Change arma::mean to mean in core/

shrit · shrit · commit e25e4e2a95dc · 2024-01-14T20:29:25.000+01:00
Signed-off-by: Omar Shrit &lt;omar@avontech.fr&gt;
diff --git a/src/mlpack/core/cv/k_fold_cv_impl.hpp b/src/mlpack/core/cv/k_fold_cv_impl.hpp
@@ -270,7 +270,7 @@ double KFoldCV<MLAlgorithm,
     return 0.0;
   }
 
-  return arma::mean(evaluations.elem(arma::find_finite(evaluations)));
+  return mean(evaluations.elem(arma::find_finite(evaluations)));
 }
 
 template<typename MLAlgorithm,
@@ -300,7 +300,7 @@ double KFoldCV<MLAlgorithm,
       modelPtr.reset(new MLAlgorithm(std::move(model)));
   }
 
-  return arma::mean(evaluations);
+  return mean(evaluations);
 }
 
 template<typename MLAlgorithm,
diff --git a/src/mlpack/core/cv/metrics/f1_impl.hpp b/src/mlpack/core/cv/metrics/f1_impl.hpp
@@ -88,7 +88,7 @@ double F1<AS, PC>::Evaluate(MLAlgorithm& model,
         2.0 * precision * recall / (precision + recall);
   }
 
-  return arma::mean(f1s);
+  return mean(f1s);
 }
 
 } // namespace mlpack
diff --git a/src/mlpack/core/cv/metrics/precision_impl.hpp b/src/mlpack/core/cv/metrics/precision_impl.hpp
@@ -77,7 +77,7 @@ double Precision<AS, PC>::Evaluate(MLAlgorithm& model,
     precisions(c) = double(tp) / numberOfPositivePredictions;
   }
 
-  return arma::mean(precisions);
+  return mean(precisions);
 }
 
 } // namespace mlpack
diff --git a/src/mlpack/core/cv/metrics/r2_score_impl.hpp b/src/mlpack/core/cv/metrics/r2_score_impl.hpp
@@ -27,7 +27,7 @@ double R2Score<AdjustedR2>::Evaluate(MLAlgorithm& model,
   // Taking Predicted Output from the model.
   model.Predict(data, predictedResponses);
   // Mean value of response.
-  double meanResponses = arma::mean(responses);
+  double meanResponses = mean(responses);
 
   // Calculate the numerator i.e. residual sum of squares.
   double residualSumSquared = arma::accu(arma::square(responses -
diff --git a/src/mlpack/core/cv/metrics/recall_impl.hpp b/src/mlpack/core/cv/metrics/recall_impl.hpp
@@ -77,7 +77,7 @@ double Recall<AS, PC>::Evaluate(MLAlgorithm& model,
     recalls(c) = double(tp) / positiveLabels;
   }
 
-  return arma::mean(recalls);
+  return mean(recalls);
 }
 
 } // namespace mlpack
diff --git a/src/mlpack/core/cv/metrics/silhouette_score_impl.hpp b/src/mlpack/core/cv/metrics/silhouette_score_impl.hpp
@@ -22,7 +22,7 @@ double SilhouetteScore::Overall(const DataType& X,
                                 const Metric& metric)
 {
   util::CheckSameSizes(X, labels, "SilhouetteScore::Overall()");
-  return arma::mean(SamplesScore(X, labels, metric));
+  return mean(SamplesScore(X, labels, metric));
 }
 
 template<typename DataType>
diff --git a/src/mlpack/core/data/scaler_methods/mean_normalization.hpp b/src/mlpack/core/data/scaler_methods/mean_normalization.hpp
@@ -54,7 +54,7 @@ class MeanNormalization
   template<typename MatType>
   void Fit(const MatType& input)
   {
-    itemMean = arma::mean(input, 1);
+    itemMean = mean(input, 1);
     itemMin = arma::min(input, 1);
     itemMax = arma::max(input, 1);
     scale = itemMax - itemMin;
diff --git a/src/mlpack/core/data/scaler_methods/pca_whitening.hpp b/src/mlpack/core/data/scaler_methods/pca_whitening.hpp
@@ -70,7 +70,7 @@ class PCAWhitening
   template<typename MatType>
   void Fit(const MatType& input)
   {
-    itemMean = arma::mean(input, 1);
+    itemMean = mean(input, 1);
     // Get eigenvectors and eigenvalues of covariance of input matrix.
     eig_sym(eigenValues, eigenVectors, ColumnCovariance(
         input.each_col() - itemMean));
diff --git a/src/mlpack/core/data/scaler_methods/standard_scaler.hpp b/src/mlpack/core/data/scaler_methods/standard_scaler.hpp
@@ -55,7 +55,7 @@ class StandardScaler
   template<typename MatType>
   void Fit(const MatType& input)
   {
-    itemMean = arma::mean(input, 1);
+    itemMean = mean(input, 1);
     itemStdDev = arma::stddev(input, 1, 1);
     // Handle zeros in scale vector.
     itemStdDev.for_each([](arma::vec::elem_type& val) { val =
diff --git a/src/mlpack/core/dists/gamma_distribution_impl.hpp b/src/mlpack/core/dists/gamma_distribution_impl.hpp
@@ -56,8 +56,8 @@ inline void GammaDistribution::Train(const arma::mat& rdata, const double tol)
     return;
 
   // Calculate log(mean(x)) and mean(log(x)) of each dataset row.
-  const arma::vec meanLogxVec = arma::mean(arma::log(rdata), 1);
-  const arma::vec meanxVec = arma::mean(rdata, 1);
+  const arma::vec meanLogxVec = mean(arma::log(rdata), 1);
+  const arma::vec meanxVec = mean(rdata, 1);
   const arma::vec logMeanxVec = arma::log(meanxVec);
 
   // Call the statistics-only GammaDistribution::Train() function to fit the
diff --git a/src/mlpack/core/dists/laplace_distribution_impl.hpp b/src/mlpack/core/dists/laplace_distribution_impl.hpp
@@ -67,7 +67,7 @@ inline void LaplaceDistribution::Estimate(const arma::mat& observations)
   //   \theta_k = (1 / n) \sum_{i = 1}^{n} x_ik
   // so L'(\theta) = 0 when \theta is the mean of the observations.  I am not
   // 100% certain my calculus and linear algebra is right, but I think it is...
-  mean = arma::mean(observations, 1);
+  mean = mean(observations, 1);
 
   // The maximum likelihood estimate of the scale parameter is the mean
   // deviation from the mean.
diff --git a/src/mlpack/core/math/ccov_impl.hpp b/src/mlpack/core/math/ccov_impl.hpp
@@ -40,7 +40,7 @@ inline arma::Mat<eT> ColumnCovariance(const arma::Mat<eT>& x,
     const size_t n = xAlias.n_cols;
     const eT normVal = (normType == 0) ? ((n > 1) ? eT(n - 1) : eT(1)) : eT(n);
 
-    const arma::Mat<eT> tmp = xAlias.each_col() - arma::mean(xAlias, 1);
+    const arma::Mat<eT> tmp = xAlias.each_col() - mean(xAlias, 1);
 
     out = tmp * tmp.t();
     out /= normVal;
diff --git a/src/mlpack/core/tree/binary_space_tree/rp_tree_mean_split_impl.hpp b/src/mlpack/core/tree/binary_space_tree/rp_tree_mean_split_impl.hpp
@@ -113,7 +113,7 @@ bool RPTreeMeanSplit<BoundType, MatType>::GetMeanMedian(
 {
   arma::Col<ElemType> values(samples.n_elem);
 
-  mean = arma::mean(data.cols(samples), 1);
+  mean = mean(data.cols(samples), 1);
 
   arma::Col<ElemType> tmp(data.n_rows);
 
diff --git a/src/mlpack/core/tree/cosine_tree/cosine_tree_impl.hpp b/src/mlpack/core/tree/cosine_tree/cosine_tree_impl.hpp
@@ -469,7 +469,7 @@ inline double CosineTree::MonteCarloError(CosineTree* node,
   }
 
   // Compute mean and standard deviation of the weighted samples.
-  double mu = arma::mean(weightedMagnitudes);
+  double mu = mean(weightedMagnitudes);
   double sigma = arma::stddev(weightedMagnitudes);
 
   if (!sigma)

Original file line number	Diff line number	Diff line change
`@@ -270,7 +270,7 @@ double KFoldCV<MLAlgorithm,`
`270`	`270`	`return 0.0;`
`271`	`271`	`}`
`272`	`272`
`273`		`- return arma::mean(evaluations.elem(arma::find_finite(evaluations)));`
	`273`	`+ return mean(evaluations.elem(arma::find_finite(evaluations)));`
`274`	`274`	`}`
`275`	`275`
`276`	`276`	`template<typename MLAlgorithm,`
`@@ -300,7 +300,7 @@ double KFoldCV<MLAlgorithm,`
`300`	`300`	`modelPtr.reset(new MLAlgorithm(std::move(model)));`
`301`	`301`	`}`
`302`	`302`
`303`		`- return arma::mean(evaluations);`
	`303`	`+ return mean(evaluations);`
`304`	`304`	`}`
`305`	`305`
`306`	`306`	`template<typename MLAlgorithm,`
Original file line number	Diff line number	Diff line change
`@@ -88,7 +88,7 @@ double F1<AS, PC>::Evaluate(MLAlgorithm& model,`
`88`	`88`	`2.0 * precision * recall / (precision + recall);`
`89`	`89`	`}`
`90`	`90`
`91`		`- return arma::mean(f1s);`
	`91`	`+ return mean(f1s);`
`92`	`92`	`}`
`93`	`93`
`94`	`94`	`} // namespace mlpack`
Original file line number	Diff line number	Diff line change
`@@ -77,7 +77,7 @@ double Precision<AS, PC>::Evaluate(MLAlgorithm& model,`
`77`	`77`	`precisions(c) = double(tp) / numberOfPositivePredictions;`
`78`	`78`	`}`
`79`	`79`
`80`		`- return arma::mean(precisions);`
	`80`	`+ return mean(precisions);`
`81`	`81`	`}`
`82`	`82`
`83`	`83`	`} // namespace mlpack`
Original file line number	Diff line number	Diff line change
`@@ -77,7 +77,7 @@ double Recall<AS, PC>::Evaluate(MLAlgorithm& model,`
`77`	`77`	`recalls(c) = double(tp) / positiveLabels;`
`78`	`78`	`}`
`79`	`79`
`80`		`- return arma::mean(recalls);`
	`80`	`+ return mean(recalls);`
`81`	`81`	`}`
`82`	`82`
`83`	`83`	`} // namespace mlpack`
Original file line number	Diff line number	Diff line change
`@@ -22,7 +22,7 @@ double SilhouetteScore::Overall(const DataType& X,`
`22`	`22`	`const Metric& metric)`
`23`	`23`	`{`
`24`	`24`	`util::CheckSameSizes(X, labels, "SilhouetteScore::Overall()");`
`25`		`- return arma::mean(SamplesScore(X, labels, metric));`
	`25`	`+ return mean(SamplesScore(X, labels, metric));`
`26`	`26`	`}`
`27`	`27`
`28`	`28`	`template<typename DataType>`
Original file line number	Diff line number	Diff line change
`@@ -54,7 +54,7 @@ class MeanNormalization`
`54`	`54`	`template<typename MatType>`
`55`	`55`	`void Fit(const MatType& input)`
`56`	`56`	`{`
`57`		`- itemMean = arma::mean(input, 1);`
	`57`	`+ itemMean = mean(input, 1);`
`58`	`58`	`itemMin = arma::min(input, 1);`
`59`	`59`	`itemMax = arma::max(input, 1);`
`60`	`60`	`scale = itemMax - itemMin;`
Original file line number	Diff line number	Diff line change
`@@ -70,7 +70,7 @@ class PCAWhitening`
`70`	`70`	`template<typename MatType>`
`71`	`71`	`void Fit(const MatType& input)`
`72`	`72`	`{`
`73`		`- itemMean = arma::mean(input, 1);`
	`73`	`+ itemMean = mean(input, 1);`
`74`	`74`	`// Get eigenvectors and eigenvalues of covariance of input matrix.`
`75`	`75`	`eig_sym(eigenValues, eigenVectors, ColumnCovariance(`
`76`	`76`	`input.each_col() - itemMean));`
Original file line number	Diff line number	Diff line change
`@@ -55,7 +55,7 @@ class StandardScaler`
`55`	`55`	`template<typename MatType>`
`56`	`56`	`void Fit(const MatType& input)`
`57`	`57`	`{`
`58`		`- itemMean = arma::mean(input, 1);`
	`58`	`+ itemMean = mean(input, 1);`
`59`	`59`	`itemStdDev = arma::stddev(input, 1, 1);`
`60`	`60`	`// Handle zeros in scale vector.`
`61`	`61`	`itemStdDev.for_each([](arma::vec::elem_type& val) { val =`