Change arma::randu to randu

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

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

@@ -48,7 +48,7 @@ BernoulliDistribution<DataType>::BernoulliDistribution(
 template<typename DataType>
 DataType BernoulliDistribution<DataType>::Sample() const
 {
-  DataType sample = arma::randu<DataType>
+  DataType sample = randu<DataType>
       (probability.n_rows, probability.n_cols);
 
   for (size_t i = 0; i < sample.n_elem; ++i)

src/mlpack/methods/ann/init_rules/orthogonal_init.hpp

@@ -44,7 +44,7 @@ class OrthogonalInitialization
     arma::Mat<eT> V;
     arma::Col<eT> s;
 
-    arma::svd_econ(W, s, V, arma::randu<arma::Mat<eT> >(rows, cols));
+    arma::svd_econ(W, s, V, randu<arma::Mat<eT> >(rows, cols));
     W *= gain;
   }
 
@@ -60,7 +60,7 @@ class OrthogonalInitialization
     arma::Mat<eT> V;
     arma::Col<eT> s;
 
-    arma::svd_econ(W, s, V, arma::randu<arma::Mat<eT> >(W.n_rows, W.n_cols));
+    arma::svd_econ(W, s, V, randu<arma::Mat<eT> >(W.n_rows, W.n_cols));
     W *= gain;
   }
 

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

@@ -103,7 +103,7 @@ void AlphaDropoutType<MatType>::Forward(const MatType& input, MatType& output)
     // Set values to alphaDash with probability ratio.  Then apply affine
     // transformation so as to keep mean and variance of outputs to their
     // original values.
-    mask = arma::randu<MatType>(input.n_rows, input.n_cols);
+    mask = randu<MatType>(input.n_rows, input.n_cols);
     mask.transform( [&](double val) { return (val > ratio); } );
     output = (input % mask + alphaDash * (1 - mask)) * a + b;
   }

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

@@ -114,7 +114,7 @@ void DropConnectType<MatType>::Forward(const MatType& input, MatType& output)
 
     // Scale with input / (1 - ratio) and set values to zero with
     // probability ratio.
-    mask = arma::randu<MatType>(denoise.n_rows, denoise.n_cols);
+    mask = randu<MatType>(denoise.n_rows, denoise.n_cols);
     mask.transform([&](double val) { return (val > ratio); });
 
     baseLayer->Parameters() = denoise % mask;

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

@@ -86,7 +86,7 @@ void DropoutType<MatType>::Forward(const MatType& input, MatType& output)
   {
     // Scale with input / (1 - ratio) and set values to zero with probability
     // 'ratio'.
-    mask = arma::randu<MatType>(input.n_rows, input.n_cols);
+    mask = randu<MatType>(input.n_rows, input.n_cols);
     mask.transform([&](double val) { return (val > ratio); });
     output = input % mask * scale;
   }

src/mlpack/methods/hmm/hmm_impl.hpp

@@ -29,8 +29,8 @@ HMM<Distribution>::HMM(const size_t states,
                        const Distribution emissions,
                        const double tolerance) :
     emission(states, /* default distribution */ emissions),
-    transitionProxy(arma::randu<arma::mat>(states, states)),
-    initialProxy(arma::randu<arma::vec>(states) / (double) states),
+    transitionProxy(randu<arma::mat>(states, states)),
+    initialProxy(randu<arma::vec>(states) / (double) states),
     dimensionality(emissions.Dimensionality()),
     tolerance(tolerance),
     recalculateInitial(false),

src/mlpack/methods/hmm/hmm_train_main.cpp

@@ -247,7 +247,7 @@ struct Init
       const size_t dimensionality = e[i].Mean().n_rows;
       e[i].Mean().randu();
       // Generate random covariance.
-      arma::mat r = arma::randu<arma::mat>(dimensionality, dimensionality);
+      arma::mat r = randu<arma::mat>(dimensionality, dimensionality);
       e[i].Covariance(r * r.t());
     }
   }
@@ -269,7 +269,7 @@ struct Init
         e[i].Component(g).Mean().randu();
 
         // Generate random covariance.
-        arma::mat r = arma::randu<arma::mat>(dimensionality,
+        arma::mat r = randu<arma::mat>(dimensionality,
             dimensionality);
         e[i].Component(g).Covariance(r * r.t());
       }
@@ -293,7 +293,7 @@ struct Init
         e[i].Component(g).Mean().randu();
 
         // Generate random diagonal covariance.
-        arma::vec r = arma::randu<arma::vec>(dimensionality);
+        arma::vec r = randu<arma::vec>(dimensionality);
         e[i].Component(g).Covariance(r);
       }
     }

src/mlpack/methods/lmnn/lmnn_main.cpp

@@ -338,7 +338,7 @@ void BINDING_FUNCTION(util::Params& params, util::Timers& timers)
   }
   else if (rank)
   {
-    distance = arma::randu(rank, data.n_rows);
+    distance = randu(rank, data.n_rows);
   }
   // Normalize the data, if necessary.
   else if (normalize)

src/mlpack/methods/lsh/lsh_search_impl.hpp

@@ -215,7 +215,7 @@ void LSHSearch<SortPolicy, MatType>::Train(MatType referenceSet,
   // Step I: Prepare the second level hash.
 
   // Obtain the weights for the second hash.
-  secondHashWeights = arma::floor(arma::randu(numProj) *
+  secondHashWeights = arma::floor(randu(numProj) *
                                   (double) secondHashSize);
 
   // Instead of putting the points in the row corresponding to the bucket, we

src/mlpack/methods/matrix_completion/matrix_completion_impl.hpp

@@ -27,7 +27,7 @@ inline MatrixCompletion::MatrixCompletion(
     n(n),
     indices(indices),
     values(values),
-    sdp(indices.n_cols, 0, arma::randu<arma::mat>(m + n, r))
+    sdp(indices.n_cols, 0, randu<arma::mat>(m + n, r))
 {
   CheckValues();
   InitSDP();
@@ -59,7 +59,7 @@ inline MatrixCompletion::MatrixCompletion(
     indices(indices),
     values(values),
     sdp(indices.n_cols, 0,
-        arma::randu<arma::mat>(m + n, DefaultRank(m, n, indices.n_cols)))
+        randu<arma::mat>(m + n, DefaultRank(m, n, indices.n_cols)))
 {
   CheckValues();
   InitSDP();

src/mlpack/methods/reinforcement_learning/environment/acrobot.hpp

@@ -212,7 +212,7 @@ class Acrobot
   State InitialSample()
   {
     stepsPerformed = 0;
-    return State((arma::randu<arma::colvec>(4) - 0.5) / 5.0);
+    return State((randu<arma::colvec>(4) - 0.5) / 5.0);
   }
 
   /**

src/mlpack/methods/reinforcement_learning/environment/cart_pole.hpp

@@ -206,7 +206,7 @@ class CartPole
   State InitialSample()
   {
     stepsPerformed = 0;
-    return State((arma::randu<arma::colvec>(4) - 0.5) / 10.0);
+    return State((randu<arma::colvec>(4) - 0.5) / 10.0);
   }
 
   /**

src/mlpack/methods/reinforcement_learning/environment/cont_double_pole_cart.hpp

@@ -282,7 +282,7 @@ class ContinuousDoublePoleCart
   State InitialSample()
   {
     stepsPerformed = 0;
-    return State((arma::randu<arma::vec>(6) - 0.5) / 10.0);
+    return State((randu<arma::vec>(6) - 0.5) / 10.0);
   }
 
   /**

src/mlpack/methods/reinforcement_learning/environment/double_pole_cart.hpp

@@ -288,7 +288,7 @@ class DoublePoleCart
   State InitialSample()
   {
     stepsPerformed = 0;
-    return State((arma::randu<arma::vec>(6) - 0.5) / 10.0);
+    return State((randu<arma::vec>(6) - 0.5) / 10.0);
   }
 
   /**

src/mlpack/methods/reinforcement_learning/environment/mountain_car.hpp

@@ -188,7 +188,7 @@ class MountainCar
     State state;
     stepsPerformed = 0;
     state.Velocity() = 0.0;
-    state.Position() = arma::as_scalar(arma::randu(1)) * 0.2 - 0.6;
+    state.Position() = arma::as_scalar(randu(1)) * 0.2 - 0.6;
     return state;
   }
 

src/mlpack/methods/reinforcement_learning/replay/prioritized_replay.hpp

@@ -201,7 +201,7 @@ class PrioritizedReplay
     double sumPerRange = totalSum / batchSize;
     for (size_t bt = 0; bt < batchSize; bt++)
     {
-      const double mass = arma::randu() * sumPerRange + bt * sumPerRange;
+      const double mass = randu() * sumPerRange + bt * sumPerRange;
       idxes(bt) = idxSum.FindPrefixSum(mass);
     }
     return idxes;