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diff --git a/bordeaux/learning/stochastic_linear_ranker/native/sparse_weight_vector.h b/bordeaux/learning/stochastic_linear_ranker/native/sparse_weight_vector.h
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index b5f1ff5af..000000000
--- a/bordeaux/learning/stochastic_linear_ranker/native/sparse_weight_vector.h
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-/*
- * Copyright (C) 2012 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-// Purpose: A container for sparse weight vectors
-// Maintains the sparse vector as a list of (name, value) pairs alongwith
-// a normalizer_. All operations assume that (name, value/normalizer_) is the
-// true value in question.
-
-#ifndef LEARNING_STOCHASTIC_LINEAR_SPARSE_WEIGHT_VECTOR_H_
-#define LEARNING_STOCHASTIC_LINEAR_SPARSE_WEIGHT_VECTOR_H_
-
-#include <math.h>
-
-#include <iosfwd>
-#include <sstream>
-#include <string>
-#include <unordered_map>
-
-#include "common_defs.h"
-
-namespace learning_stochastic_linear {
-
-template<class Key = std::string, class Hash = std::unordered_map<Key, double> >
-class SparseWeightVector {
- public:
-  typedef Hash Wmap;
-  typedef typename Wmap::iterator Witer;
-  typedef typename Wmap::const_iterator Witer_const;
-  SparseWeightVector() {
-    normalizer_ = 1.0;
-  }
-  ~SparseWeightVector() {}
-  explicit SparseWeightVector(const SparseWeightVector<Key, Hash> &other) {
-    CopyFrom(other);
-  }
-  void operator=(const SparseWeightVector<Key, Hash> &other) {
-    CopyFrom(other);
-  }
-  void CopyFrom(const SparseWeightVector<Key, Hash> &other) {
-    w_ = other.w_;
-    wmin_ = other.wmin_;
-    wmax_ = other.wmax_;
-    normalizer_ = other.normalizer_;
-  }
-
-  // This function implements checks to prevent unbounded vectors. It returns
-  // true if the checks succeed and false otherwise. A vector is deemed invalid
-  // if any of these conditions are met:
-  // 1. it has no values.
-  // 2. its normalizer is nan or inf or close to zero.
-  // 3. any of its values are nan or inf.
-  // 4. its L0 norm is close to zero.
-  bool IsValid() const;
-
-  // Normalizer getters and setters.
-  double GetNormalizer() const {
-    return normalizer_;
-  }
-  void SetNormalizer(const double norm) {
-    normalizer_ = norm;
-  }
-  void NormalizerMultUpdate(const double mul) {
-    normalizer_ = normalizer_ * mul;
-  }
-  void NormalizerAddUpdate(const double add) {
-    normalizer_ += add;
-  }
-
-  // Divides all the values by the normalizer, then it resets it to 1.0
-  void ResetNormalizer();
-
-  // Bound getters and setters.
-  // True if there is a bound with val containing the bound. false otherwise.
-  bool GetElementMinBound(const Key &fname, double *val) const {
-    return GetValue(wmin_, fname, val);
-  }
-  bool GetElementMaxBound(const Key &fname, double *val) const {
-    return GetValue(wmax_, fname, val);
-  }
-  void SetElementMinBound(const Key &fname, const double bound) {
-    wmin_[fname] = bound;
-  }
-  void SetElementMaxBound(const Key &fname, const double bound) {
-    wmax_[fname] = bound;
-  }
-  // Element getters and setters.
-  double GetElement(const Key &fname) const {
-    double val = 0;
-    GetValue(w_, fname, &val);
-    return val;
-  }
-  void SetElement(const Key &fname, const double val) {
-    //DCHECK(!isnan(val));
-    w_[fname] = val;
-  }
-  void AddUpdateElement(const Key &fname, const double val) {
-    w_[fname] += val;
-  }
-  void MultUpdateElement(const Key &fname, const double val) {
-    w_[fname] *= val;
-  }
-  // Load another weight vectors. Will overwrite the current vector.
-  void LoadWeightVector(const SparseWeightVector<Key, Hash> &vec) {
-    w_.clear();
-    w_.insert(vec.w_.begin(), vec.w_.end());
-    wmax_.insert(vec.wmax_.begin(), vec.wmax_.end());
-    wmin_.insert(vec.wmin_.begin(), vec.wmin_.end());
-    normalizer_ = vec.normalizer_;
-  }
-  void Clear() {
-    w_.clear();
-    wmax_.clear();
-    wmin_.clear();
-  }
-  const Wmap& GetMap() const {
-    return w_;
-  }
-  // Vector Operations.
-  void AdditiveWeightUpdate(const double multiplier,
-                            const SparseWeightVector<Key, Hash> &w1,
-                            const double additive_const);
-  void AdditiveSquaredWeightUpdate(const double multiplier,
-                                   const SparseWeightVector<Key, Hash> &w1,
-                                   const double additive_const);
-  void AdditiveInvSqrtWeightUpdate(const double multiplier,
-                                   const SparseWeightVector<Key, Hash> &w1,
-                                   const double additive_const);
-  void MultWeightUpdate(const SparseWeightVector<Key, Hash> &w1);
-  double DotProduct(const SparseWeightVector<Key, Hash> &s) const;
-  // L-x norm. eg. L1, L2.
-  double LxNorm(const double x) const;
-  double L2Norm() const;
-  double L1Norm() const;
-  double L0Norm(const double epsilon) const;
-  // Bound preserving updates.
-  void AdditiveWeightUpdateBounded(const double multiplier,
-                                   const SparseWeightVector<Key, Hash> &w1,
-                                   const double additive_const);
-  void MultWeightUpdateBounded(const SparseWeightVector<Key, Hash> &w1);
-  void ReprojectToBounds();
-  void ReprojectL0(const double l0_norm);
-  void ReprojectL1(const double l1_norm);
-  void ReprojectL2(const double l2_norm);
-  // Reproject using the given norm.
-  // Will also rescale regularizer_ if it gets too small/large.
-  int32 Reproject(const double norm, const RegularizationType r);
-  // Convert this vector to a string, simply for debugging.
-  std::string DebugString() const {
-    std::stringstream stream;
-    stream << *this;
-    return stream.str();
-  }
- private:
-  // The weight map.
-  Wmap w_;
-  // Constraint bounds.
-  Wmap wmin_;
-  Wmap wmax_;
-  // Normalizing constant in magnitude measurement.
-  double normalizer_;
-  // This function is necessary since by default unordered_map inserts an
-  // element if it does not find the key through [] operator. It implements a
-  // lookup without the space overhead of an add.
-  bool GetValue(const Wmap &w1, const Key &fname, double *val) const {
-    Witer_const iter = w1.find(fname);
-    if (iter != w1.end()) {
-      (*val) = iter->second;
-      return true;
-    } else {
-      (*val) = 0;
-      return false;
-    }
-  }
-};
-
-// Outputs a SparseWeightVector, for debugging.
-template <class Key, class Hash>
-std::ostream& operator<<(std::ostream &stream,
-                    const SparseWeightVector<Key, Hash> &vector) {
-  typename SparseWeightVector<Key, Hash>::Wmap w_map = vector.GetMap();
-  stream << "[[ ";
-  for (typename SparseWeightVector<Key, Hash>::Witer_const iter = w_map.begin();
-       iter != w_map.end();
-       ++iter) {
-    stream << "<" << iter->first << ", " << iter->second << "> ";
-  }
-  return stream << " ]]";
-};
-
-}  // namespace learning_stochastic_linear
-#endif  // LEARNING_STOCHASTIC_LINEAR_SPARSE_WEIGHT_VECTOR_H_