Merge pull request #68 from R-Goc/change-RNG-in-kmeans.h

Changing the RNG in kmeans.h to platform agnostic.

src/machine_learning/clustering/kmeans/kmeans.h

@@ -15,111 +15,130 @@ using json = nlohmann::json;
 /**
  * @ brief Class for the kmeans clustering algorithm
  */
-class kmeans{
+class kmeans
+{
 private:
   /**
-  * @ brief distance function
-  * @ param a the first input vector
-  * @ param b the second input vector
-  * @ return int64_t the euclidean distance between vector a and b
-  *
-  */
-  double distance(std::vector<double> &a, std::vector<double> &b){
-    return sqrt(pow((a[0] - b[0]) , 2) + pow((a[1] - b[1]), 2));
+   * @ brief distance function
+   * @ param a the first input vector
+   * @ param b the second input vector
+   * @ return int64_t the euclidean distance between vector a and b
+   *
+   */
+  double distance(std::vector<double> &a, std::vector<double> &b)
+  {
+    return sqrt(pow((a[0] - b[0]), 2) + pow((a[1] - b[1]), 2));
   }
   std::vector<std::vector<double>> data;
   int K;
-  std::vector<std::vector<double> > cluster_centers;
+  std::vector<std::vector<double>> cluster_centers;
   std::map<std::vector<double>, int64_t> assignments;
 
 public:
   /**
-  * @brief Constructor for the kmeans class
-  * @param data: the input data(2D vector)
-  * @param K: the number of clusters
-  * @param MAX_ITER: default 500, maximum iterations till it converges
-  */
-  kmeans(std::vector<std::vector<double>> data, int K, int64_t MAX_ITER=1500) : data(data), K(K) {
-
-
-    for(int i = 0; i<K; i++){
-      int rand_num = arc4random() % data.size() - 1;
+   * @brief Constructor for the kmeans class
+   * @param data: the input data(2D vector)
+   * @param K: the number of clusters
+   * @param MAX_ITER: default 500, maximum iterations till it converges
+   */
+  kmeans(std::vector<std::vector<double>> data, int K, int64_t MAX_ITER = 1500) : data(data), K(K)
+  {
+
+    std::random_device rd;
+    std::mt19937_64 gen(rd());
+    std::uniform_real_distribution<double> distrib(std::numeric_limits<double>::min(), std::numeric_limits<double>::max());
+    for (int i = 0; i < K; i++)
+    {
+      double rand_num = distrib(gen);
       this->cluster_centers.push_back(data[rand_num]);
     }
 
-    for(int ww = 0; ww<MAX_ITER; ww++){
-      for(int64_t i = 0; i<data.size(); i++){
+    for (int ww = 0; ww < MAX_ITER; ww++)
+    {
+      for (int64_t i = 0; i < data.size(); i++)
+      {
         assign_to_closest(data[i]);
       }
 
-      std::vector<std::vector<std::vector<double> > > _clusters(K);
-      for(auto & x: assignments){
+      std::vector<std::vector<std::vector<double>>> _clusters(K);
+      for (auto &x : assignments)
+      {
         _clusters[x.second].push_back(x.first);
       }
-      std::vector<std::vector<double> > new_centroids;
-      for(int i = 0; i<K; i++){
+      std::vector<std::vector<double>> new_centroids;
+      for (int i = 0; i < K; i++)
+      {
         new_centroids.push_back(get_centroid(_clusters[i]));
       }
 
-      if(new_centroids == this->cluster_centers){
+      if (new_centroids == this->cluster_centers)
+      {
         break;
       }
-      else{
+      else
+      {
         this->cluster_centers = new_centroids;
       }
     }
   }
-  
+
   /**
    * @brief Destroy the kmeans object
    */
-  ~kmeans() {
+  ~kmeans()
+  {
     cluster_centers.clear();
     assignments.clear();
   }
-  
+
   /**
-  * @ brief assign_to_closest function
-  * assigns the closest centroid to point x
-  * @ param x: the input vector
-  */
-  void assign_to_closest(std::vector<double> &x){
+   * @ brief assign_to_closest function
+   * assigns the closest centroid to point x
+   * @ param x: the input vector
+   */
+  void assign_to_closest(std::vector<double> &x)
+  {
     std::vector<double> id = this->cluster_centers[0];
     int index = 0;
     int min_dist = distance(x, id);
-    for(int j = 0; j<this->cluster_centers.size(); j++){
+    for (int j = 0; j < this->cluster_centers.size(); j++)
+    {
       int current_dist = distance(this->cluster_centers[j], x);
-      if(current_dist < min_dist){
+      if (current_dist < min_dist)
+      {
         min_dist = current_dist;
         index = j;
         id = this->cluster_centers[index];
       }
     }
     assignments[x] = index;
   }
-  
+
   /**
-  * @ brief get_centroid function
-  * @ param cluster: the input cluster
-  * @ return vector<int>: the centroid of the cluster
-  */
-  std::vector<double> get_centroid(std::vector<std::vector<double> > cluster){
+   * @ brief get_centroid function
+   * @ param cluster: the input cluster
+   * @ return vector<int>: the centroid of the cluster
+   */
+  std::vector<double> get_centroid(std::vector<std::vector<double>> cluster)
+  {
     double sum_x = 0, sum_y = 0, n = cluster.size();
-    for(auto & x : cluster){
+    for (auto &x : cluster)
+    {
       sum_x += x[0];
       sum_y += x[1];
     }
     sum_x /= n;
     sum_y /= n;
     return {sum_x, sum_y};
   }
-  
+
   /**
-  * @ brief run function
-  * @ return pair<vector<int64_t>, map<int64_t, int> >: the centroids and the assignments of the clustering algorithm
-  *
-  */
-  std::pair<std::vector<std::vector<double> >, std::map<std::vector<double>, int64_t> > fit(){
+   * @ brief run function
+   * @ return pair<vector<int64_t>, map<int64_t, int> >: the centroids and the assignments of the clustering algorithm
+   *
+   */
+  std::pair<std::vector<std::vector<double>>, std::map<std::vector<double>, int64_t>> fit()
+  {
     return std::make_pair(cluster_centers, assignments);
   }
 };