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Added Utility Function
Added Utility Function Style Fix Fix Typo causing build error in windows Fix Typo causing build error in windows Fix const issue for windows Extract in data folder Reposition force local This should work Print Path in windows for debugging Print Path in windows for debugging Print Path in windows for debugging Stip components of tar Stip components of tar strip componenets Relative Path Relative Path Relative Path Add List Dir utility function Print Print again Print again Squash this Update utils.hpp Squash this Clean Up Added more tests Style Fix Style Fix
1 parent 78aa733 commit 6ae432e

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-70654
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.ci/linux-steps.yaml

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git clone --depth 1 https://github.com/mlpack/jenkins-conf.git conf
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git clone --depth 1 https://github.com/mlpack/mlpack.git
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mkdir data
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sudo add-apt-repository ppa:mhier/libboost-latest
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sudo apt-get update
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.ci/macos-steps.yaml

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# Configure CMake Args for models.
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- script: |
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mkdir build && cd build && cmake $(CMakeArgs-models) ..
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mkdir data && mkdir build && cd build && cmake $(CMakeArgs-models) ..
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displayName: 'CMake for models'
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# Build mlpack

.ci/windows-steps.yaml

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- powershell: |
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mkdir build
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mkdir data
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cp $(Agent.ToolsDirectory)\boost_libs\*.* build\
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cp $(Agent.ToolsDirectory)\OpenBLAS.0.2.14.1\lib\native\lib\x64\*.* build\
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cp $(Agent.ToolsDirectory)\OpenBLAS.0.2.14.1\lib\native\bin\x64\*.* build\

CMakeLists.txt

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utils/
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dataloader/
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tests/
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models/
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computer_vision/
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)
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foreach(dir ${DIRS})

computer_vision/CMakeLists.txt

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computer_vision/object_classification/CMakeLists.txt

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computer_vision/object_classification/object_classification.cpp

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data/mnist_test.csv

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data/mnist_train.csv

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dataloader/dataloader_impl.hpp

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datasetMap[dataset].endTrainingPredictionFeatures,
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datasetMap[dataset].endTrainingPredictionFeatures);
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LoadCSV(datasetMap[dataset].testPath, false, false, useScaler,
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LoadCSV(datasetMap[dataset].testPath, false, false, ratio, useScaler,
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datasetMap[dataset].dropHeader,
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datasetMap[dataset].startTestingInputFeatures,
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datasetMap[dataset].endTestingInputFeatures);
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scaler.Transform(dataset, dataset);
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}
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testFeatures = dataset.submat(WrapIndex(startInputFeatures, dataset.n_rows),
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0, WrapIndex(endInputFeatures, dataset.n_rows), dataset.n_cols - 1);
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testFeatures = dataset.rows(WrapIndex(startInputFeatures, dataset.n_rows),
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WrapIndex(endInputFeatures, dataset.n_rows));
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mlpack::Log::Info << "Testing Dataset Loaded." << std::endl;
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}
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}

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