diff --git a/CMake/ARMA_FindARPACK.cmake b/CMake/ARMA_FindARPACK.cmake
index 35edae7ccd5..ff1ee227977 100644
--- a/CMake/ARMA_FindARPACK.cmake
+++ b/CMake/ARMA_FindARPACK.cmake
@@ -1,39 +1,39 @@
-# - Try to find ARPACK
-# Once done this will define
-#
-#  ARPACK_FOUND        - system has ARPACK
-#  ARPACK_LIBRARY      - Link this to use ARPACK
-
-
-find_library(ARPACK_LIBRARY
-  NAMES arpack
-  PATHS /usr/lib64 /usr/lib /usr/local/lib64 /usr/local/lib
-  )
-
-
-if (ARPACK_LIBRARY)
-  set(ARPACK_FOUND YES)
-else ()
-  # Search for PARPACK.
-  find_library(ARPACK_LIBRARY
-    NAMES parpack
-    PATHS /usr/lib64 /usr/lib /usr/local/lib64 /usr/local/lib
-  )
-
-  if (ARPACK_LIBRARY)
-    set(ARPACK_FOUND YES)
-  else ()
-    set(ARPACK_FOUND NO)
-  endif ()
-endif ()
-
-
-if (ARPACK_FOUND)
-  if (NOT ARPACK_FIND_QUIETLY)
-     message(STATUS "Found an ARPACK library: ${ARPACK_LIBRARY}")
-  endif ()
-else ()
-  if (ARPACK_FIND_REQUIRED)
-     message(FATAL_ERROR "Could not find an ARPACK library")
-  endif ()
-endif ()
+# - Try to find ARPACK
+# Once done this will define
+#
+#  ARPACK_FOUND        - system has ARPACK
+#  ARPACK_LIBRARY      - Link this to use ARPACK
+
+
+find_library(ARPACK_LIBRARY
+  NAMES arpack
+  PATHS /usr/lib64 /usr/lib /usr/local/lib64 /usr/local/lib
+  )
+
+
+if (ARPACK_LIBRARY)
+  set(ARPACK_FOUND YES)
+else ()
+  # Search for PARPACK.
+  find_library(ARPACK_LIBRARY
+    NAMES parpack
+    PATHS /usr/lib64 /usr/lib /usr/local/lib64 /usr/local/lib
+  )
+
+  if (ARPACK_LIBRARY)
+    set(ARPACK_FOUND YES)
+  else ()
+    set(ARPACK_FOUND NO)
+  endif ()
+endif ()
+
+
+if (ARPACK_FOUND)
+  if (NOT ARPACK_FIND_QUIETLY)
+     message(STATUS "Found an ARPACK library: ${ARPACK_LIBRARY}")
+  endif ()
+else ()
+  if (ARPACK_FIND_REQUIRED)
+     message(FATAL_ERROR "Could not find an ARPACK library")
+  endif ()
+endif ()
diff --git a/doc/tutorials/approx_kfn/approx_kfn.txt b/doc/tutorials/approx_kfn/approx_kfn.txt
index aa477a0d097..247eb72ff98 100644
--- a/doc/tutorials/approx_kfn/approx_kfn.txt
+++ b/doc/tutorials/approx_kfn/approx_kfn.txt
@@ -144,7 +144,7 @@ neighbor search:
  - \c mlpack_kfn, for exact and approximate tree-based approaches
 
 These two programs allow a large number of algorithms to be used to find
-approximate furthest neighbors.  Note that the \c mlpack_kfn program is also 
+approximate furthest neighbors.  Note that the \c mlpack_kfn program is also
 documented by the \ref cli_nstut section of the \ref nstutorial page, as it
 shares options with the \c mlpack_knn program.
 
@@ -170,7 +170,7 @@ $ mlpack_approx_kfn -q queries.csv -r refs.csv -v -k 5 -n n.csv -d d.csv
 [INFO ] Search complete.
 [INFO ] Saving CSV data to 'n.csv'.
 [INFO ] Saving CSV data to 'd.csv'.
-[INFO ] 
+[INFO ]
 [INFO ] Execution parameters:
 [INFO ]   algorithm: ds
 [INFO ]   calculate_error: false
@@ -188,7 +188,7 @@ $ mlpack_approx_kfn -q queries.csv -r refs.csv -v -k 5 -n n.csv -d d.csv
 [INFO ]   reference_file: refs.csv
 [INFO ]   verbose: true
 [INFO ]   version: false
-[INFO ] 
+[INFO ]
 [INFO ] Program timers:
 [INFO ]   drusilla_select_construct: 0.000342s
 [INFO ]   drusilla_select_search: 0.000780s
@@ -231,7 +231,7 @@ $ mlpack_approx_kfn -q queries.csv -r refs.csv -v -k 4 -n n.csv -d d.csv -t 10 -
 [INFO ] Search complete.
 [INFO ] Saving CSV data to 'n.csv'.
 [INFO ] Saving CSV data to 'd.csv'.
-[INFO ] 
+[INFO ]
 [INFO ] Execution parameters:
 [INFO ]   algorithm: ds
 [INFO ]   calculate_error: false
@@ -249,7 +249,7 @@ $ mlpack_approx_kfn -q queries.csv -r refs.csv -v -k 4 -n n.csv -d d.csv -t 10 -
 [INFO ]   reference_file: refs.csv
 [INFO ]   verbose: true
 [INFO ]   version: false
-[INFO ] 
+[INFO ]
 [INFO ] Program timers:
 [INFO ]   drusilla_select_construct: 0.000645s
 [INFO ]   drusilla_select_search: 0.000551s
@@ -276,7 +276,7 @@ $ mlpack_approx_kfn -q queries.csv -r refs.csv -v -k 3 -n n.csv -d d.csv -a qdaf
 [INFO ] Search complete.
 [INFO ] Saving CSV data to 'n.csv'.
 [INFO ] Saving CSV data to 'd.csv'.
-[INFO ] 
+[INFO ]
 [INFO ] Execution parameters:
 [INFO ]   algorithm: qdafn
 [INFO ]   calculate_error: false
@@ -294,7 +294,7 @@ $ mlpack_approx_kfn -q queries.csv -r refs.csv -v -k 3 -n n.csv -d d.csv -a qdaf
 [INFO ]   reference_file: refs.csv
 [INFO ]   verbose: true
 [INFO ]   version: false
-[INFO ] 
+[INFO ]
 [INFO ] Program timers:
 [INFO ]   loading_data: 0.008380s
 [INFO ]   qdafn_construct: 0.003399s
@@ -326,7 +326,7 @@ $ mlpack_approx_kfn -q queries.csv -r refs.csv -v -k 1 -e -q -n n.csv
 [INFO ] Average error: 1.08417.
 [INFO ] Maximum error: 1.28712.
 [INFO ] Minimum error: 1.
-[INFO ] 
+[INFO ]
 [INFO ] Execution parameters:
 [INFO ]   algorithm: ds
 [INFO ]   calculate_error: true
@@ -344,7 +344,7 @@ $ mlpack_approx_kfn -q queries.csv -r refs.csv -v -k 1 -e -q -n n.csv
 [INFO ]   reference_file: refs.csv
 [INFO ]   verbose: true
 [INFO ]   version: false
-[INFO ] 
+[INFO ]
 [INFO ] Program timers:
 [INFO ]   computing_neighbors: 0.001476s
 [INFO ]   drusilla_select_construct: 0.000309s
@@ -391,7 +391,7 @@ $ mlpack_approx_kfn -q queries.csv -r refs.csv -k 1 -e -x exact.csv -n n.csv -v
 [INFO ] Maximum error: 1.67407.
 [INFO ] Minimum error: 1.
 [INFO ] Saving CSV data to 'n.csv'.
-[INFO ] 
+[INFO ]
 [INFO ] Execution parameters:
 [INFO ]   algorithm: qdafn
 [INFO ]   calculate_error: true
@@ -409,7 +409,7 @@ $ mlpack_approx_kfn -q queries.csv -r refs.csv -k 1 -e -x exact.csv -n n.csv -v
 [INFO ]   reference_file: refs.csv
 [INFO ]   verbose: true
 [INFO ]   version: false
-[INFO ] 
+[INFO ]
 [INFO ] Program timers:
 [INFO ]   loading_data: 0.010348s
 [INFO ]   qdafn_construct: 0.000318s
@@ -443,7 +443,7 @@ $ mlpack_kfn -q queries.csv -r refs.csv -v -k 3 -e 0.5 -n n.csv -d d.csv
 [INFO ] Search complete.
 [INFO ] Saving CSV data to 'n.csv'.
 [INFO ] Saving CSV data to 'd.csv'.
-[INFO ] 
+[INFO ]
 [INFO ] Execution parameters:
 [INFO ]   algorithm: dual_tree
 [INFO ]   distances_file: d.csv
@@ -467,7 +467,7 @@ $ mlpack_kfn -q queries.csv -r refs.csv -v -k 3 -e 0.5 -n n.csv -d d.csv
 [INFO ]   true_neighbors_file: ""
 [INFO ]   verbose: true
 [INFO ]   version: false
-[INFO ] 
+[INFO ]
 [INFO ] Program timers:
 [INFO ]   computing_neighbors: 0.000442s
 [INFO ]   loading_data: 0.008060s
@@ -503,7 +503,7 @@ mlpack_kfn -q queries.csv -r refs.csv -v -k 3 -e 0.1 -n n.csv -d d.csv -a single
 [INFO ] Search complete.
 [INFO ] Saving CSV data to 'n.csv'.
 [INFO ] Saving CSV data to 'd.csv'.
-[INFO ] 
+[INFO ]
 [INFO ] Execution parameters:
 [INFO ]   algorithm: single_tree
 [INFO ]   distances_file: d.csv
@@ -527,7 +527,7 @@ mlpack_kfn -q queries.csv -r refs.csv -v -k 3 -e 0.1 -n n.csv -d d.csv -a single
 [INFO ]   true_neighbors_file: ""
 [INFO ]   verbose: true
 [INFO ]   version: false
-[INFO ] 
+[INFO ]
 [INFO ] Program timers:
 [INFO ]   computing_neighbors: 0.000850s
 [INFO ]   loading_data: 0.007858s
@@ -554,7 +554,7 @@ $ mlpack_approx_kfn -r refs.csv -t 5 -p 10 -v -M model.bin
 [INFO ] Loading 'refs.csv' as CSV data.  Size is 3 x 1000.
 [INFO ] Building DrusillaSelect model...
 [INFO ] Model built.
-[INFO ] 
+[INFO ]
 [INFO ] Execution parameters:
 [INFO ]   algorithm: ds
 [INFO ]   calculate_error: false
@@ -572,7 +572,7 @@ $ mlpack_approx_kfn -r refs.csv -t 5 -p 10 -v -M model.bin
 [INFO ]   reference_file: refs.csv
 [INFO ]   verbose: true
 [INFO ]   version: false
-[INFO ] 
+[INFO ]
 [INFO ] Program timers:
 [INFO ]   drusilla_select_construct: 0.000321s
 [INFO ]   loading_data: 0.004700s
@@ -589,7 +589,7 @@ $ mlpack_approx_kfn -m model.bin -q queries.csv -k 3 -d d.csv -n n.csv -v
 [INFO ] Search complete.
 [INFO ] Saving CSV data to 'n.csv'.
 [INFO ] Saving CSV data to 'd.csv'.
-[INFO ] 
+[INFO ]
 [INFO ] Execution parameters:
 [INFO ]   algorithm: ds
 [INFO ]   calculate_error: false
@@ -607,7 +607,7 @@ $ mlpack_approx_kfn -m model.bin -q queries.csv -k 3 -d d.csv -n n.csv -v
 [INFO ]   reference_file: ""
 [INFO ]   verbose: true
 [INFO ]   version: false
-[INFO ] 
+[INFO ]
 [INFO ] Program timers:
 [INFO ]   drusilla_select_search: 0.000878s
 [INFO ]   loading_data: 0.004599s
diff --git a/src/mlpack/core/arma_extend/fn_ind2sub.hpp b/src/mlpack/core/arma_extend/fn_ind2sub.hpp
index cfa6ab5b44d..a3bff68195e 100644
--- a/src/mlpack/core/arma_extend/fn_ind2sub.hpp
+++ b/src/mlpack/core/arma_extend/fn_ind2sub.hpp
@@ -6,7 +6,7 @@
 // -------------------------------------------------------------------
 //
 // Written by Conrad Sanderson - http://conradsanderson.id.au
-  
+
   #if (ARMA_VERSION_MAJOR < 6 || \
     (ARMA_VERSION_MAJOR == 6 && ARMA_VERSION_MINOR < 399))
   inline
@@ -14,14 +14,14 @@
   ind2sub(const SizeMat& s, const uword i)
     {
     arma_extra_debug_sigprint();
-    
+
     arma_debug_check( (i >= (s.n_rows * s.n_cols) ), "ind2sub(): index out of range" );
-    
+
     uvec out(2);
-    
+
     out[0] = i % s.n_rows;
     out[1] = i / s.n_rows;
-    
+
     return out;
     }
 
@@ -31,22 +31,22 @@
   ind2sub(const SizeCube& s, const uword i)
     {
     arma_extra_debug_sigprint();
-    
+
     arma_debug_check( (i >= (s.n_rows * s.n_cols * s.n_slices) ), "ind2sub(): index out of range" );
-    
+
     const uword n_elem_slice = s.n_rows * s.n_cols;
-    
+
     const uword slice  = i / n_elem_slice;
     const uword j      = i - (slice * n_elem_slice);
     const uword row    = j % s.n_rows;
     const uword col    = j / s.n_rows;
-    
+
     uvec out(3);
-    
+
     out[0] = row;
     out[1] = col;
     out[2] = slice;
-    
+
     return out;
     }
 
@@ -56,9 +56,9 @@
   sub2ind(const SizeMat& s, const uword row, const uword col)
     {
     arma_extra_debug_sigprint();
-    
+
     arma_debug_check( ((row >= s.n_rows) || (col >= s.n_cols)), "sub2ind(): subscript out of range" );
-    
+
     return uword(row + col*s.n_rows);
     }
 
@@ -68,11 +68,11 @@
   sub2ind(const SizeCube& s, const uword row, const uword col, const uword slice)
     {
     arma_extra_debug_sigprint();
-    
+
     arma_debug_check( ((row >= s.n_rows) || (col >= s.n_cols) || (slice >= s.n_slices)), "sub2ind(): subscript out of range" );
-    
+
     return uword( (slice * s.n_rows * s.n_cols) + (col * s.n_rows) + row );
     }
 #endif
- 
+
 
diff --git a/src/mlpack/core/boost_backport/bernoulli.hpp b/src/mlpack/core/boost_backport/bernoulli.hpp
index 554297001e3..4e8ee6f9214 100644
--- a/src/mlpack/core/boost_backport/bernoulli.hpp
+++ b/src/mlpack/core/boost_backport/bernoulli.hpp
@@ -48,8 +48,8 @@ namespace boost { namespace math{
 #include "detail/unchecked_bernoulli.hpp"
 #include "detail/bernoulli_details.hpp"
 
-namespace boost { namespace math { 
-   
+namespace boost { namespace math {
+
 namespace detail {
 
 template <class T, class OutputIterator, class Policy, int N>
@@ -60,7 +60,7 @@ OutputIterator bernoulli_number_imp(OutputIterator out, std::size_t start, std::
       *out = unchecked_bernoulli_imp<T>(i, tag);
       ++out;
    }
-   
+
    for(std::size_t i = (std::max)(static_cast<std::size_t>(max_bernoulli_b2n<T>::value + 1), start); i < start + n; ++i)
    {
       // We must overflow:
diff --git a/src/mlpack/core/boost_backport/detail/bernoulli_details.hpp b/src/mlpack/core/boost_backport/detail/bernoulli_details.hpp
index f26dfe7d166..d07d796b1f8 100644
--- a/src/mlpack/core/boost_backport/detail/bernoulli_details.hpp
+++ b/src/mlpack/core/boost_backport/detail/bernoulli_details.hpp
@@ -82,12 +82,12 @@ T b2n_asymptotic(int n)
    const T nx = static_cast<T>(n);
    const T nx2(nx * nx);
 
-   const T approximate_log_of_bernoulli_bn = 
+   const T approximate_log_of_bernoulli_bn =
         ((boost::math::constants::half<T>() + nx) * log(nx))
         + ((boost::math::constants::half<T>() - nx) * log(boost::math::constants::pi<T>()))
         + (((T(3) / 2) - nx) * boost::math::constants::ln_two<T>())
         + ((nx * (T(2) - (nx2 * 7) * (1 + ((nx2 * 30) * ((nx2 * 12) - 1))))) / (((nx2 * nx2) * nx2) * 2520));
-   return ((n / 2) & 1 ? 1 : -1) * (approximate_log_of_bernoulli_bn > tools::log_max_value<T>() 
+   return ((n / 2) & 1 ? 1 : -1) * (approximate_log_of_bernoulli_bn > tools::log_max_value<T>()
       ? policies::raise_overflow_error<T>("boost::math::bernoulli_b2n<%1%>(std::size_t)", 0, nx, Policy())
       : static_cast<T>(exp(approximate_log_of_bernoulli_bn)));
 }
@@ -114,12 +114,12 @@ T t2n_asymptotic(int n)
 // us to elude a great deal of runtime checking for values below
 // n, and only perform the full overflow checks when we know that we're
 // getting close to the point where our calculations will overflow.
-// We use Luschny's LogB3 formula (http://www.luschny.de/math/primes/bernincl.html) 
+// We use Luschny's LogB3 formula (http://www.luschny.de/math/primes/bernincl.html)
 // to find the limit, and since we're dealing with the log of the Bernoulli numbers
 // we need only perform the calculation at double precision and not with T
 // (which may be a multiprecision type).  The limit returned is within 1 of the true
 // limit for all the types tested.  Note that although the code below is basically
-// the same as b2n_asymptotic above, it has been recast as a continuous real-valued 
+// the same as b2n_asymptotic above, it has been recast as a continuous real-valued
 // function as this makes the root finding go smoother/faster.  It also omits the
 // sign of the Bernoulli number.
 //
@@ -241,10 +241,10 @@ struct fixed_vector : private std::allocator<T>
    typedef T* iterator;
    typedef const T* const_iterator;
    fixed_vector() : m_used(0)
-   { 
+   {
       std::size_t overflow_limit = 5 + b2n_overflow_limit<T, policies::policy<> >();
       m_capacity = static_cast<unsigned>((std::min)(overflow_limit, static_cast<std::size_t>(100000u)));
-      m_data = this->allocate(m_capacity); 
+      m_data = this->allocate(m_capacity);
    }
    ~fixed_vector()
    {
@@ -402,7 +402,7 @@ class bernoulli_numbers_cache
       // There are basically 3 thread safety options:
       //
       // 1) There are no threads (BOOST_HAS_THREADS is not defined).
-      // 2) There are threads, but we do not have a true atomic integer type, 
+      // 2) There are threads, but we do not have a true atomic integer type,
       //    in this case we just use a mutex to guard against race conditions.
       // 3) There are threads, and we have an atomic integer: in this case we can
       //    use the double-checked locking pattern to avoid thread synchronisation
@@ -504,7 +504,7 @@ class bernoulli_numbers_cache
       // There are basically 3 thread safety options:
       //
       // 1) There are no threads (BOOST_HAS_THREADS is not defined).
-      // 2) There are threads, but we do not have a true atomic integer type, 
+      // 2) There are threads, but we do not have a true atomic integer type,
       //    in this case we just use a mutex to guard against race conditions.
       // 3) There are threads, and we have an atomic integer: in this case we can
       //    use the double-checked locking pattern to avoid thread synchronisation
diff --git a/src/mlpack/core/boost_backport/detail/polygamma.hpp b/src/mlpack/core/boost_backport/detail/polygamma.hpp
index 35b45fff14e..201f81929da 100644
--- a/src/mlpack/core/boost_backport/detail/polygamma.hpp
+++ b/src/mlpack/core/boost_backport/detail/polygamma.hpp
@@ -26,10 +26,10 @@
   #include <boost/mpl/int.hpp>
   #include <boost/static_assert.hpp>
   #include <boost/type_traits/is_convertible.hpp>
-  
+
   // Forward declarations.
   namespace boost { namespace math {
-    
+
     // Forward declaration of boost::math::polygamma.
     template<class T, class Policy>
     inline typename tools::promote_args<T>::type polygamma(const int n, T x, const Policy& pol);
@@ -39,12 +39,12 @@
     namespace policies {
 
       template <class T, class Policy>
-        inline int digits_base10(BOOST_MATH_EXPLICIT_TEMPLATE_TYPE(T)) 
+        inline int digits_base10(BOOST_MATH_EXPLICIT_TEMPLATE_TYPE(T))
         {
           return boost::math::policies::digits<T, Policy>() * 301 / 1000L;
         }
     }
-    
+
   }}
 
   namespace boost { namespace math { namespace detail{
@@ -77,12 +77,12 @@
      // (n-1)! / x^(n+1)
      //
      // which is common to both the first term of the series (with k = 1)
-     // and to the leading part.  
+     // and to the leading part.
      // We can then get to the leading term by:
      //
      // part_term * (n + 2 * x) / 2
      //
-     // and to the first term in the series 
+     // and to the first term in the series
      // (excluding the Bernoulli number) by:
      //
      // part_term n * (n + 1) / (2x)
@@ -91,7 +91,7 @@
      // or the power term underflows, this just gets set to 0 and then we
      // know that we have to use logs for the initial terms:
      //
-     part_term = ((n > (int)boost::math::max_factorial<T>::value) && (T(n) * n > tools::log_max_value<T>())) 
+     part_term = ((n > (int)boost::math::max_factorial<T>::value) && (T(n) * n > tools::log_max_value<T>()))
         ? T(0) : static_cast<T>(boost::math::factorial<T>(n - 1, pol) * pow(x, -n - 1));
      if(part_term == 0)
      {
@@ -138,7 +138,7 @@
            return policies::raise_evaluation_error(function, "Series did not converge, closest value was %1%", sum, pol);
         }
      }
-     
+
      if((n - 1) & 1)
         sum = -sum;
 
@@ -212,7 +212,7 @@
      T factorial_part = 1;
      //
      // "prefix" is what we'll be adding the accumulated sum to, it will
-     // be n! / z^(n+1), but since we're scaling by n! it's just 
+     // be n! / z^(n+1), but since we're scaling by n! it's just
      // 1 / z^(n+1) for now:
      //
      T prefix = pow(x, n + 1);
@@ -224,10 +224,10 @@
      // ignore the sum if it will have no effect on the result anyway:
      //
      if(prefix > 2 / policies::get_epsilon<T, Policy>())
-        return ((n & 1) ? 1 : -1) * 
+        return ((n & 1) ? 1 : -1) *
          (tools::max_value<T>() / prefix < scale ? policies::raise_overflow_error<T>(function, 0, pol) : prefix * scale);
      //
-     // As this is an alternating series we could accelerate it using 
+     // As this is an alternating series we could accelerate it using
      // "Convergence Acceleration of Alternating Series",
      // Henri Cohen, Fernando Rodriguez Villegas, and Don Zagier, Experimental Mathematics, 1999.
      // In practice however, it appears not to make any difference to the number of terms
@@ -410,7 +410,7 @@
      }
 
      //
-     // We'll have to compute the coefficients up to n, 
+     // We'll have to compute the coefficients up to n,
      // complexity is O(n^2) which we don't worry about for now
      // as the values are computed once and then cached.
      // However, if the final evaluation would have too many
@@ -493,7 +493,7 @@
 
   template <class T, class Policy>
   const typename polygamma_initializer<T, Policy>::init polygamma_initializer<T, Policy>::initializer;
-  
+
   template<class T, class Policy>
   inline T polygamma_imp(const int n, T x, const Policy &pol)
   {
diff --git a/src/mlpack/core/boost_backport/detail/unchecked_bernoulli.hpp b/src/mlpack/core/boost_backport/detail/unchecked_bernoulli.hpp
index 80801bd6bb5..c61c08999b0 100644
--- a/src/mlpack/core/boost_backport/detail/unchecked_bernoulli.hpp
+++ b/src/mlpack/core/boost_backport/detail/unchecked_bernoulli.hpp
@@ -19,8 +19,8 @@
 #include <boost/mpl/int.hpp>
 #include <boost/type_traits/is_convertible.hpp>
 
-namespace boost { namespace math { 
-   
+namespace boost { namespace math {
+
 namespace detail {
 
 template <unsigned N>
@@ -56,7 +56,7 @@ struct max_bernoulli_index<4>
 template <class T>
 struct bernoulli_imp_variant
 {
-   static const unsigned value = 
+   static const unsigned value =
       (std::numeric_limits<T>::max_exponent == 128)
       && (std::numeric_limits<T>::radix == 2)
       && (std::numeric_limits<T>::digits <= std::numeric_limits<float>::digits)
@@ -452,176 +452,176 @@ inline T unchecked_bernoulli_imp(std::size_t n, const mpl::int_<3>& )
       +1.33527841873546338750122832017820518292039E306L,
 #if LDBL_MAX_EXP == 16384
       // Entries 260 - 600 http://www.wolframalpha.com/input/?i=TABLE[N[Bernoulli[i]%2C40]%2C+{i%2C258%2C600%2C2}]
-      -2.277640649601959593875058983506938037019e309L, 
-      3.945184036046326234163525556422667595884e312L, 
-      -6.938525772130602106071724989641405550473e315L, 
-      1.238896367577564823729057820219210929986e319L, 
-      -2.245542599169309759499987966025604480745e322L, 
-      4.131213176073842359732511639489669404266e325L, 
-      -7.713581346815269584960928069762882771369e328L, 
-      1.461536066837669600638613788471335541313e332L, 
-      -2.809904606225532896862935642992712059631e335L, 
-      5.480957121318876639512096994413992284327e338L, 
-      -1.084573284087686110518125291186079616320e342L, 
-      2.176980775647663539729165173863716459962e345L, 
-      -4.431998786117553751947439433256752608068e348L, 
-      9.150625657715535047417756278073770096073e351L, 
-      -1.915867353003157351316577579148683133613e355L, 
-      4.067256303542212258698836003682016040629e358L, 
-      -8.754223791037736616228150209910348734629e361L, 
-      1.910173688735533667244373747124109379826e365L, 
-      -4.225001320265091714631115064713174404607e368L, 
-      9.471959352547827678466770796787503034505e371L, 
-      -2.152149973279986829719817376756088198573e375L, 
-      4.955485775334221051344839716507812871361e378L, 
-      -1.156225941759134696630956889716381968142e382L, 
-      2.733406597646137698610991926705098514017e385L, 
-      -6.546868135325176947099912523279938546333e388L, 
-      1.588524912441221472814692121069821695547e392L, 
-      -3.904354800861715180218598151050191841308e395L, 
-      9.719938686092045781827273411668132975319e398L, 
-      -2.450763621049522051234479737511375679283e402L, 
-      6.257892098396815305085674126334317095277e405L, 
-      -1.618113552083806592527989531636955084420e409L, 
-      4.236528795217618357348618613216833722648e412L, 
-      -1.123047068199051008086174989124136878992e416L, 
-      3.013971787525654770217283559392286666886e419L, 
-      -8.188437573221553030375681429202969070420e422L, 
-      2.251910591336716809153958146725775718707e426L, 
-      -6.268411292043789823075314151509139413399e429L, 
-      1.765990845202322642693572112511312471527e433L, 
-      -5.035154436231331651259071296731160882240e436L, 
-      1.452779356460483245253765356664402207266e440L, 
-      -4.241490890130137339052414960684151515166e443L, 
-      1.252966001692427774088293833338841893293e447L, 
-      -3.744830047478272947978103227876747240343e450L, 
-      1.132315806695710930595876001089232216024e454L, 
-      -3.463510845942701805991786197773934662578e457L, 
-      1.071643382649675572086865465873916611537e461L, 
-      -3.353824475439933688957233489984711465335e464L, 
-      1.061594257145875875963152734129803268488e468L, 
-      -3.398420969215528955528654193586189805265e471L, 
-      1.100192502000434096206138068020551065890e475L, 
-      -3.601686379213993374332690210094863486472e478L, 
-      1.192235170430164900533187239994513019475e482L, 
-      -3.990342751779668381699052942504119409180e485L, 
-      1.350281800938769780891258894167663309221e489L, 
-      -4.619325443466054312873093650888507562249e492L, 
-      1.597522243968586548227514639959727696694e496L, 
-      -5.584753729092155108530929002119620487652e499L, 
-      1.973443623104646193229794524759543752089e503L, 
-      -7.048295441989615807045620880311201930244e506L, 
-      2.544236702499719094591873151590280263560e510L, 
-      -9.281551595258615205927443367289948150345e513L, 
-      3.421757163154453657766296828520235351572e517L, 
-      -1.274733639384538364282697627345068947433e521L, 
-      4.798524805311016034711205886780460173566e524L, 
-      -1.825116948422858388787806917284878870034e528L, 
-      7.013667442807288452441777981425055613982e531L, 
-      -2.723003862685989740898815670978399383114e535L, 
-      1.068014853917260290630122222858884658850e539L, 
-      -4.231650952273697842269381683768681118533e542L, 
-      1.693650052202594386658903598564772900388e546L, 
-      -6.846944855806453360616258582310883597678e549L, 
-      2.795809132238082267120232174243715559601e553L, 
-      -1.153012972808983269106716828311318981951e557L, 
-      4.802368854268746357511997492039592697149e560L, 
-      -2.019995255271910836389761734035403905781e564L, 
-      8.580207235032617856059250643095019760968e567L, 
-      -3.680247942263468164408192134916355198549e571L, 
-      1.593924457586765331397457407661306895942e575L, 
-      -6.970267175232643679233530367569943057501e578L, 
-      3.077528087427698518703282907890556154309e582L, 
-      -1.371846760052887888926055417297342106614e586L, 
-      6.173627360829553396851763207025505289166e589L, 
-      -2.804703130495506384463249394043486916669e593L, 
-      1.286250900087150126167490951216207186092e597L, 
-      -5.954394420063617872366818601092036543220e600L, 
-      2.782297785278756426177542270854984091406e604L, 
-      -1.312214674935307746141207680066262384215e608L, 
-      6.246299145383554153167974732783934504370e611L, 
-      -3.000812007679574430883792565577444226490e615L, 
-      1.454904877136007844493861746476079537075e619L, 
-      -7.118558521873800304612781121044077357278e622L, 
-      3.514739820897817389472822276832677887997e626L, 
-      -1.751137068816377401163011262831890828437e630L, 
-      8.803498091818800678575314081978951179602e633L, 
-      -4.465612911700593572269200981612564161010e637L, 
-      2.285494565287530681465757798517033542888e641L, 
-      -1.180145168917737098025683613598595411329e645L, 
-      6.147941849198393232663105284575149616925e648L, 
-      -3.231069156963603593233679426198974663352e652L, 
-      1.713042725635435041806895849197608270935e656L, 
-      -9.161761363270648920537613435771882898051e659L, 
-      4.942675965960539112005679080810117766825e663L, 
-      -2.689684712697383518131267222872386600031e667L, 
-      1.476320014229917759615308193449511534656e671L, 
-      -8.173037740864781506597184122049453514594e674L, 
-      4.563462313190521363235182420178784459580e678L, 
-      -2.569790015236158475703055501886439298708e682L, 
-      1.459410219452119981958355737832022375085e686L, 
-      -8.358304882556983795372406183642486436653e689L, 
-      4.827305091483557818593092377664570208355e693L, 
-      -2.811394311081493166793414157061950132403e697L, 
-      1.651026863340675349245561261339568827739e701L, 
-      -9.776578579336866764167878646459810047899e704L, 
-      5.837207965197521880181236529616560780535e708L, 
-      -3.513938957938032127105389702846371181520e712L, 
-      2.132747371360190507595748444536911078788e716L, 
-      -1.305047363239192640729466563372665311602e720L, 
-      8.050825342678337497636292798039996484780e723L, 
-      -5.006884161223862543665524155681082112689e727L, 
-      3.139016066011452177570812014513491361235e731L, 
-      -1.983829535212711378291469356666001365873e735L, 
-      1.263822427649676371257598052486237628698e739L, 
-      -8.115678659900522918802121684491754629503e742L, 
-      5.252995164972075271667364371449050412435e746L, 
-      -3.427038125662404660056511738625477058135e750L, 
-      2.253446011834352733279946306835940729858e754L, 
-      -1.493407341897034717876962786798831719683e758L, 
-      9.974681322653365118752729509398728354442e761L, 
-      -6.714230142773850863927710112350816379426e765L, 
-      4.554668668931723346600337564274944733530e769L, 
-      -3.113635386023220127834102980385275379533e773L, 
-      2.144945411287666204679363498162954050208e777L, 
-      -1.488982121181387164932397544378555256016e781L, 
-      1.041537218854627455352298173588983048748e785L, 
-      -7.341073881786613676177562822942175683993e788L, 
-      5.213524272587199574980117351016322518428e792L, 
-      -3.730592531776514409283897139216167197989e796L, 
-      2.689592876341877079083449497724049500175e800L, 
-      -1.953643788231947582529884602972233135002e804L, 
-      1.429691073080500563348668321308878246277e808L, 
-      -1.054059177095488639836063073070536825675e812L, 
-      7.828919160938693948399336431565350676613e815L, 
-      -5.857884457184396382550955498026762014753e819L, 
-      4.415401588264172474136969345712659422380e823L, 
-      -3.352573884181287635796498822858109969161e827L, 
-      2.564210385719224000156548240934108974447e831L, 
-      -1.975534392116037602837941409848663077528e835L, 
-      1.533062123975940045180943006948008486466e839L, 
-      -1.198306160488763291730059994812781226903e843L, 
-      9.434034267770711698676321369174735725321e846L, 
-      -7.480619200038505368468483892246806488879e850L, 
-      5.974161898439971564124576801455052907638e854L, 
-      -4.805125663714699771668630995361572639386e858L, 
-      3.892332138028039952403812726744593073776e862L, 
-      -3.175276505779699340738548328810180869575e866L, 
-      2.608608681939322393581069188271626122519e870L, 
-      -2.158148554392732439392868052394994052628e874L, 
-      1.797993483301448477700600221980862686033e878L, 
-      -1.508407575089108597171576068862286462909e882L, 
-      1.274273406242459482708930389008701147244e886L, 
-      -1.083950475353171986748233157909397370193e890L, 
-      9.284292630726328432038470356821265395331e893L, 
-      -8.007012115449516364480417355063446317414e897L, 
-      6.952871948429568933888979915833266241471e901L, 
-      -6.078828929473797621198666799700739891205e905L, 
-      5.350908089710964244671334224708057812633e909L, 
-      -4.742168072503284973969982758434401589090e913L, 
-      4.231149239401967697257534662010605751136e917L, 
-      -3.800684612827828851942743291026898158947e921L, 
-      3.436984796314246158361599955909956583986e925L, 
+      -2.277640649601959593875058983506938037019e309L,
+      3.945184036046326234163525556422667595884e312L,
+      -6.938525772130602106071724989641405550473e315L,
+      1.238896367577564823729057820219210929986e319L,
+      -2.245542599169309759499987966025604480745e322L,
+      4.131213176073842359732511639489669404266e325L,
+      -7.713581346815269584960928069762882771369e328L,
+      1.461536066837669600638613788471335541313e332L,
+      -2.809904606225532896862935642992712059631e335L,
+      5.480957121318876639512096994413992284327e338L,
+      -1.084573284087686110518125291186079616320e342L,
+      2.176980775647663539729165173863716459962e345L,
+      -4.431998786117553751947439433256752608068e348L,
+      9.150625657715535047417756278073770096073e351L,
+      -1.915867353003157351316577579148683133613e355L,
+      4.067256303542212258698836003682016040629e358L,
+      -8.754223791037736616228150209910348734629e361L,
+      1.910173688735533667244373747124109379826e365L,
+      -4.225001320265091714631115064713174404607e368L,
+      9.471959352547827678466770796787503034505e371L,
+      -2.152149973279986829719817376756088198573e375L,
+      4.955485775334221051344839716507812871361e378L,
+      -1.156225941759134696630956889716381968142e382L,
+      2.733406597646137698610991926705098514017e385L,
+      -6.546868135325176947099912523279938546333e388L,
+      1.588524912441221472814692121069821695547e392L,
+      -3.904354800861715180218598151050191841308e395L,
+      9.719938686092045781827273411668132975319e398L,
+      -2.450763621049522051234479737511375679283e402L,
+      6.257892098396815305085674126334317095277e405L,
+      -1.618113552083806592527989531636955084420e409L,
+      4.236528795217618357348618613216833722648e412L,
+      -1.123047068199051008086174989124136878992e416L,
+      3.013971787525654770217283559392286666886e419L,
+      -8.188437573221553030375681429202969070420e422L,
+      2.251910591336716809153958146725775718707e426L,
+      -6.268411292043789823075314151509139413399e429L,
+      1.765990845202322642693572112511312471527e433L,
+      -5.035154436231331651259071296731160882240e436L,
+      1.452779356460483245253765356664402207266e440L,
+      -4.241490890130137339052414960684151515166e443L,
+      1.252966001692427774088293833338841893293e447L,
+      -3.744830047478272947978103227876747240343e450L,
+      1.132315806695710930595876001089232216024e454L,
+      -3.463510845942701805991786197773934662578e457L,
+      1.071643382649675572086865465873916611537e461L,
+      -3.353824475439933688957233489984711465335e464L,
+      1.061594257145875875963152734129803268488e468L,
+      -3.398420969215528955528654193586189805265e471L,
+      1.100192502000434096206138068020551065890e475L,
+      -3.601686379213993374332690210094863486472e478L,
+      1.192235170430164900533187239994513019475e482L,
+      -3.990342751779668381699052942504119409180e485L,
+      1.350281800938769780891258894167663309221e489L,
+      -4.619325443466054312873093650888507562249e492L,
+      1.597522243968586548227514639959727696694e496L,
+      -5.584753729092155108530929002119620487652e499L,
+      1.973443623104646193229794524759543752089e503L,
+      -7.048295441989615807045620880311201930244e506L,
+      2.544236702499719094591873151590280263560e510L,
+      -9.281551595258615205927443367289948150345e513L,
+      3.421757163154453657766296828520235351572e517L,
+      -1.274733639384538364282697627345068947433e521L,
+      4.798524805311016034711205886780460173566e524L,
+      -1.825116948422858388787806917284878870034e528L,
+      7.013667442807288452441777981425055613982e531L,
+      -2.723003862685989740898815670978399383114e535L,
+      1.068014853917260290630122222858884658850e539L,
+      -4.231650952273697842269381683768681118533e542L,
+      1.693650052202594386658903598564772900388e546L,
+      -6.846944855806453360616258582310883597678e549L,
+      2.795809132238082267120232174243715559601e553L,
+      -1.153012972808983269106716828311318981951e557L,
+      4.802368854268746357511997492039592697149e560L,
+      -2.019995255271910836389761734035403905781e564L,
+      8.580207235032617856059250643095019760968e567L,
+      -3.680247942263468164408192134916355198549e571L,
+      1.593924457586765331397457407661306895942e575L,
+      -6.970267175232643679233530367569943057501e578L,
+      3.077528087427698518703282907890556154309e582L,
+      -1.371846760052887888926055417297342106614e586L,
+      6.173627360829553396851763207025505289166e589L,
+      -2.804703130495506384463249394043486916669e593L,
+      1.286250900087150126167490951216207186092e597L,
+      -5.954394420063617872366818601092036543220e600L,
+      2.782297785278756426177542270854984091406e604L,
+      -1.312214674935307746141207680066262384215e608L,
+      6.246299145383554153167974732783934504370e611L,
+      -3.000812007679574430883792565577444226490e615L,
+      1.454904877136007844493861746476079537075e619L,
+      -7.118558521873800304612781121044077357278e622L,
+      3.514739820897817389472822276832677887997e626L,
+      -1.751137068816377401163011262831890828437e630L,
+      8.803498091818800678575314081978951179602e633L,
+      -4.465612911700593572269200981612564161010e637L,
+      2.285494565287530681465757798517033542888e641L,
+      -1.180145168917737098025683613598595411329e645L,
+      6.147941849198393232663105284575149616925e648L,
+      -3.231069156963603593233679426198974663352e652L,
+      1.713042725635435041806895849197608270935e656L,
+      -9.161761363270648920537613435771882898051e659L,
+      4.942675965960539112005679080810117766825e663L,
+      -2.689684712697383518131267222872386600031e667L,
+      1.476320014229917759615308193449511534656e671L,
+      -8.173037740864781506597184122049453514594e674L,
+      4.563462313190521363235182420178784459580e678L,
+      -2.569790015236158475703055501886439298708e682L,
+      1.459410219452119981958355737832022375085e686L,
+      -8.358304882556983795372406183642486436653e689L,
+      4.827305091483557818593092377664570208355e693L,
+      -2.811394311081493166793414157061950132403e697L,
+      1.651026863340675349245561261339568827739e701L,
+      -9.776578579336866764167878646459810047899e704L,
+      5.837207965197521880181236529616560780535e708L,
+      -3.513938957938032127105389702846371181520e712L,
+      2.132747371360190507595748444536911078788e716L,
+      -1.305047363239192640729466563372665311602e720L,
+      8.050825342678337497636292798039996484780e723L,
+      -5.006884161223862543665524155681082112689e727L,
+      3.139016066011452177570812014513491361235e731L,
+      -1.983829535212711378291469356666001365873e735L,
+      1.263822427649676371257598052486237628698e739L,
+      -8.115678659900522918802121684491754629503e742L,
+      5.252995164972075271667364371449050412435e746L,
+      -3.427038125662404660056511738625477058135e750L,
+      2.253446011834352733279946306835940729858e754L,
+      -1.493407341897034717876962786798831719683e758L,
+      9.974681322653365118752729509398728354442e761L,
+      -6.714230142773850863927710112350816379426e765L,
+      4.554668668931723346600337564274944733530e769L,
+      -3.113635386023220127834102980385275379533e773L,
+      2.144945411287666204679363498162954050208e777L,
+      -1.488982121181387164932397544378555256016e781L,
+      1.041537218854627455352298173588983048748e785L,
+      -7.341073881786613676177562822942175683993e788L,
+      5.213524272587199574980117351016322518428e792L,
+      -3.730592531776514409283897139216167197989e796L,
+      2.689592876341877079083449497724049500175e800L,
+      -1.953643788231947582529884602972233135002e804L,
+      1.429691073080500563348668321308878246277e808L,
+      -1.054059177095488639836063073070536825675e812L,
+      7.828919160938693948399336431565350676613e815L,
+      -5.857884457184396382550955498026762014753e819L,
+      4.415401588264172474136969345712659422380e823L,
+      -3.352573884181287635796498822858109969161e827L,
+      2.564210385719224000156548240934108974447e831L,
+      -1.975534392116037602837941409848663077528e835L,
+      1.533062123975940045180943006948008486466e839L,
+      -1.198306160488763291730059994812781226903e843L,
+      9.434034267770711698676321369174735725321e846L,
+      -7.480619200038505368468483892246806488879e850L,
+      5.974161898439971564124576801455052907638e854L,
+      -4.805125663714699771668630995361572639386e858L,
+      3.892332138028039952403812726744593073776e862L,
+      -3.175276505779699340738548328810180869575e866L,
+      2.608608681939322393581069188271626122519e870L,
+      -2.158148554392732439392868052394994052628e874L,
+      1.797993483301448477700600221980862686033e878L,
+      -1.508407575089108597171576068862286462909e882L,
+      1.274273406242459482708930389008701147244e886L,
+      -1.083950475353171986748233157909397370193e890L,
+      9.284292630726328432038470356821265395331e893L,
+      -8.007012115449516364480417355063446317414e897L,
+      6.952871948429568933888979915833266241471e901L,
+      -6.078828929473797621198666799700739891205e905L,
+      5.350908089710964244671334224708057812633e909L,
+      -4.742168072503284973969982758434401589090e913L,
+      4.231149239401967697257534662010605751136e917L,
+      -3.800684612827828851942743291026898158947e921L,
+      3.436984796314246158361599955909956583986e925L,
       -3.128930718993658356398482705317381808301e929L,
       //
       // 602-1300: http://www.wolframalpha.com/input/?i=TABLE[N[Bernoulli[i]%2C40]%2C+{i%2C602%2C1300%2C2}]
diff --git a/src/mlpack/core/boost_backport/policy.hpp b/src/mlpack/core/boost_backport/policy.hpp
index 391c9d19641..bef8aaf1f95 100644
--- a/src/mlpack/core/boost_backport/policy.hpp
+++ b/src/mlpack/core/boost_backport/policy.hpp
@@ -31,7 +31,7 @@
 #include <stddef.h>
 #include <math.h>
 
-namespace boost{ namespace math{ 
+namespace boost{ namespace math{
 
 namespace tools{
 
@@ -309,7 +309,7 @@ char test_is_valid_arg(const default_policy*);
 char test_is_default_arg(const default_policy*);
 
 template <class T>
-struct is_valid_policy_imp 
+struct is_valid_policy_imp
 {
    BOOST_STATIC_CONSTANT(bool, value = sizeof(::boost::math::policies::detail::test_is_valid_arg(static_cast<T*>(0))) == 1);
 };
@@ -320,13 +320,13 @@ struct is_default_policy_imp
    BOOST_STATIC_CONSTANT(bool, value = sizeof(::boost::math::policies::detail::test_is_default_arg(static_cast<T*>(0))) == 1);
 };
 
-template <class T> struct is_valid_policy 
-: public mpl::bool_< 
+template <class T> struct is_valid_policy
+: public mpl::bool_<
    ::boost::math::policies::detail::is_valid_policy_imp<T>::value>
 {};
 
-template <class T> struct is_default_policy 
-: public mpl::bool_< 
+template <class T> struct is_default_policy
+: public mpl::bool_<
    ::boost::math::policies::detail::is_default_policy_imp<T>::value>
 {
    template <class U>
@@ -389,8 +389,8 @@ typedef default_args<BOOST_MATH_PROMOTE_FLOAT_POLICY, BOOST_MATH_PROMOTE_DOUBLE_
 // Now define the policy type with enough arguments to handle all
 // the policies:
 //
-template <class A1 = default_policy, 
-          class A2 = default_policy, 
+template <class A1 = default_policy,
+          class A2 = default_policy,
           class A3 = default_policy,
           class A4 = default_policy,
           class A5 = default_policy,
@@ -517,9 +517,9 @@ struct policy<detail::forwarding_arg1, detail::forwarding_arg2, default_policy,
    typedef max_root_iterations<> max_root_iterations_type;
 };
 
-template <class Policy, 
-          class A1 = default_policy, 
-          class A2 = default_policy, 
+template <class Policy,
+          class A1 = default_policy,
+          class A2 = default_policy,
           class A3 = default_policy,
           class A4 = default_policy,
           class A5 = default_policy,
@@ -614,9 +614,9 @@ struct normalise
 // Full specialisation to speed up compilation of the common case:
 //
 template <>
-struct normalise<policy<>, 
-          promote_float<false>, 
-          promote_double<false>, 
+struct normalise<policy<>,
+          promote_float<false>,
+          promote_double<false>,
           discrete_quantile<>,
           assert_undefined<>,
           default_policy,
@@ -652,72 +652,72 @@ inline BOOST_MATH_CONSTEXPR policy<> make_policy() BOOST_NOEXCEPT
 
 template <class A1>
 inline BOOST_MATH_CONSTEXPR typename normalise<policy<>, A1>::type make_policy(const A1&) BOOST_NOEXCEPT
-{ 
+{
    typedef typename normalise<policy<>, A1>::type result_type;
-   return result_type(); 
+   return result_type();
 }
 
 template <class A1, class A2>
 inline BOOST_MATH_CONSTEXPR typename normalise<policy<>, A1, A2>::type make_policy(const A1&, const A2&) BOOST_NOEXCEPT
-{ 
+{
    typedef typename normalise<policy<>, A1, A2>::type result_type;
-   return result_type(); 
+   return result_type();
 }
 
 template <class A1, class A2, class A3>
 inline BOOST_MATH_CONSTEXPR typename normalise<policy<>, A1, A2, A3>::type make_policy(const A1&, const A2&, const A3&) BOOST_NOEXCEPT
-{ 
+{
    typedef typename normalise<policy<>, A1, A2, A3>::type result_type;
-   return result_type(); 
+   return result_type();
 }
 
 template <class A1, class A2, class A3, class A4>
 inline BOOST_MATH_CONSTEXPR typename normalise<policy<>, A1, A2, A3, A4>::type make_policy(const A1&, const A2&, const A3&, const A4&) BOOST_NOEXCEPT
-{ 
+{
    typedef typename normalise<policy<>, A1, A2, A3, A4>::type result_type;
-   return result_type(); 
+   return result_type();
 }
 
 template <class A1, class A2, class A3, class A4, class A5>
 inline BOOST_MATH_CONSTEXPR typename normalise<policy<>, A1, A2, A3, A4, A5>::type make_policy(const A1&, const A2&, const A3&, const A4&, const A5&) BOOST_NOEXCEPT
-{ 
+{
    typedef typename normalise<policy<>, A1, A2, A3, A4, A5>::type result_type;
-   return result_type(); 
+   return result_type();
 }
 
 template <class A1, class A2, class A3, class A4, class A5, class A6>
 inline BOOST_MATH_CONSTEXPR typename normalise<policy<>, A1, A2, A3, A4, A5, A6>::type make_policy(const A1&, const A2&, const A3&, const A4&, const A5&, const A6&) BOOST_NOEXCEPT
-{ 
+{
    typedef typename normalise<policy<>, A1, A2, A3, A4, A5, A6>::type result_type;
-   return result_type(); 
+   return result_type();
 }
 
 template <class A1, class A2, class A3, class A4, class A5, class A6, class A7>
 inline BOOST_MATH_CONSTEXPR typename normalise<policy<>, A1, A2, A3, A4, A5, A6, A7>::type make_policy(const A1&, const A2&, const A3&, const A4&, const A5&, const A6&, const A7&) BOOST_NOEXCEPT
-{ 
+{
    typedef typename normalise<policy<>, A1, A2, A3, A4, A5, A6, A7>::type result_type;
-   return result_type(); 
+   return result_type();
 }
 
 template <class A1, class A2, class A3, class A4, class A5, class A6, class A7, class A8>
 inline BOOST_MATH_CONSTEXPR typename normalise<policy<>, A1, A2, A3, A4, A5, A6, A7, A8>::type make_policy(const A1&, const A2&, const A3&, const A4&, const A5&, const A6&, const A7&, const A8&) BOOST_NOEXCEPT
-{ 
+{
    typedef typename normalise<policy<>, A1, A2, A3, A4, A5, A6, A7, A8>::type result_type;
-   return result_type(); 
+   return result_type();
 }
 
 template <class A1, class A2, class A3, class A4, class A5, class A6, class A7, class A8, class A9>
 inline BOOST_MATH_CONSTEXPR typename normalise<policy<>, A1, A2, A3, A4, A5, A6, A7, A8, A9>::type make_policy(const A1&, const A2&, const A3&, const A4&, const A5&, const A6&, const A7&, const A8&, const A9&) BOOST_NOEXCEPT
-{ 
+{
    typedef typename normalise<policy<>, A1, A2, A3, A4, A5, A6, A7, A8, A9>::type result_type;
-   return result_type(); 
+   return result_type();
 }
 
 template <class A1, class A2, class A3, class A4, class A5, class A6, class A7, class A8, class A9, class A10>
 inline BOOST_MATH_CONSTEXPR typename normalise<policy<>, A1, A2, A3, A4, A5, A6, A7, A8, A9, A10>::type make_policy(const A1&, const A2&, const A3&, const A4&, const A5&, const A6&, const A7&, const A8&, const A9&, const A10&) BOOST_NOEXCEPT
-{ 
+{
    typedef typename normalise<policy<>, A1, A2, A3, A4, A5, A6, A7, A8, A9, A10>::type result_type;
-   return result_type(); 
+   return result_type();
 }
 
 template <class A1, class A2, class A3, class A4, class A5, class A6, class A7, class A8, class A9, class A10, class A11>
@@ -808,7 +808,7 @@ struct precision
       // Possibly unknown precision:
       precision_type,
       typename mpl::if_c<
-         ((::std::numeric_limits<Real>::digits <= precision_type::value) 
+         ((::std::numeric_limits<Real>::digits <= precision_type::value)
          || (Policy::precision_type::value <= 0)),
          // Default case, full precision for RealType:
          digits2< ::std::numeric_limits<Real>::digits>,
@@ -966,8 +966,8 @@ inline BOOST_MATH_CONSTEXPR T get_epsilon(BOOST_MATH_EXPLICIT_TEMPLATE_TYPE(T))
 
 namespace detail{
 
-template <class A1, 
-          class A2, 
+template <class A1,
+          class A2,
           class A3,
           class A4,
           class A5,
@@ -1028,7 +1028,7 @@ struct is_noexcept_error_policy
    typedef typename Policy::rounding_error_type             t7;
    typedef typename Policy::indeterminate_result_error_type t8;
 
-   BOOST_STATIC_CONSTANT(bool, value = 
+   BOOST_STATIC_CONSTANT(bool, value =
       ((t1::value != throw_on_error) && (t1::value != user_error)
       && (t2::value != throw_on_error) && (t2::value != user_error)
       && (t3::value != throw_on_error) && (t3::value != user_error)
diff --git a/src/mlpack/core/boost_backport/polygamma.hpp b/src/mlpack/core/boost_backport/polygamma.hpp
index 482796f5963..3ff77836b3b 100644
--- a/src/mlpack/core/boost_backport/polygamma.hpp
+++ b/src/mlpack/core/boost_backport/polygamma.hpp
@@ -18,17 +18,17 @@
 // Forward declarations
 namespace boost { namespace math {
   template <class T, class Policy>
-  inline typename tools::promote_args<T>::type 
+  inline typename tools::promote_args<T>::type
   trigamma(T x, const Policy&);
 
   template <class T>
-  inline typename tools::promote_args<T>::type 
+  inline typename tools::promote_args<T>::type
   trigamma(T x);
 }}
 
 namespace boost { namespace math {
 
-  
+
   template<class T, class Policy>
   inline typename tools::promote_args<T>::type polygamma(const int n, T x, const Policy& pol)
   {
diff --git a/src/mlpack/core/boost_backport/trigamma.hpp b/src/mlpack/core/boost_backport/trigamma.hpp
index c526f1a0af5..ebf435289e4 100644
--- a/src/mlpack/core/boost_backport/trigamma.hpp
+++ b/src/mlpack/core/boost_backport/trigamma.hpp
@@ -99,7 +99,7 @@ T trigamma_prec(T x, const mpl::int_<53>*, const Policy&)
    T y = 1 / x;
    return (1 + tools::evaluate_polynomial(P_4_inf, y) / tools::evaluate_polynomial(Q_4_inf, y)) / x;
 }
-   
+
 template <class T, class Policy>
 T trigamma_prec(T x, const mpl::int_<64>*, const Policy&)
 {
@@ -418,7 +418,7 @@ const typename trigamma_initializer<T, Policy>::init trigamma_initializer<T, Pol
 } // namespace detail
 
 template <class T, class Policy>
-inline typename tools::promote_args<T>::type 
+inline typename tools::promote_args<T>::type
    trigamma(T x, const Policy&)
 {
    typedef typename tools::promote_args<T>::type result_type;
@@ -457,7 +457,7 @@ inline typename tools::promote_args<T>::type
 }
 
 template <class T>
-inline typename tools::promote_args<T>::type 
+inline typename tools::promote_args<T>::type
    trigamma(T x)
 {
    return trigamma(x, policies::policy<>());
diff --git a/src/mlpack/core/dists/gamma_distribution.cpp b/src/mlpack/core/dists/gamma_distribution.cpp
index d2e424390dc..a8da1a28c6d 100644
--- a/src/mlpack/core/dists/gamma_distribution.cpp
+++ b/src/mlpack/core/dists/gamma_distribution.cpp
@@ -29,7 +29,7 @@ GammaDistribution::GammaDistribution(const arma::mat& data,
   Train(data, tol);
 }
 
-GammaDistribution::GammaDistribution(const arma::vec& alpha, 
+GammaDistribution::GammaDistribution(const arma::vec& alpha,
                                      const arma::vec& beta)
 {
   if (beta.n_elem != alpha.n_elem)
@@ -142,7 +142,7 @@ void GammaDistribution::Train(const arma::vec& logMeanxVec,
 
       // Protect against division by 0.
       if (denominator == 0)
-        throw std::logic_error("GammaDistribution::Train() attempted division" 
+        throw std::logic_error("GammaDistribution::Train() attempted division"
             " by 0.");
 
       aEst = 1.0 / ((1.0 / aEst) + nominator / denominator);
@@ -160,7 +160,7 @@ void GammaDistribution::Train(const arma::vec& logMeanxVec,
 }
 
 // Returns the probability of the provided observations.
-void GammaDistribution::Probability(const arma::mat& observations, 
+void GammaDistribution::Probability(const arma::mat& observations,
                                     arma::vec& probabilities) const
 {
   size_t numObs = observations.n_cols;
@@ -191,12 +191,12 @@ void GammaDistribution::Probability(const arma::mat& observations,
 // dimensions.
 double GammaDistribution::Probability(double x, size_t dim) const
 {
-  return std::pow(x, alpha(dim) - 1) * std::exp(-x / beta(dim)) / 
+  return std::pow(x, alpha(dim) - 1) * std::exp(-x / beta(dim)) /
       (std::tgamma(alpha(dim)) * std::pow(beta(dim), alpha(dim)));
 }
 
 // Returns the log probability of the provided observations.
-void GammaDistribution::LogProbability(const arma::mat& observations, 
+void GammaDistribution::LogProbability(const arma::mat& observations,
                                        arma::vec& LogProbabilities) const
 {
   size_t numObs = observations.n_cols;
diff --git a/src/mlpack/core/dists/gamma_distribution.hpp b/src/mlpack/core/dists/gamma_distribution.hpp
index 95b2de8991c..eadf78a55ac 100644
--- a/src/mlpack/core/dists/gamma_distribution.hpp
+++ b/src/mlpack/core/dists/gamma_distribution.hpp
@@ -88,7 +88,7 @@ class GammaDistribution
      *
      * @param rdata Reference data to fit parameters to.
      * @param tol Convergence tolerance. This is *not* an absolute measure:
-     *    It will stop the approximation once the *change* in the value is 
+     *    It will stop the approximation once the *change* in the value is
      *    smaller than tol.
      */
     void Train(const arma::mat& rdata, const double tol = 1e-8);
@@ -101,7 +101,7 @@ class GammaDistribution
      * @param probabilities The probability of each observation. One value per
      *     column of the observations matrix.
      * @param tol Convergence tolerance. This is *not* an absolute measure:
-     *    It will stop the approximation once the *change* in the value is 
+     *    It will stop the approximation once the *change* in the value is
      *    smaller than tol.
      */
     void Train(const arma::mat& observations,
diff --git a/src/mlpack/core/tree/binary_space_tree/ub_tree_split_impl.hpp b/src/mlpack/core/tree/binary_space_tree/ub_tree_split_impl.hpp
index 0841c15c508..46f784b60c2 100644
--- a/src/mlpack/core/tree/binary_space_tree/ub_tree_split_impl.hpp
+++ b/src/mlpack/core/tree/binary_space_tree/ub_tree_split_impl.hpp
@@ -85,7 +85,7 @@ bool UBTreeSplit<BoundType, MatType>::SplitNode(BoundType& bound,
 
     for (; row < data.n_rows; row++)
       for (; bit < order; bit++)
-        lo[row] |= ((AddressElemType) 1 << (order - 1 - bit)); 
+        lo[row] |= ((AddressElemType) 1 << (order - 1 - bit));
   }
 
   // The bound shouldn't contain too many subrectangles.
@@ -128,7 +128,7 @@ bool UBTreeSplit<BoundType, MatType>::SplitNode(BoundType& bound,
 
     for (; row < data.n_rows; row++)
       for (; bit < order; bit++)
-        hi[row] &= ~((AddressElemType) 1 << (order - 1 - bit)); 
+        hi[row] &= ~((AddressElemType) 1 << (order - 1 - bit));
   }
 
   // Set the minimum and the maximum addresses.
diff --git a/src/mlpack/core/tree/cellbound.hpp b/src/mlpack/core/tree/cellbound.hpp
index 7b45cc523f4..5805f99b7a5 100644
--- a/src/mlpack/core/tree/cellbound.hpp
+++ b/src/mlpack/core/tree/cellbound.hpp
@@ -123,7 +123,7 @@ class CellBound
   arma::Col<AddressElemType>& LoAddress() { return loAddress; }
   //! Modify lower address.
   const arma::Col<AddressElemType>& LoAddress() const {return loAddress; }
-  
+
   //! Get high address.
   arma::Col<AddressElemType>& HiAddress() { return hiAddress; }
   //! Modify high address.
diff --git a/src/mlpack/core/tree/rectangle_tree/r_plus_tree_split_policy.hpp b/src/mlpack/core/tree/rectangle_tree/r_plus_tree_split_policy.hpp
index 0a6047343e2..3b22375b3e7 100644
--- a/src/mlpack/core/tree/rectangle_tree/r_plus_tree_split_policy.hpp
+++ b/src/mlpack/core/tree/rectangle_tree/r_plus_tree_split_policy.hpp
@@ -61,7 +61,7 @@ class RPlusTreeSplitPolicy
    * Return the minimum bounding rectangle of the node.
    * This method should always return the bound that is used for the
    * decision-making in GetSplitPolicy().
-   * 
+   *
    * @param node The node whose bound is requested.
     */
   template<typename TreeType>
diff --git a/src/mlpack/core/tree/rectangle_tree/rectangle_tree_impl.hpp b/src/mlpack/core/tree/rectangle_tree/rectangle_tree_impl.hpp
index 42746e84b80..d88452499ad 100644
--- a/src/mlpack/core/tree/rectangle_tree/rectangle_tree_impl.hpp
+++ b/src/mlpack/core/tree/rectangle_tree/rectangle_tree_impl.hpp
@@ -113,7 +113,7 @@ RectangleTree(
     RectangleTree<MetricType, StatisticType, MatType, SplitType, DescentType,
                   AuxiliaryInformationType>*
         parentNode,const size_t numMaxChildren) :
-    maxNumChildren(numMaxChildren > 0 ? numMaxChildren : 
+    maxNumChildren(numMaxChildren > 0 ? numMaxChildren :
                                         parentNode->MaxNumChildren()),
     minNumChildren(parentNode->MinNumChildren()),
     numChildren(0),
diff --git a/src/mlpack/core/tree/rectangle_tree/x_tree_split_impl.hpp b/src/mlpack/core/tree/rectangle_tree/x_tree_split_impl.hpp
index a8f6dc89634..b8482c41f78 100644
--- a/src/mlpack/core/tree/rectangle_tree/x_tree_split_impl.hpp
+++ b/src/mlpack/core/tree/rectangle_tree/x_tree_split_impl.hpp
@@ -316,7 +316,7 @@ bool XTreeSplit::SplitNonLeafNode(TreeType *tree,std::vector<bool>& relevels)
   std::vector<bool> axes(tree->Bound().Dim());
   std::vector<int> dimensionsLastUsed(tree->NumChildren());
   for (size_t i = 0; i < tree->NumChildren(); i++)
-    dimensionsLastUsed[i] = 
+    dimensionsLastUsed[i] =
                     tree->Child(i).AuxiliaryInfo().SplitHistory().lastDimension;
   std::sort(dimensionsLastUsed.begin(), dimensionsLastUsed.end());
 
@@ -328,7 +328,7 @@ bool XTreeSplit::SplitNonLeafNode(TreeType *tree,std::vector<bool>& relevels)
   {
     axes[i] = true;
     for (size_t j = 0; j < tree->NumChildren(); j++)
-      axes[i] = axes[i] & 
+      axes[i] = axes[i] &
                 tree->Child(j).AuxiliaryInfo().SplitHistory().history[i];
     if (axes[i] == true)
     {
@@ -342,7 +342,7 @@ bool XTreeSplit::SplitNonLeafNode(TreeType *tree,std::vector<bool>& relevels)
     {
       axes[i] = true;
       for (size_t j = 0; j < tree->NumChildren(); j++)
-        axes[i] = axes[i] & 
+        axes[i] = axes[i] &
                   tree->Child(j).AuxiliaryInfo().SplitHistory().history[i];
       if (axes[i] == true)
       {
@@ -688,7 +688,7 @@ bool XTreeSplit::SplitNonLeafNode(TreeType *tree,std::vector<bool>& relevels)
           (tree->Parent()->NumChildren() == 1))
       {
         // We make the root a supernode instead.
-        tree->Parent()->MaxNumChildren() = tree->MaxNumChildren() + 
+        tree->Parent()->MaxNumChildren() = tree->MaxNumChildren() +
                               tree->AuxiliaryInfo().NormalNodeMaxNumChildren();
         tree->Parent()->children.resize(tree->Parent()->MaxNumChildren() + 1);
         tree->Parent()->NumChildren() = tree->NumChildren();
@@ -706,7 +706,7 @@ bool XTreeSplit::SplitNonLeafNode(TreeType *tree,std::vector<bool>& relevels)
       }
 
       // If we don't have to worry about the root, we just enlarge this node.
-      tree->MaxNumChildren() += 
+      tree->MaxNumChildren() +=
                               tree->AuxiliaryInfo().NormalNodeMaxNumChildren();
       tree->children.resize(tree->MaxNumChildren() + 1);
       for (size_t i = 0; i < tree->NumChildren(); i++)
diff --git a/src/mlpack/methods/adaboost/adaboost_main.cpp b/src/mlpack/methods/adaboost/adaboost_main.cpp
index c05f083ecb5..4310e2048fd 100644
--- a/src/mlpack/methods/adaboost/adaboost_main.cpp
+++ b/src/mlpack/methods/adaboost/adaboost_main.cpp
@@ -229,7 +229,7 @@ int main(int argc, char *argv[])
 
     Row<size_t> results;
     data::RevertLabels(predictedLabels, m.Mappings(), results);
-    
+
     if (CLI::HasParam("output"))
       CLI::GetParam<arma::Row<size_t>>("output") = std::move(results);
   }
diff --git a/src/mlpack/methods/ann/activation_functions/softplus_function.hpp b/src/mlpack/methods/ann/activation_functions/softplus_function.hpp
index ab306496c79..4adc5d746c1 100644
--- a/src/mlpack/methods/ann/activation_functions/softplus_function.hpp
+++ b/src/mlpack/methods/ann/activation_functions/softplus_function.hpp
@@ -43,7 +43,7 @@ namespace ann /** Artificial Neural Network. */ {
 class SoftplusFunction
 {
   public:
-  
+
   /**
    * Computes the softplus function.
    *
diff --git a/src/mlpack/methods/dbscan/dbscan.hpp b/src/mlpack/methods/dbscan/dbscan.hpp
index e35d7f9bfb1..58ce304171c 100644
--- a/src/mlpack/methods/dbscan/dbscan.hpp
+++ b/src/mlpack/methods/dbscan/dbscan.hpp
@@ -65,7 +65,7 @@ class DBSCAN
          PointSelectionPolicy pointSelector = PointSelectionPolicy());
 
   /**
-   * Performs DBSCAN clustering on the data, returning number of clusters 
+   * Performs DBSCAN clustering on the data, returning number of clusters
    * and also the centroid of each cluster.
    *
    * @tparam MatType Type of matrix (arma::mat or arma::sp_mat).
@@ -77,7 +77,7 @@ class DBSCAN
                  arma::mat& centroids);
 
   /**
-   * Performs DBSCAN clustering on the data, returning number of clusters 
+   * Performs DBSCAN clustering on the data, returning number of clusters
    * and also the list of cluster assignments.
    *
    * @tparam MatType Type of matrix (arma::mat or arma::sp_mat).
@@ -89,7 +89,7 @@ class DBSCAN
                  arma::Row<size_t>& assignments);
 
   /**
-   * Performs DBSCAN clustering on the data, returning number of clusters, 
+   * Performs DBSCAN clustering on the data, returning number of clusters,
    * the centroid of each cluster and also the list of cluster assignments.
    * If assignments[i] == assignments.n_elem - 1, then the point is considered
    * "noise".
diff --git a/src/mlpack/methods/dbscan/dbscan_impl.hpp b/src/mlpack/methods/dbscan/dbscan_impl.hpp
index 91e0b829f10..1c6d5e76675 100644
--- a/src/mlpack/methods/dbscan/dbscan_impl.hpp
+++ b/src/mlpack/methods/dbscan/dbscan_impl.hpp
@@ -35,7 +35,7 @@ DBSCAN<RangeSearchType, PointSelectionPolicy>::DBSCAN(
 }
 
 /**
- * Performs DBSCAN clustering on the data, returning number of clusters 
+ * Performs DBSCAN clustering on the data, returning number of clusters
  * and also the centroid of each cluster.
  */
 template<typename RangeSearchType, typename PointSelectionPolicy>
@@ -53,7 +53,7 @@ size_t DBSCAN<RangeSearchType, PointSelectionPolicy>::Cluster(
 }
 
 /**
- * Performs DBSCAN clustering on the data, returning number of clusters, 
+ * Performs DBSCAN clustering on the data, returning number of clusters,
  * the centroid of each cluster and also the list of cluster assignments.
  */
 template<typename RangeSearchType, typename PointSelectionPolicy>
@@ -89,7 +89,7 @@ size_t DBSCAN<RangeSearchType, PointSelectionPolicy>::Cluster(
 }
 
 /**
- * Performs DBSCAN clustering on the data, returning number of clusters 
+ * Performs DBSCAN clustering on the data, returning number of clusters
  * and also the list of cluster assignments.
  */
 template<typename RangeSearchType, typename PointSelectionPolicy>
diff --git a/src/mlpack/methods/naive_bayes/naive_bayes_classifier.hpp b/src/mlpack/methods/naive_bayes/naive_bayes_classifier.hpp
index 71df747331f..b5b4e1fce4b 100644
--- a/src/mlpack/methods/naive_bayes/naive_bayes_classifier.hpp
+++ b/src/mlpack/methods/naive_bayes/naive_bayes_classifier.hpp
@@ -120,7 +120,7 @@ class NaiveBayesClassifier
   size_t Classify(const VecType& point) const;
 
   /**
-   * Classify the given point using the training GaussianNB model 
+   * Classify the given point using the training GaussianNB model
    * and also return estimates of the probability for
    * each class in the given vector.
    *
@@ -152,8 +152,8 @@ class NaiveBayesClassifier
                 arma::Row<size_t>& predictions) const;
 
   /**
-   * Classify the given points using the training GaussianNB model 
-   * and also return estimates of the probabilities for each class in the given matrix.  
+   * Classify the given points using the training GaussianNB model
+   * and also return estimates of the probabilities for each class in the given matrix.
    * The predicted labels for each point are stored in the given vector.
    *
    * @code
diff --git a/src/mlpack/methods/range_search/rs_model.cpp b/src/mlpack/methods/range_search/rs_model.cpp
index 401372b16b8..747c3f600e8 100644
--- a/src/mlpack/methods/range_search/rs_model.cpp
+++ b/src/mlpack/methods/range_search/rs_model.cpp
@@ -136,7 +136,7 @@ void RSModel::BuildModel(arma::mat&& referenceSet,
       break;
 
     case BALL_TREE:
-      rSearch = new RSType<tree::BallTree>(naive, singleMode);  
+      rSearch = new RSType<tree::BallTree>(naive, singleMode);
       break;
 
     case X_TREE:
diff --git a/src/mlpack/methods/softmax_regression/softmax_regression_main.cpp b/src/mlpack/methods/softmax_regression/softmax_regression_main.cpp
index f17e11d0562..f262fb4082c 100644
--- a/src/mlpack/methods/softmax_regression/softmax_regression_main.cpp
+++ b/src/mlpack/methods/softmax_regression/softmax_regression_main.cpp
@@ -179,7 +179,7 @@ void TestClassifyAcc(size_t numClasses, const Model& model)
   // Calculate accuracy, if desired.
   if (CLI::HasParam("test_labels"))
   {
-    arma::Row<size_t> testLabels = 
+    arma::Row<size_t> testLabels =
       std::move(CLI::GetParam<arma::Row<size_t>>("test_labels"));
 
     if (testData.n_cols != testLabels.n_elem)
@@ -231,7 +231,7 @@ unique_ptr<Model> TrainSoftmax(const size_t maxIterations)
   else
   {
     arma::mat trainData = std::move(CLI::GetParam<arma::mat>("training"));
-    arma::Row<size_t> trainLabels = 
+    arma::Row<size_t> trainLabels =
         std::move(CLI::GetParam<arma::Row<size_t>>("labels"));
 
     if (trainData.n_cols != trainLabels.n_elem)
diff --git a/src/mlpack/tests/det_test.cpp b/src/mlpack/tests/det_test.cpp
index b09781bef1d..bff2baf583c 100644
--- a/src/mlpack/tests/det_test.cpp
+++ b/src/mlpack/tests/det_test.cpp
@@ -150,30 +150,30 @@ BOOST_AUTO_TEST_CASE(TestSplitData)
 BOOST_AUTO_TEST_CASE(TestSparseFindSplit)
 {
   arma::mat realData(4,7);
-  
+
   realData << .0 << 4 << 5 << 7 << 0 << 5 << 0 << arma::endr
            << .0 << 5 << 0 << 0 << 1 << 7 << 1 << arma::endr
            << .0 << 5 << 6 << 7 << 1 << 0 << 8 << arma::endr
            << -1 << 2 << 5 << 0 << 0 << 0 << 0 << arma::endr;
-  
+
   arma::sp_mat testData(realData);
-  
+
   DTree<arma::sp_mat> testDTree(testData);
-  
+
   size_t obDim, trueDim;
   double trueLeftError, obLeftError, trueRightError, obRightError, obSplit, trueSplit;
-  
+
   trueDim = 1;
   trueSplit = .5;
   trueLeftError = 2 * log(3.0 / 7.0) - (log(7.0) + log(0.5) + log(8.0) + log(6.0));
   trueRightError = 2 * log(4.0 / 7.0) - (log(7.0) + log(6.5) + log(8.0) + log(6.0));
-  
+
   testDTree.logVolume = log(7.0) + log(7.0) + log(8.0) + log(6.0);
   BOOST_REQUIRE(testDTree.FindSplit(testData, obDim, obSplit, obLeftError, obRightError, 1));
-  
+
   BOOST_REQUIRE(trueDim == obDim);
   BOOST_REQUIRE_CLOSE(trueSplit, obSplit, 1e-10);
-  
+
   BOOST_REQUIRE_CLOSE(trueLeftError, obLeftError, 1e-10);
   BOOST_REQUIRE_CLOSE(trueRightError, obRightError, 1e-10);
 }
@@ -181,26 +181,26 @@ BOOST_AUTO_TEST_CASE(TestSparseFindSplit)
 BOOST_AUTO_TEST_CASE(TestSparseSplitData)
 {
   arma::mat realData(4,7);
-  
+
   realData << .0 << 4 << 5 << 7 << 0 << 5 << 0 << arma::endr
            << .0 << 5 << 0 << 0 << 1 << 7 << 1 << arma::endr
            << .0 << 5 << 6 << 7 << 1 << 0 << 8 << arma::endr
            << -1 << 2 << 5 << 0 << 0 << 0 << 0 << arma::endr;
-  
+
   arma::sp_mat testData(realData);
-  
+
   DTree<arma::sp_mat> testDTree(testData);
-  
+
   arma::Col<size_t> oTest(7);
   oTest << 1 << 2 << 3 << 4 << 5 << 6 << 7;
-  
+
   size_t splitDim = 1;
   double trueSplitVal = .5;
-  
+
   size_t splitInd = testDTree.SplitData(testData, splitDim, trueSplitVal, oTest);
-  
+
   BOOST_REQUIRE_EQUAL(splitInd, 3); // 2 points on left side.
-  
+
   BOOST_REQUIRE_EQUAL(oTest[0], 1);
   BOOST_REQUIRE_EQUAL(oTest[1], 4);
   BOOST_REQUIRE_EQUAL(oTest[2], 3);
diff --git a/src/mlpack/tests/distribution_test.cpp b/src/mlpack/tests/distribution_test.cpp
index 242927225ac..bbf22a6c400 100644
--- a/src/mlpack/tests/distribution_test.cpp
+++ b/src/mlpack/tests/distribution_test.cpp
@@ -129,7 +129,7 @@ BOOST_AUTO_TEST_CASE(MultiDiscreteDistributionTrainProbTest)
   arma::mat obs("0 1 1 1 2 2 2 2 2 2;"
                 "0 0 0 1 1 1 2 2 2 2;"
                 "0 0 0 1 1 2 2 2 2 2;");
- 
+
   d.Train(obs);
   BOOST_REQUIRE_CLOSE(d.Probability("0 0 0"), 0.009, 1e-5);
   BOOST_REQUIRE_CLOSE(d.Probability("0 1 2"), 0.015, 1e-5);
@@ -137,7 +137,7 @@ BOOST_AUTO_TEST_CASE(MultiDiscreteDistributionTrainProbTest)
 }
 
 /**
- * Make sure we initialize multidimensional probability distribution 
+ * Make sure we initialize multidimensional probability distribution
  * correctly.
  */
 BOOST_AUTO_TEST_CASE(MultiDiscreteDistributionConstructorTest)
diff --git a/src/mlpack/tests/ind2sub_test.cpp b/src/mlpack/tests/ind2sub_test.cpp
index c43cb00a76e..98e425c01dc 100644
--- a/src/mlpack/tests/ind2sub_test.cpp
+++ b/src/mlpack/tests/ind2sub_test.cpp
@@ -16,7 +16,7 @@
 BOOST_AUTO_TEST_SUITE(ind2subTest);
 
 /**
- * This test checks whether ind2sub and sub2ind are 
+ * This test checks whether ind2sub and sub2ind are
  * compiled successfully and that they function properly.
  */
 BOOST_AUTO_TEST_CASE(ind2sub_test)
diff --git a/src/mlpack/tests/prefixedoutstream_test.cpp b/src/mlpack/tests/prefixedoutstream_test.cpp
index 5eabc6a31ce..eb4a93cae43 100644
--- a/src/mlpack/tests/prefixedoutstream_test.cpp
+++ b/src/mlpack/tests/prefixedoutstream_test.cpp
@@ -29,7 +29,7 @@ using namespace mlpack::util;
 BOOST_AUTO_TEST_SUITE(PrefixedOutStreamTest);
 
 /**
- * Test the output of CLI using PrefixedOutStream.  We will pass bogus 
+ * Test the output of CLI using PrefixedOutStream.  We will pass bogus
  * input to a stringstream so that none of it gets to the screen.
  */
 BOOST_AUTO_TEST_CASE(TestPrefixedOutStreamBasic)
@@ -220,11 +220,11 @@ BOOST_AUTO_TEST_CASE(TestArmaCustomPrecision)
   pss << trans(test2);
 
   BOOST_REQUIRE_EQUAL(ss.str(),
-      BASH_GREEN "[INFO ] " BASH_CLEAR 
+      BASH_GREEN "[INFO ] " BASH_CLEAR
       "    1.00000000    2.50000000   40.00000000\n"
-      BASH_GREEN "[INFO ] " BASH_CLEAR 
+      BASH_GREEN "[INFO ] " BASH_CLEAR
       "    1.50000000    3.00000000    4.50000000\n"
-      BASH_GREEN "[INFO ] " BASH_CLEAR 
+      BASH_GREEN "[INFO ] " BASH_CLEAR
       "    2.00000000    3.50000000    4.99999000\n");
 
   // Test stream after reset.
diff --git a/src/mlpack/tests/tree_test.cpp b/src/mlpack/tests/tree_test.cpp
index f0463c784ed..449ebebc05f 100644
--- a/src/mlpack/tests/tree_test.cpp
+++ b/src/mlpack/tests/tree_test.cpp
@@ -1573,7 +1573,7 @@ void CheckRPTreeSplit(const TreeType& tree)
       BOOST_REQUIRE_LE(maxDist, dist *
           (1.0 + 10.0 * std::numeric_limits<ElemType>::epsilon()));
     }
-    
+
   }
 
   CheckRPTreeSplit<TreeType, MetricType>(*tree.Left());
diff --git a/src/mlpack/tests/ub_tree_test.cpp b/src/mlpack/tests/ub_tree_test.cpp
index 7e9f0fd66e1..8b75e2d7e31 100644
--- a/src/mlpack/tests/ub_tree_test.cpp
+++ b/src/mlpack/tests/ub_tree_test.cpp
@@ -203,7 +203,7 @@ void CheckDistance(TreeType& tree, TreeType* node = NULL)
       BOOST_REQUIRE_LE(maxDist, r.Hi() *
           (1.0 + 10 * std::numeric_limits<ElemType>::epsilon()));
     }
-      
+
     if (!tree.IsLeaf())
     {
       CheckDistance<TreeType, MetricType>(*tree.Left());
diff --git a/src/mlpack/tests/vantage_point_tree_test.cpp b/src/mlpack/tests/vantage_point_tree_test.cpp
index ba60f2d126e..2223341f21c 100644
--- a/src/mlpack/tests/vantage_point_tree_test.cpp
+++ b/src/mlpack/tests/vantage_point_tree_test.cpp
@@ -107,7 +107,7 @@ BOOST_AUTO_TEST_CASE(HollowBallBoundTest)
   b2 = HollowBallBound<EuclideanDistance>(0.5, 2.0,
       arma::vec("1.0 8.0 3.0 4.0 5.0"));
   BOOST_REQUIRE_CLOSE(b.MaxDistance(b2), 12.0, 1e-5);
-  
+
   b2 = HollowBallBound<EuclideanDistance>(0.5, 2.0,
       arma::vec("1.0 3.0 3.0 4.0 5.0"));
   BOOST_REQUIRE_CLOSE(b.MaxDistance(b2), 7.0, 1e-5);
@@ -118,7 +118,7 @@ BOOST_AUTO_TEST_CASE(HollowBallBoundTest)
 
   b1 |= b2;
   BOOST_REQUIRE_CLOSE(b1.InnerRadius(), 0.5, 1e-5);
-  
+
   b1 = b;
   b2 = HollowBallBound<EuclideanDistance>(0.5, 2.0,
       arma::vec("1.0 3.0 3.0 4.0 5.0"));