classify.py

def classify(cell_feature_vector, sickle_cluster, red_cluster, white_cluster):
  #first, get the minimum of the distances to the known centroids. 
  sickle_dist = dist(cell_feature_vector, sickle_cluster.centroid)
  red_dist = dist(cell_feature_vector, red_cluster.centroid)
  white_dist = dist(cell_feature_vector, white_cluster.centroid)
  
  min_dist = min(sickle_dist, red_dist, white_dist)
  
  #check which centroid minimized the distance, and then check if this minimum distance is less than the furthest outlier
  #that was still correctly classified. 
  
  if(min_dist == sickle_dist):
    if(min_dist <= sickle_cluster.MAX_OUTLIER_DIST):
      #if this vector was closer to the sickle cluster's centroid than the furthest outlier still classified as a sickle cell,
      #then add it to the dataset, recompute the centroid, and return the label. 
        sickle_cluster.add(cell_feature_vector)
        recompute_centroid(sickle_cluster)
        return "sickle cell"
    
  elif(min_dist == red_dist):
    if(min_dist <= red_cluster.MAX_OUTLIER_DIST):
        red_cluster.add(cell_feature_vector)
        recompute_centroid(red_cluster)
        return "red blood cell"
  
  else:
    if(min_dist <= white_cluster.MAX_OUTLIER_DIST):
        white_cluster.add(cell_feature_vector)
        recompute_centroid(white_cluster)
        return "white blood cell"
        
  return "Unable to classify cell"