Update nanoflann.

vendor/nanoflann/nanoflann.hpp

@@ -278,105 +278,6 @@ class RadiusResultSet {
 
 struct Metric {};
 
-/** Manhattan distance functor (generic version, optimized for
- * high-dimensionality data sets). Corresponding distance traits:
- * nanoflann::metric_L1 \tparam T Type of the elements (e.g. double, float,
- * uint8_t) \tparam _DistanceType Type of distance variables (must be signed)
- * (e.g. float, double, int64_t)
- */
-template <class T, class DataSource, typename _DistanceType = T>
-struct L1_Adaptor {
-  typedef T ElementType;
-  typedef _DistanceType DistanceType;
-
-  const DataSource &data_source;
-
-  L1_Adaptor(const DataSource &_data_source) : data_source(_data_source) {}
-
-  inline DistanceType evalMetric(const T *a, const size_t b_idx, size_t size,
-                                 DistanceType worst_dist = -1) const {
-    DistanceType result = DistanceType();
-    const T *last = a + size;
-    const T *lastgroup = last - 3;
-    size_t d = 0;
-
-    /* Process 4 items with each loop for efficiency. */
-    while (a < lastgroup) {
-      const DistanceType diff0 =
-          std::abs(a[0] - data_source.kdtree_get_pt(b_idx, d++));
-      const DistanceType diff1 =
-          std::abs(a[1] - data_source.kdtree_get_pt(b_idx, d++));
-      const DistanceType diff2 =
-          std::abs(a[2] - data_source.kdtree_get_pt(b_idx, d++));
-      const DistanceType diff3 =
-          std::abs(a[3] - data_source.kdtree_get_pt(b_idx, d++));
-      result += diff0 + diff1 + diff2 + diff3;
-      a += 4;
-      if ((worst_dist > 0) && (result > worst_dist)) {
-        return result;
-      }
-    }
-    /* Process last 0-3 components.  Not needed for standard vector lengths. */
-    while (a < last) {
-      result += std::abs(*a++ - data_source.kdtree_get_pt(b_idx, d++));
-    }
-    return result;
-  }
-
-  template <typename U, typename V>
-  inline DistanceType accum_dist(const U a, const V b, const size_t) const {
-    return std::abs(a - b);
-  }
-};
-
-/** Squared Euclidean distance functor (generic version, optimized for
- * high-dimensionality data sets). Corresponding distance traits:
- * nanoflann::metric_L2 \tparam T Type of the elements (e.g. double, float,
- * uint8_t) \tparam _DistanceType Type of distance variables (must be signed)
- * (e.g. float, double, int64_t)
- */
-template <class T, class DataSource, typename _DistanceType = T>
-struct L2_Adaptor {
-  typedef T ElementType;
-  typedef _DistanceType DistanceType;
-
-  const DataSource &data_source;
-
-  L2_Adaptor(const DataSource &_data_source) : data_source(_data_source) {}
-
-  inline DistanceType evalMetric(const T *a, const size_t b_idx, size_t size,
-                                 DistanceType worst_dist = -1) const {
-    DistanceType result = DistanceType();
-    const T *last = a + size;
-    const T *lastgroup = last - 3;
-    size_t d = 0;
-
-    /* Process 4 items with each loop for efficiency. */
-    while (a < lastgroup) {
-      const DistanceType diff0 = a[0] - data_source.kdtree_get_pt(b_idx, d++);
-      const DistanceType diff1 = a[1] - data_source.kdtree_get_pt(b_idx, d++);
-      const DistanceType diff2 = a[2] - data_source.kdtree_get_pt(b_idx, d++);
-      const DistanceType diff3 = a[3] - data_source.kdtree_get_pt(b_idx, d++);
-      result += diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3;
-      a += 4;
-      if ((worst_dist > 0) && (result > worst_dist)) {
-        return result;
-      }
-    }
-    /* Process last 0-3 components.  Not needed for standard vector lengths. */
-    while (a < last) {
-      const DistanceType diff0 = *a++ - data_source.kdtree_get_pt(b_idx, d++);
-      result += diff0 * diff0;
-    }
-    return result;
-  }
-
-  template <typename U, typename V>
-  inline DistanceType accum_dist(const U a, const V b, const size_t) const {
-    return (a - b) * (a - b);
-  }
-};
-
 /** Squared Euclidean (L2) distance functor (suitable for low-dimensionality
  * datasets, like 2D or 3D point clouds) Corresponding distance traits:
  * nanoflann::metric_L2_Simple \tparam T Type of the elements (e.g. double,
@@ -409,18 +310,6 @@ struct L2_Simple_Adaptor {
   }
 };
 
-/** Metaprogramming helper traits class for the L1 (Manhattan) metric */
-struct metric_L1 : public Metric {
-  template <class T, class DataSource> struct traits {
-    typedef L1_Adaptor<T, DataSource> distance_t;
-  };
-};
-/** Metaprogramming helper traits class for the L2 (Euclidean) metric */
-struct metric_L2 : public Metric {
-  template <class T, class DataSource> struct traits {
-    typedef L2_Adaptor<T, DataSource> distance_t;
-  };
-};
 /** Metaprogramming helper traits class for the L2_simple (Euclidean) metric */
 struct metric_L2_Simple : public Metric {
   template <class T, class DataSource> struct traits {