 changes: 1 addition & 1 deletion examples/machine_learning/neural_network.cpp
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Expand Up @@ -22,7 +22,7 @@ std::string toStr(const dtype dt) {
switch(dt) {
case f32: return "f32";
case f16: return "f16";
default: return std::to_string(dt);
default: return "N/A";
}
}

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