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CI: Fix coverage job #41

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merged 2 commits into from
Apr 26, 2024
Merged

CI: Fix coverage job #41

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f21f3e6
CI: Fix coverage job
jschueller Apr 26, 2024
c2dc03e
Fix unused variables
jschueller Apr 26, 2024
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9 changes: 4 additions & 5 deletions .ci_support/run_docker_linux.sh
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Expand Up @@ -16,15 +16,14 @@ ctest --output-on-failure --timeout 100 ${MAKEFLAGS}

# coverage
gcov `find lib/src/ -name "*.gcno"`
lcov --capture --directory ./lib/src/ -o coverage.info
lcov --remove coverage.info "/usr/include/*" -o coverage.info
genhtml coverage.info --output-directory ~/.local/share/doc/*/html/coverage
cp -v coverage.info ~/.local/share/doc/*/html/coverage
lcov --capture --directory . --output-file coverage.info --include "*.cxx"
genhtml --output-directory coverage coverage.info
cp -v coverage.info coverage

uid=$1
gid=$2
if test -n "${uid}" -a -n "${gid}"
then
sudo cp -r ~/.local/share/doc/*/html /io
sudo cp -r ~/.local/share/doc/*/html coverage /io
sudo chown -R ${uid}:${gid} /io/html
fi
3 changes: 1 addition & 2 deletions .github/workflows/build.yml
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Expand Up @@ -30,8 +30,7 @@ jobs:
with:
token: ${{ secrets.CODECOV_TOKEN }}
slug: openturns/otsvm
file: ./html/coverage/coverage.info
verbose: true
file: ./coverage/coverage.info

mingw:
runs-on: ubuntu-latest
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8 changes: 0 additions & 8 deletions codecov.yml
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1 change: 0 additions & 1 deletion lib/src/SVMKernelRegressionEvaluation.cxx
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Expand Up @@ -95,7 +95,6 @@ Point SVMKernelRegressionEvaluation::operator() (const Point & inP) const
if (lagrangeMultiplier_[i] != 0.0)
output += lagrangeMultiplier_[i] * kernel_(dataIn_[i], inP);
}
const Point result(1, output);
return Point(1, output);
}

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6 changes: 0 additions & 6 deletions lib/src/SVMRegression.cxx
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Expand Up @@ -82,16 +82,10 @@ void SVMRegression::run()
Scalar bestTradeoffFactor = tradeoffFactor_[0];
Scalar bestKernelParameter = kernelParameter_[0];

Sample isoProbSample( size, inputDimension );
Function outputTransformation;
Function outputInverseTransformation;

Collection<Function> marginals;
Collection<Function> outputTransformationMarginals;

Point relativeError( outputDimension );
Point residu( outputDimension );
Point variance( outputDimension );

driver_.normalize(outputSample_, outputTransformation, outputInverseTransformation);
Sample normalizedOutputSample(outputTransformation(outputSample_));
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