Fix style issues after regexp

Signed-off-by: Omar Shrit <omar@avontech.fr>

src/mlpack/methods/ann/augmented/tasks/score_impl.hpp

@@ -37,8 +37,7 @@ double SequencePrecision(arma::field<MatType> trueOutputs,
 
   for (size_t i = 0; i < testSize; ++i)
   {
-    arma::vec delta = vectorise(arma::abs(
-      trueOutputs.at(i) - predOutputs.at(i)));
+    arma::vec delta = vectorise(abs(trueOutputs.at(i) - predOutputs.at(i)));
     double maxDelta = arma::max(delta);
     if (maxDelta < tol)
     {

src/mlpack/methods/ann/dists/normal_distribution_impl.hpp

@@ -45,8 +45,7 @@ DataType NormalDistribution<DataType>::LogProbability(
     const DataType& observation) const
 {
   const DataType v1 = arma::log(sigma) + std::log(std::sqrt(2 * M_PI));
-  const DataType v2 = square(observation - mean) /
-                      (2 * square(sigma));
+  const DataType v2 = square(observation - mean) / (2 * square(sigma));
   return  (-v1 - v2);
 }
 
@@ -57,8 +56,7 @@ void NormalDistribution<DataType>::ProbBackward(
     DataType& dsigma) const
 {
   dmu = (observation - mean) / (square(sigma)) % Probability(observation);
-  dsigma = (- 1.0 / sigma +
-            (square(observation - mean) / arma::pow(sigma, 3)))
+  dsigma = (- 1.0 / sigma + (square(observation - mean) / arma::pow(sigma, 3)))
             % Probability(observation);
 }
 

src/mlpack/methods/ann/layer/linear3d_impl.hpp

@@ -172,8 +172,7 @@ void Linear3DType<MatType, RegularizerType>::Gradient(
     dW.slice(i) = errorTemp.slice(i) * inputTemp.slice(i).t();
   }
 
-  gradient.submat(0, 0, weight.n_elem - 1, 0)
-      = vectorise(arma::sum(dW, 2));
+  gradient.submat(0, 0, weight.n_elem - 1, 0) = vectorise(arma::sum(dW, 2));
 
   gradient.submat(weight.n_elem, 0, weights.n_elem - 1, 0)
       = vectorise(arma::sum(arma::sum(errorTemp, 2), 1));

src/mlpack/methods/ann/layer/linear_impl.hpp

@@ -128,8 +128,7 @@ void LinearType<MatType, RegularizerType>::Gradient(
     const MatType& error,
     MatType& gradient)
 {
-  gradient.submat(0, 0, weight.n_elem - 1, 0) = vectorise(
-      error * input.t());
+  gradient.submat(0, 0, weight.n_elem - 1, 0) = vectorise(error * input.t());
   gradient.submat(weight.n_elem, 0, gradient.n_elem - 1, 0) =
       arma::sum(error, 1);
 

src/mlpack/methods/ann/layer/linear_no_bias_impl.hpp

@@ -123,8 +123,7 @@ void LinearNoBiasType<MatType, RegularizerType>::Gradient(
     const MatType& error,
     MatType& gradient)
 {
-  gradient.submat(0, 0, weight.n_elem - 1, 0) = vectorise(
-      error * input.t());
+  gradient.submat(0, 0, weight.n_elem - 1, 0) = vectorise(error * input.t());
   regularizer.Evaluate(weight, gradient);
 }
 

src/mlpack/methods/ann/layer/lstm_impl.hpp

@@ -344,26 +344,22 @@ void LSTMType<MatType>::Gradient(
 
   // output2GateOutputWeight gradients.
   gradient.submat(offset, 0, offset + output2GateOutputWeight.n_elem - 1, 0) =
-      vectorise(outputGateError *
-      outParameter.slice(this->CurrentStep()).t());
+      vectorise(outputGateError * outParameter.slice(this->CurrentStep()).t());
   offset += output2GateOutputWeight.n_elem;
 
   // output2GateForgetWeight gradients.
   gradient.submat(offset, 0, offset + output2GateForgetWeight.n_elem - 1, 0) =
-      vectorise(forgetGateError *
-      outParameter.slice(this->CurrentStep()).t());
+      vectorise(forgetGateError * outParameter.slice(this->CurrentStep()).t());
   offset += output2GateForgetWeight.n_elem;
 
   // output2GateInputWeight gradients.
   gradient.submat(offset, 0, offset + output2GateInputWeight.n_elem - 1, 0) =
-      vectorise(inputGateError *
-      outParameter.slice(this->CurrentStep()).t());
+      vectorise(inputGateError * outParameter.slice(this->CurrentStep()).t());
   offset += output2GateInputWeight.n_elem;
 
   // output2HiddenWeight gradients.
   gradient.submat(offset, 0, offset + output2HiddenWeight.n_elem - 1, 0) =
-      vectorise(hiddenError *
-      outParameter.slice(this->CurrentStep()).t());
+      vectorise(hiddenError * outParameter.slice(this->CurrentStep()).t());
   offset += output2HiddenWeight.n_elem;
 
   // cell2GateOutputWeight gradients.

src/mlpack/methods/ann/loss_functions/mean_squared_error_impl.hpp

@@ -29,8 +29,7 @@ typename MatType::elem_type MeanSquaredErrorType<MatType>::Forward(
     const MatType& prediction,
     const MatType& target)
 {
-  typename MatType::elem_type lossSum =
-      arma::accu(square(prediction - target));
+  typename MatType::elem_type lossSum = arma::accu(square(prediction - target));
 
   if (reduction)
     return lossSum;

src/mlpack/methods/ann/loss_functions/mean_squared_logarithmic_error_impl.hpp

@@ -31,8 +31,7 @@ typename MatType::elem_type MeanSquaredLogarithmicErrorType<MatType>::Forward(
     const MatType& target)
 {
   typename MatType::elem_type lossSum =
-      arma::accu(square(arma::log(1.0 + target) -
-      arma::log(1.0 + prediction)));
+      arma::accu(square(arma::log(1.0 + target) - arma::log(1.0 + prediction)));
 
   if (reduction)
     return lossSum;

src/mlpack/methods/cf/neighbor_search_policies/pearson_search.hpp

@@ -57,8 +57,7 @@ class PearsonSearch
     // For each vector x, first subtract mean(x) from each element in x.
     // Then normalize the vector to unit length.
     arma::mat normalizedSet(arma::size(referenceSet));
-    normalizedSet = normalise(
-        referenceSet.each_row() - arma::mean(referenceSet));
+    normalizedSet = normalise(referenceSet.each_row() - arma::mean(referenceSet));
 
     neighborSearch.Train(std::move(normalizedSet));
   }