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sample end-to-end ML C++ app tutorial

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gmanlan
2018-06-18 16:29:49 -07:00
parent 9186526453
commit f29fefbfed
1 changed files with 18 additions and 11 deletions
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doc/guide/sample_ml_app.hpp
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@@ -12,7 +12,7 @@ adapt it to a different platform by following the provided source code.
@section sample_create_project Creating the VS project
- Open Visual Studio and create a new project (Windows Console Application)
- For this sample, the project is named “mymlpackapp” and is located at "C:\myprojects\"
- For this sample, the project is named “sample-ml-app”
@section sample_project_config Project Configuration
@@ -22,16 +22,22 @@ mlpack and dependencies in Release Mode).
- Right click on the project and select Properties, select the x64 Debug profile
- Under C/C++ > General > Additional Include Directories add:
- "C:\boost\boost_1_66_0"
- "C:\mlpack\armadillo-8.500.1\include"
- "C:\mlpack\mlpack-3.0.2\build\include"
@code
- C:\boost\boost_1_66_0
- C:\mlpack\armadillo-8.500.1\include
- C:\mlpack\mlpack-3.0.2\build\include
@endcode
- Under Linker > Input > Additional Dependencies add:
- "C:\mlpack\mlpack-3.0.2\build\Debug\mlpack.lib"
- "C:\boost\boost_1_66_0\lib64-msvc-14.1\libboost_serialization-vc141-mt-gd-x64-1_66.lib"
- "C:\boost\boost_1_66_0\lib64-msvc-14.1\libboost_program_options-vc141-mt-gd-x64-1_66.lib"
@code
- C:\mlpack\mlpack-3.0.2\build\Debug\mlpack.lib
- C:\boost\boost_1_66_0\lib64-msvc-14.1\libboost_serialization-vc141-mt-gd-x64-1_66.lib
- C:\boost\boost_1_66_0\lib64-msvc-14.1\libboost_program_options-vc141-mt-gd-x64-1_66.lib
@endcode
- Under Build Events > Post-Build Event > Command Line add:
@code
- xcopy /y "C:\mlpack\mlpack-3.0.2\build\Debug\mlpack.dll" $(OutDir)
- xcopy /y "C:\mlpack\mlpack-3.0.2\packages\OpenBLAS.0.2.14.1\lib\native\bin\x64\*.dll" $(OutDir)
@endcode
@note Recent versions of Visual Studio set "Conformance Mode" enabled by default. This causes some issues with
the armadillo library. If you encounter this issue, disable "Conformance Mode" under C/C++ > Language.
@@ -75,8 +81,8 @@ using namespace mlpack::cv;
@section sample_load_dataset Loading the dataset
First step is about loading the dataset. Here we load a CSV dataset, assuming the labels
don't require normalization.
First step is about loading the dataset. Here we load a CSV dataset
(i.e.: mlpack/tests/data/german.csv), assuming the labels don't require normalization.
@code
bool loaded = mlpack::data::Load("data/german.csv", dataset);
@@ -181,7 +187,8 @@ printf("\nClassification result: %i (Probabilities: %f/%f)", result,
@section sample_app_conclussion Final thoughts
Building real-life applications and services using machine learning can be challenging. Hopefully, this
tutorial provides a good starting point that covers the entire workflow you may need to follow while
developing it. You can take a look at the entire source code in the provided sample project.
tutorial provides a good starting point that covers the basic workflow you may need to follow while
developing it. You can take a look at the entire source code in the provided sample project located here:
"doc\examples\sample-ml-app".
*/